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Purpose: To evaluate the role of semiquantitative parameters derived from 18 F-FDG PET/CT in characterizing pediatric malignant kidney tumors., Methods: 54 children with pathologically confirmed renal malignancies who underwent pretreatment 18 F-FDG PET/CT were retrospectively reviewed. Association between PET-derived semiquantitative parameters, including maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), metabolic tumor volume (MTV) and total lesion glycolysis (TLG), and tumor histology, disease stage, and loss of heterozygosity were analyzed., Results: Of the 54 patients, 43 had Wilms tumor (WT), 4 had clear cell sarcoma of the kidney (CCSK), 3 had rhabdoid tumor of kidney, 2 had renal cell carcinoma, and 2 had congenital mesoblastic nephroma. 18 F-FDG PET/CT detected all primary renal tumors, with SUVmax ranging from 1.2 to 14.5. PET-derived parameters showed no significant differences among different tumor types, except for CCSK, which exhibited lower FDG uptake compared to WT (SUVmax 3.60 ± 1.02 vs. 6.10 ± 2.25, p = 0.034). Higher MTV, TLG, and lactate dehydrogenase were associated with stage IV disease. ROC analysis showed that TLG had the highest AUC (0.931) for predicting stage IV disease in the whole cohort, while SUVmax had the highest AUC (0.867) for predicting stage IV and V in WT. Furthermore, Children with 1q gain exhibited higher MTV in WT (447 ± 354 vs. 207 ± 189, p = 0.041)., Conclusion: PET-derived semiquantitative parameters highly associated with disease stage both in the overall cohort of renal malignancies and specifically in the WT group. Additionally, volumetric metabolic indices might provide further insights into loss of heterozygosity in children with WT., Competing Interests: Declarations. Ethical approval: All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent: Informed consent was obtained from all individual participants included in the study. Author disclosures: Compliance with ethical standards. Conflict of interest: The authors declare that they have no conflicts of interest., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Objectives: To distinguish histological subtypes of renal tumors using radiomic features and machine learning (ML) based on multiphase computed tomography (CT)., Material and Methods: Patients who underwent surgical treatment for renal tumors at two tertiary centers from 2012 to 2022 were included retrospectively. Preoperative arterial (corticomedullary) and venous (nephrogenic) phase CT scans from these centers, as well as from external imaging facilities, were manually segmented, and standardized radiomic features were extracted. Following preprocessing and addressing the class imbalance, a ML algorithm based on extreme gradient boosting trees (XGB) was employed to predict renal tumor subtypes using 10-fold cross-validation. The evaluation was conducted using the multiclass area under the receiver operating characteristic curve (AUC). Algorithms were trained on data from one center and independently tested on data from the other center., Results: The training cohort comprised n = 297 patients (64.3% clear cell renal cell cancer [RCC], 13.5% papillary renal cell carcinoma (pRCC), 7.4% chromophobe RCC, 9.4% oncocytomas, and 5.4% angiomyolipomas (AML)), and the testing cohort n = 121 patients (56.2%/16.5%/3.3%/21.5%/2.5%). The XGB algorithm demonstrated a diagnostic performance of AUC = 0.81/0.64/0.8 for venous/arterial/combined contrast phase CT in the training cohort, and AUC = 0.75/0.67/0.75 in the independent testing cohort. In pairwise comparisons, the lowest diagnostic accuracy was evident for the identification of oncocytomas (AUC = 0.57-0.69), and the highest for the identification of AMLs (AUC = 0.9-0.94) CONCLUSION: Radiomic feature analyses can distinguish renal tumor subtypes on routinely acquired CTs, with oncocytomas being the hardest subtype to identify., Clinical Relevance Statement: Radiomic feature analyses yield robust results for renal tumor assessment on routine CTs. Although radiologists routinely rely on arterial phase CT for renal tumor assessment and operative planning, radiomic features derived from arterial phase did not improve the accuracy of renal tumor subtype identification in our cohort., (© 2024. The Author(s).)

Purpose: The management of renal cell carcinoma (RCC) relies on clinical and histopathological features for treatment decisions. Recently, radiomics, which involves the extraction and analysis of quantitative imaging features, has shown promise in improving RCC management. This review evaluates the current application and limitations of radiomics for predicting treatment and oncological outcomes in RCC., Methods: A systematic search was conducted in Medline, EMBASE, and Web of Science databases or studies that used radiomics to predict response to treatment and survival outcomes in patients with RCC. The study quality was assessed using the Radiomics Quality Score (RQS) tools., Results: The systematic review identified a total of 27 studies, examining 6,119 patients. The most used imaging modality was contrast-enhanced abdominal CT. The reviewed studies extracted between 19 and 3376 radiomics features, including Histogram, Texture, Filter, or transformation method. Radiomics-based risk stratification models provided valuable insights into treatment response and oncological outcomes. All developed signatures demonstrated at least modest accuracy (AUC range: 0.55-0.99). The studies included in this analysis reported heterogeneous results regarding radiomics methods. The range of Radiomics Quality Score (RQS) was from - 5 to 20, with a mean RQS total of 9.15 ± 7.95., Conclusion: Radiomics has emerged as a promising tool in the management of RCC. It offers the potential for improved risk stratification and response assessment. However, future trials must demonstrate the generalizability of findings to prospective cohorts before progressing towards clinical translation., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Objective: To evaluate a recently proposed CT-based algorithm for diagnosis of clear-cell renal cell carcinoma (ccRCC) among small (≤ 4 cm) solid renal masses diagnosed by renal mass biopsy., Methods: This retrospective study included 51 small renal masses in 51 patients with renal-mass CT and biopsy between 2014 and 2021. Three radiologists independently evaluated corticomedullary phase CT for the following: heterogeneity and attenuation ratio (mass:renal cortex), which were used to inform the CT score (1-5). CT score ≥ 4 was considered positive for ccRCC. Diagnostic accuracy was calculated for each reader and overall using fixed effects logistic regression modelling., Results: There were 51% (26/51) ccRCC and 49% (25/51) other masses. For diagnosis of ccRCC, area under curve (AUC), sensitivity, specificity, and positive predictive value (PPV) were 0.69 (95% confidence interval 0.61-0.76), 78% (68-86%), 59% (46-71%), and 67% (54-79%), respectively. CT score ≤ 2 had a negative predictive value 97% (92-99%) to exclude diagnosis of ccRCC. For diagnosis of papillary renal cell carcinoma (pRCC), CT score ≤ 2, AUC, sensitivity, specificity, and PPV were 0.89 (0.81-0.98), 81% (58-94%), 98% (93-99%), and 85% (62-97%), respectively. Pooled inter-observer agreement for CT scoring was moderate (Fleiss weighted kappa = 0.52)., Conclusion: The CT scoring system for prediction of ccRCC was sensitive with a high negative predictive value and moderate agreement. The CT score is highly specific for diagnosis of pRCC., Clinical Relevance Statement: The CT score algorithm may help guide renal mass biopsy decisions in clinical practice, with high sensitivity to identify clear-cell tumors for biopsy to establish diagnosis and grade and high specificity to avoid biopsy in papillary tumors., Key Points: • A CT score ≥ 4 had high sensitivity and negative predictive value for diagnosis of clear-cell renal cell carcinoma (RCC) among solid ≤ 4-cm renal masses. • A CT score ≤ 2 was highly specific for diagnosis of papillary RCC among solid ≤ 4-cm renal masses. • Inter-observer agreement for CT score was moderate., (© 2023. The Author(s), under exclusive licence to European Society of Radiology.)

Purpose: This study aimed to develop and validate an ultrasound (US)-based nomogram for the preoperative differentiation of renal urothelial carcinoma (rUC) from central renal cell carcinoma (c-RCC)., Methods: Clinical data and US images of 655 patients with 655 histologically confirmed malignant renal tumors (521 c-RCCs and 134 rUCs) were collected and divided into training (n = 455) and validation (n = 200) cohorts according to examination dates. Conventional US and contrast-enhanced US (CEUS) tumor features were analyzed to determine those that could discriminate rUC from c-RCC. Least absolute shrinkage and selection operator regression was applied to screen clinical and US features for the differentiation of rUC from c-RCC. Using multivariate logistic regression analysis, a diagnostic model of rUC was constructed and visualized as a nomogram. The diagnostic model's performance was assessed in the training and validation cohorts by calculating the area under the receiver operating characteristic curve (AUC) and calibration plot. Decision curve analysis (DCA) was used to assess the clinical usefulness of the US-based nomogram., Results: Seven features of both clinical features and ultrasound imaging were selected to build the diagnostic model. The nomogram achieved favorable discrimination in the training (AUC = 0.996, 95% CI: 0.993-0.999) and validation (AUC = 0.995, 95% CI: 0.974, 1.000) cohorts, and good calibration (Brier scores: 0.019 and 0.016, respectively). DCA demonstrated the clinical usefulness of the US-based nomogram., Conclusion: A noninvasive clinical and US-based nomogram combining conventional US and CEUS features possesses good predictive value for differentiating rUC from c-RCC., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Purpose: To assess the effectiveness of a deep learning model using contrastenhanced ultrasound (CEUS) images in distinguishing between low-grade (grade I and II) and high-grade (grade III and IV) clear cell renal cell carcinoma (ccRCC)., Methods: A retrospective study was conducted using CEUS images of 177 Fuhrmangraded ccRCCs (93 low-grade and 84 high-grade) from May 2017 to December 2020. A total of 6412 CEUS images were captured from the videos and normalized for subsequent analysis. A deep learning model using the RepVGG architecture was proposed to differentiate between low-grade and high-grade ccRCC. The model's performance was evaluated based on sensitivity, specificity, positive predictive value, negative predictive value and area under the receiver operating characteristic curve (AUC). Class activation mapping (CAM) was used to visualize the specific areas that contribute to the model's predictions., Results: For discriminating high-grade ccRCC from low-grade, the deep learning model achieved a sensitivity of 74.8%, specificity of 79.1%, accuracy of 77.0%, and an AUC of 0.852 in the test set., Conclusion: The deep learning model based on CEUS images can accurately differentiate between low-grade and high-grade ccRCC in a non-invasive manner., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Objectives: To develop and validate a CT-based deep learning radiomics nomogram (DLRN) for outcome prediction in clear cell renal cell carcinoma (ccRCC), and its performance was compared with the Stage, Size, Grade, and Necrosis (SSIGN) score, the University of California, Los Angeles, Integrated Staging System (UISS), the Memorial Sloan-Kettering Cancer Center (MSKCC), and the International Metastatic Renal Cell Database Consortium (IMDC)., Methods: A multicenter of 799 localized (training/ test cohort, 558/241) and 45 metastatic ccRCC patients were studied. A DLRN was developed for predicting recurrence-free survival (RFS) in localized ccRCC patients, and another DLRN was developed for predicting overall survival (OS) in metastatic ccRCC patients. The performance of the two DLRNs was compared with that of the SSIGN, UISS, MSKCC, and IMDC. Model performance was assessed with Kaplan-Meier curves, time-dependent area under the curve (time-AUC), Harrell's concordance index (C-index), and decision curve analysis (DCA)., Results: In the test cohort, the DLRN achieved higher time-AUCs (0.921, 0.911, and 0.900 for 1, 3, and 5 years, respectively), C-index (0.883), and net benefit than SSIGN and UISS in predicting RFS for localized ccRCC patients. The DLRN provided higher time-AUCs (0.594, 0.649, and 0.754 for 1, 3, and 5 years, respectively) than MSKCC and IMDC in predicting OS for metastatic ccRCC patients., Conclusions: The DLRN can accurately predict outcomes and outperformed the existing prognostic models in ccRCC patients., Clinical Relevance Statement: This deep learning radiomics nomogram may facilitate individualized treatment, surveillance, and adjuvant trial design for patients with clear cell renal cell carcinoma., Key Points: • SSIGN, UISS, MSKCC, and IMDC may be insufficient for outcome prediction in ccRCC patients. • Radiomics and deep learning allow for the characterization of tumor heterogeneity. • The CT-based deep learning radiomics nomogram outperforms the existing prognostic models in ccRCC outcome prediction., (© 2023. The Author(s), under exclusive licence to European Society of Radiology.)

Objectives: Contrast enhancement by MRI done early after cryoablation for renal malignancies may suggest residual tumor (RT). However, we have observed MRI enhancement within 48 h of cryoablation in patients who had no contrast enhancement 6 weeks later. Our purpose was to identify features of 48-h contrast enhancement in patients without RT., Methods: This single-center retrospective study included consecutive patients who underwent percutaneous cryoablation of renal malignancies in 2013-2020, exhibited cryoablation-zone MRI contrast enhancement 48 h later, and had available 6-week MRI scans. Persistent or growing CE at 6 weeks vs. 48 h was classified as RT. A washout index was calculated for each 48-h MRI, and its performance for predicting RT was assessed by receiver operating characteristic curve analysis., Results: We included 60 patients with 72 cryoablation procedures and 83 cryoablation zones exhibiting 48-h contrast enhancement; mean age was 66 ± 17 years. Clear-cell renal cell carcinoma accounted for 95% of tumors. Of the 83 48-h enhancement zones, RT was observed in eight while 75 were benign. The 48-h enhancement was consistently visible at the arterial phase. Washout was significantly associated with RT (p < 0.001) and gradually increasing contrast enhancement with benignity (p < 0.009). A washout index below  - 1.1 predicted RT with 88% sensitivity and 84% specificity., Conclusion: MRI contrast enhancement 48 h after cryoablation of renal malignancies was usually benign. Washout was associated with residual tumor, with a washout index value below  - 1.1 exhibiting good performance in predicting residual tumor. These findings may help to guide decisions about repeat cryoablation., Clinical Relevance Statement: Magnetic resonance imaging contrast enhancement 48 h after cryoablation of renal malignancies rarely indicates residual tumor, which is characterized by washout with a washout index lower than  - 1.1., Key Points: • Contrast enhancement at the arterial phase of magnetic resonance imaging done 48 h after cryoablation of a renal malignancy is usually benign. • Residual tumor manifesting as contrast enhancement at the arterial phase is characterized by subsequent marked washout. • A washout index below  - 1.1 has 88% sensitivity and 84% specificity for residual tumor., (© 2023. The Author(s), under exclusive licence to European Society of Radiology.)

Objective: To identify adhesive renal venous tumor thrombus (RVTT) of renal cell carcinoma (RCC) by contrast-enhancement CT (CECT)., Materials and Methods: Our retrospective study included 53 patients who underwent preoperative CECT and pathologically confirmed RCC combined with RVTT. They were divided into two groups based on the intra-operative findings of RVTT adhesion to the venous wall, with 26 cases in the adhesive RVTT group (ARVTT) and 27 cases in the non-adhesive group (NRVTT). The location, maximum diameter (MD) and CT values of tumors, the maximum length (ML) and width (MW) of RVTT, and length of inferior vena cava tumor thrombus were compared between the two groups. The presence of renal venous wall involvement, renal venous wall inflammation, and enlarged retroperitoneal lymph node was compared between the two groups. A receiver operating characteristic curve was used to analyze the diagnostic performance., Results: The MD of RCC and the ML and MW of the RVTT were all larger in the ARVTT group than in the NRVTT group (p = 0.042, p < 0.001, and p = 0.002). The proportion of renal vein wall involvement and renal vein wall inflammation were higher in the ARVTT group than in NRVTT groups (both p < 0.001). The multivariable model including ML and vascular wall inflammation to predict ARVTT could achieve the best diagnostic performance with the area under the curve, sensitivity, specificity, and accuracy of 0.91, 88.5%, 96.3%, and 92.5%, respectively., Conclusion: The multivariable model acquired by CECT images could be used to predict RVTT adhesion., Clinical Relevance Statement: For RCC patients with tumor thrombus, contrast-enhanced CT could noninvasively predict the adhesion of tumor thrombus, thus predicting the difficulty of surgery and contributing to the selection of an appropriate treatment plan., Key Points: • The length and width of the tumor thrombus could be used to predict its adhesion to the vessel wall. • Adhesion of the tumor thrombus can be reflected by inflammation of the renal vein wall. • The multivariable model from CECT can well predict whether the tumor thrombus adhered to the vein wall., (© 2023. The Author(s), under exclusive licence to European Society of Radiology.)

Objectives: The purpose of the current study is to evaluate the performance of RENAL and mRENAL scores, in the prediction of oncological outcomes in patients treated with microwave ablation (MWA) for (T1) renal cell carcinomas (RCC)., Methods: Institutional database retrospective research identified 76 patients with a biopsy-proven solitary T1a (84%) or T1b (16%) RCC; all patients underwent CT-guided MWA ablation. Tumor complexity was reviewed by calculating RENAL and mRENAL scores., Results: The majority of the lesions were exophytic (82.9%), with > 7 mm nearness to the collecting system (53.9%), located posteriorly (73.6%), and lower to polar lines (61.8%). Mean RENAL and mRENAL scores were 5.7 (SD = 1.9) and 6.1 (SD = 2.1) respectively. Progression rates were significantly higher with greater tumor size (> 4 cm), with < 4 mm nearness to the collecting system, for tumors crossing a polar line and with the anterior location. None of the above was associated with complications. RENAL and mRENAL scores were significantly higher in patients with incomplete ablation. The ROC analysis showed the significant prognostic ability of both RENAL and mRENAL scores for progression. In both scores, the optimal cut-off point was 6.5. Univariate Cox regression analysis for progression showed a hazard ratio of 7.73 for the RENAL score and 7.48 for the mRENAL score., Conclusion: The results of the present study show that the risk of progression was higher in patients with RENAL and mRENAL score of > 6.5, in T1b tumors, close to the collective system (< 4 mm), crossing polar lines and anterior location., Clinical Relevance Statement: CT-guided percutaneous MWA is a safe and effective technique for the treatment of T1a renal cell carcinomas. Different morphometric parameters of RCC tumors including RENAL and mRENAL score > 6.5, size, proximity to the collecting system, and crossing of polar lines impact the efficacy of MWA and progression survival rates., Key Points: • The risk of progression is higher in patients with RENAL and mRENAL score > 6.5, in T1b tumors, close to the collective system (< 4 mm), crossing polar lines and anterior location. • The significant prognostic ability of the mRENAL score for progression was higher than the respective of the RENAL score. • Complications were not associated with any of the above factors., (© 2023. The Author(s), under exclusive licence to European Society of Radiology.)

Objectives: To determine whether 3D-CT multi-level anatomical features can provide a more accurate prediction of surgical decision-making for partial or radical nephrectomy in renal cell carcinoma., Methods: This is a retrospective study based on multi-center cohorts. A total of 473 participants with pathologically proved renal cell carcinoma were split into the internal training and the external testing set. The training set contains 412 cases from five open-source cohorts and two local hospitals. The external testing set includes 61 participants from another local hospital. The proposed automatic analytic framework contains the following modules: a 3D kidney and tumor segmentation model constructed by 3D-UNet, a multi-level feature extractor based on the region of interest, and a partial or radical nephrectomy prediction classifier by XGBoost. The fivefold cross-validation strategy was used to get a robust model. A quantitative model interpretation method called the Shapley Additive Explanations was conducted to explore the contribution of each feature., Results: In the prediction of partial versus radical nephrectomy, the combination of multi-level features achieved better performance than any single-level feature. For the internal validation, the AUROC was 0.93 ± 0.1, 0.94 ± 0.1, 0.93 ± 0.1, 0.93 ± 0.1, and 0.93 ± 0.1, respectively, as determined by the fivefold cross-validation. The AUROC from the optimal model was 0.82 ± 0.1 in the external testing set. The tumor shape Maximum 3D Diameter plays the most vital role in the model decision., Conclusions: The automated surgical decision framework for partial or radical nephrectomy based on 3D-CT multi-level anatomical features exhibits robust performance in renal cell carcinoma. The framework points the way towards guiding surgery through medical images and machine learning., Clinical Relevance Statement: We proposed an automated analytic framework that can assist surgeons in partial or radical nephrectomy decision-making. The framework points the way towards guiding surgery through medical images and machine learning., Key Points: • The 3D-CT multi-level anatomical features provide a more accurate prediction of surgical decision-making for partial or radical nephrectomy in renal cell carcinoma. • The data from multicenter study and a strict fivefold cross-validation strategy, both internal validation set and external testing set, can be easily transferred to different tasks of new datasets. • The quantitative decomposition of the prediction model was conducted to explore the contribution of each extracted feature., (© 2023. The Author(s).)

Objectives: The standard therapy for small renal masses (SRMs) remains partial nephrectomy (PN), which is associated with relatively high morbidity and complication rate. Therefore, percutaneous radiofrequency ablation (PRFA) emerges as an alternative therapy. This study aimed to compare the efficacy, safety, and oncological outcomes of PRFA versus PN., Methods: A multicenter non-inferiority study with retrospective analysis of 291 patients with SRMs (N0M0), who underwent PN or PRFA (2:1), recruited prospectively from two hospitals in the Andalusian Public Health System, Spain, between 2014 and 2021. Comparisons of treatment features were evaluated using the t test, Wilcoxon-Mann-Whitney U test, chi-square test, Fisher test, and Cochran-Armitage trend test. Kaplan-Meier curves depicted overall survival (OS), local recurrence-free survival (LRFS), and metastasis-free survival (MFS) rates in the overall study population., Results: A total of 291 consecutive patients were identified; 111 and 180 patients underwent PRFA and PN, respectively. Median follow-up time was 38 and 48 months, and mean hospitalization days were 1.04 and 3.57 days, respectively. The variables underpinned with high surgical risk were significantly increased in PRFA compared to those in PN (mean age was 64.56 and 57.47 years, the solitary kidney presence was 12.6% and 5.6%, ASA score ≥ 3 was 36% and 14.5%, respectively). The rest of oncological outcomes were comparable amongst PRFA and PN. Patients undergoing PRFA did not improve OS, LRFS, and MFS compared to those undergoing PN. Limitations comprise retrospective design and limited statistical power., Conclusion: PRFA for SMRs in high-risk patients is non-inferior in terms of oncological outcomes and safety compared to PN., Clinical Relevance Statement: Our study has a direct clinical application as it proves that radiofrequency ablation is an effective and uncomplicated therapeutic option for patients with small renal masses., Key Points: •There are non-inferiority results in overall survival, local recurrence-free survival, and metastasis-free survival between PRFA and PN. •Our two-center study showed that PRFA is non-inferior to PN in oncological outcomes. •Contrast-enhanced power ultrasound-guided PRFA provides an effective therapy for T1 renal tumors., (© 2023. The Author(s), under exclusive licence to European Society of Radiology.)

Purpose: To evaluate association between computer tomography (CT)-based features of renal cell carcinoma (RCC) and survival outcomes., Methods: Data of 958 patients with clinical T1b-T2 RCC who underwent partial/radical nephrectomy from June 2003 to March 2022 were retrospectively evaluated. CT images of patients were reviewed by two radiologists for texture analysis of tumor heterogeneity and shape analysis of tumor contour. Patients were divided into three groups according to patterns of CT-based features: (1) favorable feature group (n = 117); (2) intermediate feature group (n = 606); and (3) unfavorable feature group (n = 235). Kaplan-Meier survival analysis and multivariate Cox regression analysis were performed to evaluate overall survival (OS), cancer-specific survival (CSS), and recurrence-free survival (RFS)., Results: RCCs with unfavorable CT-based feature showed larger size on CT, higher nuclear grade, higher rate of histologic necrosis, and higher rate of capsular invasion than those in the other two groups (all p < 0.001). Unfavorable feature was associated with poorer OS (p = 0.001), CSS (p < 0.001), and RFS (p < 0.001) on Kaplan-Meier analysis. In multivariate analysis, intermediate and unfavorable features were independent predictors for recurrence (hazard ratio [HR] 2.51, 95% confidence interval [CI] 1.09-5.79, p = 0.031 and HR 3.71, 95% CI 1.58-8.73, p = 0.003, respectively), but not for overall death or RCC-specific death., Conclusions: A combination of irregular tumor contour feature with heterogeneous tumor texture feature on CT is associated with poor RFS in clinical T1b-T2 RCC preoperatively., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Objectives: Previous trial results suggest that only a small number of patients with non-metastatic renal cell carcinoma (RCC) benefit from adjuvant therapy. We assessed whether the addition of CT-based radiomics to established clinico-pathological biomarkers improves recurrence risk prediction for adjuvant treatment decisions., Methods: This retrospective study included 453 patients with non-metastatic RCC undergoing nephrectomy. Cox models were trained to predict disease-free survival (DFS) using post-operative biomarkers (age, stage, tumor size and grade) with and without radiomics selected on pre-operative CT. Models were assessed using C-statistic, calibration, and decision curve analyses (repeated tenfold cross-validation)., Results: At multivariable analysis, one of four selected radiomic features (wavelet-HHL&#95;glcm&#95;ClusterShade) was prognostic for DFS with an adjusted hazard ratio (HR) of 0.44 (p = 0.02), along with American Joint Committee on Cancer (AJCC) stage group (III versus I, HR 2.90; p = 0.002), grade 4 (versus grade 1, HR 8.90; p = 0.001), age (per 10 years HR 1.29; p = 0.03), and tumor size (per cm HR 1.13; p = 0.003). The discriminatory ability of the combined clinical-radiomic model (C = 0.80) was superior to that of the clinical model (C = 0.78; p < 0.001). Decision curve analysis revealed a net benefit of the combined model when used for adjuvant treatment decisions. At an exemplary threshold probability of ≥ 25% for disease recurrence within 5 years, using the combined versus the clinical model was equivalent to treating 9 additional patients (per 1000 assessed) who would recur without treatment (i.e., true-positive predictions) with no increase in false-positive predictions., Conclusion: Adding CT-based radiomic features to established prognostic biomarkers improved post-operative recurrence risk assessment in our internal validation study and may help guide decisions regarding adjuvant therapy., Key Points: In patients with non-metastatic renal cell carcinoma undergoing nephrectomy, CT-based radiomics combined with established clinical and pathological biomarkers improved recurrence risk assessment. Compared to a clinical base model, the combined risk model enabled superior clinical utility if used to guide decisions on adjuvant treatment., (© 2023. The Author(s), under exclusive licence to European Society of Radiology.)

Objectives: To determine whether a CT-based machine learning (ML) can differentiate benign renal tumors from renal cell carcinomas (RCCs) and improve radiologists' diagnostic performance, and evaluate the impact of variable CT imaging phases, slices, tumor sizes, and region of interest (ROI) segmentation strategies., Methods: Patients with pathologically proven RCCs and benign renal tumors from our institution between 2008 and 2020 were included as the training dataset for ML model development and internal validation (including 418 RCCs and 78 benign tumors), and patients from two independent institutions and a public database (TCIA) were included as the external dataset for individual testing (including 262 RCCs and 47 benign tumors). Features were extracted from three-phase CT images. CatBoost was used for feature selection and ML model establishment. The area under the receiver operating characteristic curve (AUC) was used to assess the performance of the ML model., Results: The ML model based on 3D images performed better than that based on 2D images, with the highest AUC of 0.81 and accuracy (ACC) of 0.86. All three radiologists achieved better performance by referring to the classifier's decision, with accuracies increasing from 0.82 to 0.87, 0.82 to 0.88, and 0.76 to 0.87. The ML model achieved higher negative predictive values (NPV, 0.82-0.99), and the radiologists achieved higher positive predictive values (PPV, 0.91-0.95)., Conclusions: A ML classifier based on whole-tumor three-phase CT images can be a useful and promising tool for differentiating RCCs from benign renal tumors. The ML model also perfectly complements radiologist interpretations., Key Points: • A machine learning classifier based on CT images could be a reliable way to differentiate RCCs from benign renal tumors. • The machine learning model perfectly complemented the radiologists' interpretations. • Subtle variances in ROI delineation had little effect on the performance of the ML classifier., (© 2023. The Author(s), under exclusive licence to European Society of Radiology.)

Objectives: To evaluate the value of ZOOMit diffusion kurtosis imaging (DKI) and chemical exchange saturation transfer (CEST) imaging in predicting WHO/ISUP grade and pathological T stage in clear cell renal cell carcinoma (ccRCC)., Methods: Forty-six patients with ccRCC were included in this retrospective study. All participants underwent MRI including ZOOMit DKI and CEST. The non-Gaussian mean kurtosis (MK), mean diffusivity (MD), magnetization transfer ratio asymmetry (MTRasym (3.5 ppm)), and S sat (3.5 ppm)/S 0 were analyzed based on different WHO/ISUP grades and pT stages. Binary logistic regression was used to identify the best combination of the parameters. Pearson's correlation coefficients were calculated between CEST and diffusion-related parameters., Results: The ADC, MD, and S sat (3.5 ppm)/S 0 values were significantly lower for higher WHO/ISUP grade tumors, whereas the MK and MTRasym (3.5 ppm) were higher in higher WHO/ISUP grade and higher pT stage tumors. MTRasym (3.5 ppm) combined with MD (AUC, 0.930; 95% CI, 0.858-1.000) showed the best diagnostic efficacy in evaluating the WHO/ISUP grade. MTRasym (3.5 ppm) and MK were mildly positively correlated (r = 0.324, p = 0.028). S sat (3.5 ppm)/S 0 was moderately positively correlated with ADC (r = 0.580, p < 0.001), mildly positively correlated with MD (r = 0.412, p = 0.005), and moderately negatively correlated with MK (r = -0.575, p < .001)., Conclusion: The microstructural and biochemical assessment of ZOOMit DKI and CEST allowed for the characterization of different WHO/ISUP grades and pT stages in ccRCC. MTRasym (3.5 ppm) combined with MD showed the best diagnostic performance for WHO/ISUP grading., Key Points: • Both diffusion kurtosis imaging (DKI) and chemical exchange saturation transfer (CEST) can be used to predict the WHO/ISUP grade and pathological T stage. • MTRasym (3.5 ppm) combined with MD showed the highest AUC (0.930; 95% CI, 0.858-1.000) in WHO/ISUP grading. • MTRasym at 3.5 ppm showed a positive correlation with mean kurtosis., (© 2022. The Author(s), under exclusive licence to European Society of Radiology.)

Purpose: This study aimed to evaluate the diagnostic value of serum and CT factors to establish a convenient diagnostic method for differentiating small (≤ 4 cm) fat-poor angiomyolipoma (AML) from renal cell carcinoma (RCC)., Materials and Methods: This study analyzed the preoperative serum laboratory data and CT data of 32 fat-poor AML patients and 133 RCC patients. The CT attenuation value of tumor (AVT), relative enhancement ratio (RER), and heterogeneous degree of tumor were detected using region of interest on precontrast phase (PCP) and the corticomedullary phase. Multivariate regression was performed to filter the main factors. The main factors were selected to establish the prediction models. The area under the curve (AUC) was measured to evaluate the diagnostic efficacy., Results: Fat-poor AML was more common found in younger (47.91 ± 2.09 years vs 53.63 ± 1.17 years, P = 0.02) and female (70.68 vs 28.13%, P < 0.001) patients. Alkaline phosphatase (ALP) was higher in RCC patients (81.80 ± 1.75 vs 63.25 ± 2.95 U/L, P < 0.01). For CT factors, fat-poor AML was higher in PCP&#95;AVT (40.30 ± 1.49 vs 32.98 ± 0.69Hu, P < 0.01) but lower in RER (67.17 ± 3.17 vs 84.64 ± 2.73, P < 0.01). Gender, ALP, PCP&#95;AVT and RER was found valuable for the differentiation. When compared with laboratory-based or CT-based diagnostic models, the combination model integrating gender, ALP, PCP&#95;AVT and RER shows the best diagnostic performance (AUC = 0.922)., Conclusion: ALP was found higher in RCC patients. Female patients with ALP < 70.50U/L, PCP&#95;AVT > 35.97Hu and RER < 82.66 are more likely to be diagnose as fat-poor AML., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Objectives: To investigate the association of computed tomography-assessed body composition with survival outcomes of metastatic renal cell carcinoma (mRCC) received immunotherapy., Methods: In this multicenter, retrospective study, we reviewed 251 mRCC patients who received anti-PD1 from five centers. We analyzed the relationship between BMI, skeletal muscle area (SM), subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and subcutaneous adipose percentage (SAT%) with progression-free survival (PFS) and overall survival (OS). The spatial localization T cells was investigated by multiplex immunofluorescence., Results: Among 224 evaluable patients, 23 (10.3%) patients were underweight, 118 (52.7%) had normal weight, 65 (29%) were overweight, and 18 patients (8%) were obese. The median age was 55 years and most patients were male (71%). No significant improvement in PFS (HR, 0.61; 95% CI, 0.27-1.42) or OS (HR, 1.09; 95% CI, 0.38-3.13) was observed for the obese patients. Besides, SM, VAT, and SAT were not associated with survival outcomes (all p > 0.05). Interestingly, SAT% independently predicted PFS (as continuous variable, HR: 0.02; 95% CI, 0.01-0.11) and OS (HR:0.05; 95% CI, 0.01-0.39), which remained significant in multivariate modeling (as continuous variable, adjusted HR for PFS, 0.01; 95% CI, 0.00-0.04; adjusted HR for OS, 0.08; 95% CI, 0.01-0.72). These associations were consistent in subgroup analysis of different gender, BMI, PD-L1 positive, and sarcopenia group. Tumor of high SAT% patients had a higher intratumoral PD1 + CD8 + T cell density and ratio., Conclusion: High SAT% predicts better outcomes in mRCC patients treated with anti-PD1 and T cell location may account for the better response., Key Points: • CT-based subcutaneous adipose percentage independently predicted progression-free survival and overall survival. • Patients with a higher subcutaneous adipose percentage had a higher intratumoral PD1 + CD8 + T cell density and ratio., (© 2022. The Author(s), under exclusive licence to European Society of Radiology.)

Objective: To compare the diagnostic efficiency of contrast-enhanced ultrasound (CEUS) with that of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for the differential diagnosis of clear and non-clear cell renal cell carcinoma, as confirmed by subsequent pathology., Methods: A total of 181 patients with 184 renal lesions diagnosed by both CEUS and DCE-MRI were enrolled in the study, including 136 clear cell renal cell carcinoma (ccRCC) and 48 non-clear cell renal cell carcinoma (non-ccRCC) tumors. All lesions were confirmed by histopathologic diagnosis after surgical resection. Interobserver agreement was estimated using a weighted kappa statistic. Diagnostic efficiency in evaluating ccRCC and non-ccRCC was compared between CEUS and DCE-MRI., Results: The weighted kappa value for interobserver agreement was 0.746 to 0.884 for CEUS diagnosis and 0.764 to 0.895 for DCE-MRI diagnosis. Good diagnostic performance in differential diagnosis of ccRCC and non-ccRCC was displayed by both CEUS and DCE-MRI: sensitivity was 89.7% and 91.9%, respectively; specificity was 77.1% and 68.8%, respectively; and area under the receiver operating curve was 0.834 and 0.803, respectively. No statistically significant differences were present between the two methods (p = 0.54)., Conclusions: Both CEUS and DCE-MRI imaging are effective for the differential diagnosis of ccRCC and non-ccRCC. Thus, CEUS could be an alternative to DCE-MRI as a first test for patients at risk of renal cancer, particularly where DCE-MRI cannot be carried out., Key Points: • CEUS and DCE-MRI features can help differentiate ccRCC and non-ccRCC. • The differential diagnosis of ccRCC and non-ccRCC by CEUS is comparable to that of DCE-MRI. • Interobserver agreement is generally high using CEUS and DCE-MRI., (© 2023. The Author(s), under exclusive licence to European Society of Radiology.)

Objectives: Percutaneous radiofrequency ablation (RFA) is stated as a treatment option for renal cell carcinoma (RCC) smaller than 4 cm (T1a). Microwave ablation (MWA) is a newer technique and is still considered experimental in some guidelines. The objective of this study was to compare the safety and efficacy of RFA and MWA for the treatment of RCC., Methods: Patients with T1a RCC treated by RFA or MWA in two referral centers were retrospectively analyzed. Patient records were evaluated to generate mRENAL nephrometry scores. Local tumor progression (LTP) was considered when new (recurrence) or residual tumor enhancement within/adjacent to the ablation zone was objectified. Differences in LTP-free interval (residual + recurrence) between ablation techniques were assessed with Cox proportional hazards models and propensity score (PS) methods., Results: In 164 patients, 87 RFAs and 101 MWAs were performed for 188 RCCs. The primary efficacy rate was 92% (80/87) for RFA and 91% (92/101) for MWA. Sixteen patients had residual disease (RFA (n = 7), MWA (n = 9)) and 9 patients developed recurrence (RFA (n = 7), MWA (n = 2)). LTP-free interval was significantly worse for higher mRENAL nephrometry scores. No difference in LTP-free interval was found between RFA and MWA in a model with inverse probability weighting using PS (HR = 0.99, 95% CI 0.35-2.81, p = 0.98) and in a PS-matched dataset with 110 observations (HR = 0.82, 95% CI 0.16-4.31, p = 0.82). Twenty-eight (14.9%) complications (Clavien-Dindo grade I-IVa) occurred (RFA n = 14, MWA n = 14)., Conclusion: Primary efficacy for ablation of RCC is high for both RFA and MWA. No differences in efficacy and safety were observed between RFA and MWA., Key Points: • Both RFA and MWA are safe and effective ablation techniques in the treatment of T1a renal cell carcinomas. • High modified RENAL nephrometry scores are associated with shorter local tumor progression-free interval. • MWA can be used as heat-based ablation technique comparable to RFA for the treatment of T1a renal cell carcinomas., (© 2022. The Author(s).)

Objective: To evaluate the efficacy of parameters derived from [ 68 Ga]Ga-PSMA-11 PET/CT images in predicting pathological HIF-2α expression in primary tumors among patients with renal cell carcinoma (RCC)., Methods: Fifty-three RCC patients with preoperative [ 68 Ga]Ga-PSMA-11 PET/CT scans and complete surgical specimens were retrospectively enrolled in this study. Radiographic parameters were obtained from PET/CT images, and immunohistochemistry was used to measure the expression of HIF-2α and PSMA. Continuous variables and categorical variables were analyzed by the Mann-Whitney U test and chi-square test, respectively. ROC analysis was used to test the efficacy of several preoperative parameters in identifying pathological HIF-2α expression. Univariable logistic regression analyses were performed for significant parameters to predict pathological HIF-2α expression in RCC., Results: Of the 53 tumors, 29 (54.7%) had high expression of HIF-2α. The SUV max was significantly different in the HIF-2α expression subgroups (p < 0.001). SUV max emerged as the most significant parameter to differentiate HIF-2α expression subgroups (high vs. low), with the AUC of 0.93 (95% CI 0.85-1.00, p < 0.001), sensitivity of 90%, and specificity of 88%. Furthermore, SUV max was confirmed as the most significant predictor of HIF-2α expression level by univariable logistic regression model analysis (odds ratio 1.39, 95% CI 1.17-1.65, p < 0.001). Consistent with the radiographic results of [ 68 Ga]Ga-PSMA-11 PET/CT, the staining intensity of pathological PSMA was significantly higher in HIF-2α-high-expressing tumors (p = 0.003)., Conclusions: [ 68 Ga]Ga-PSMA-11 PET/CT was superior in identifying pathological HIF-2α expression in primary tumors of RCC patients, demonstrating its potential application in predicting responses to HIF-2α antagonists., Key Points: • [ 68 Ga]Ga-PSMA-11 PET/CT could potentially predict the HIF-2α expression of primary tumors among patients with RCC. • SUV max of [ 68 Ga]Ga-PSMA-11 PET/CT was the most significant predictor of HIF-2α expression level. • This probability could help predict the therapeutic response of patients with RCC to HIF-2α antagonists., (© 2022. The Author(s), under exclusive licence to European Society of Radiology.)

Objectives: To validate a deep learning (DL) algorithm for measurement of skeletal muscular index (SMI) and prediction of overall survival in oncology populations., Methods: A retrospective single-center observational study included patients with metastatic renal cell carcinoma between 2007 and 2019. A set of 37 patients was used for technical validation of the algorithm, comparing manual vs DL-based evaluations. Segmentations were compared using mean Dice similarity coefficient (DSC), SMI using concordance correlation coefficient (CCC) and Bland-Altman plots. Overall survivals (OS) were compared using log-rank (Kaplan-Meier) and Mann-Whitney tests. Generalizability of the prognostic value was tested in an independent validation population (N = 87)., Results: Differences between two manual segmentations (DSC = 0.91, CCC = 0.98 for areas) or manual vs. automated segmentation (DSC = 0.90, CCC = 0.98 for areas, CCC = 0.97 for SMI) had the same order of magnitude. Bland-Altman plots showed a mean difference of -3.33 cm 2 [95%CI: -15.98, 9.1] between two manual segmentations, and -3.28 cm 2 [95% CI: -14.77, 8.21] for manual vs. automated segmentations. With each method, 20/37 (56%) patients were classified as sarcopenic. Sarcopenic vs. non-sarcopenic groups had statistically different survival curves with median OS of 6.0 vs. 12.5 (p = 0.008) and 6.0 vs. 13.9 (p = 0.014) months respectively for manual and DL methods. In the independent validation population, sarcopenic patients according to DL had a lower OS (10.7 vs. 17.3 months, p = 0.033)., Conclusion: A DL algorithm allowed accurate estimation of SMI compared to manual reference standard. The DL-calculated SMI demonstrated a prognostic value in terms of OS., Key Points: • A deep learning algorithm allows accurate estimation of skeletal muscle index compared to a manual reference standard with a concordance correlation coefficient of 0.97. • Sarcopenic patients according to SMI thresholds after segmentation by the deep learning algorithm had statistically significantly lower overall survival compared to non-sarcopenic patients., (© 2022. The Author(s), under exclusive licence to European Society of Radiology.)

Objective: To investigate the influence of different volume of interest (VOI) delineation strategies on machine learning-based predictive models for discrimination between low and high nuclear grade clear cell renal cell carcinoma (ccRCC) on dynamic contrast-enhanced CT., Methods: This study retrospectively collected 177 patients with pathologically proven ccRCC (124 low-grade; 53 high-grade). Tumor VOI was manually segmented, followed by artificially introducing uncertainties as: (i) contour-focused VOI, (ii) margin erosion of 2 or 4 mm, and (iii) margin dilation (2, 4, or 6 mm) inclusive of perirenal fat, peritumoral renal parenchyma, or both. Radiomics features were extracted from four-phase CT images (unenhanced phase (UP), corticomedullary phase (CMP), nephrographic phase (NP), excretory phase (EP)). Different combinations of four-phasic features for each VOI delineation strategy were used to build 176 classification models. The best VOI delineation strategy and superior CT phase were identified and the top-ranked features were analyzed., Results: Features extracted from UP and EP outperformed features from other single/combined phase(s). Shape features and first-order statistics features exhibited superior discrimination. The best performance (ACC 81%, SEN 67%, SPE 87%, AUC 0.87) was achieved with radiomics features extracted from UP and EP based on contour-focused VOI., Conclusion: Shape and first-order features extracted from UP + EP images are better feature representations. Contour-focused VOI erosion by 2 mm or dilation by 4 mm within peritumor renal parenchyma exerts limited impact on discriminative performance. It provides a reference for segmentation tolerance in radiomics-based modeling for ccRCC nuclear grading., Key Points: • Lesion delineation uncertainties are tolerated within a VOI erosion range of 2 mm or dilation range of 4 mm within peritumor renal parenchyma for radiomics-based ccRCC nuclear grading. • Radiomics features extracted from unenhanced phase and excretory phase are superior to other single/combined phase(s) at differentiating high vs low nuclear grade ccRCC. • Shape features and first-order statistics features showed superior discriminative capability compared to texture features., (© 2021. European Society of Radiology.)

Objectives: We tried to realize accurate pathological classification, assessment of prognosis, and genomic molecular typing of renal cell carcinoma by CT texture feature analysis. To determine whether CT texture features can perform accurate pathological classification and evaluation of prognosis and genomic characteristics in renal cell carcinoma., Methods: Patients with renal cell carcinoma from five open-source cohorts were analyzed retrospectively in this study. These data were randomly split to train and test machine learning algorithms to segment the lesion, predict the histological subtype, tumor stage, and pathological grade. Dice coefficient and performance metrics such as accuracy and AUC were calculated to evaluate the segmentation and classification model. Quantitative decomposition of the predictive model was conducted to explore the contribution of each feature. Besides, survival analysis and the statistical correlation between CT texture features, pathological, and genomic signatures were investigated., Results: A total of 569 enhanced CT images of 443 patients (mean age 59.4, 278 males) were included in the analysis. In the segmentation task, the mean dice coefficient was 0.96 for the kidney and 0.88 for the cancer region. For classification of histologic subtype, tumor stage, and pathological grade, the model was on a par with radiologists and the AUC was 0.83 [Formula: see text] 0.1, 0.80 [Formula: see text] 0.1, and 0.77 [Formula: see text] 0.1 at 95% confidence intervals, respectively. Moreover, specific quantitative CT features related to clinical prognosis were identified. A strong statistical correlation (R 2  = 0.83) between the feature crosses and genomic characteristics was shown. The structural equation modeling confirmed significant associations between CT features, pathological (β =  - 0.75), and molecular subtype (β =  - 0.30)., Conclusions: The framework illustrates high performance in the pathological classification of renal cell carcinoma. Prognosis and genomic characteristics can be inferred by quantitative image analysis., Key Points: • The analytical framework exhibits high-performance pathological classification of renal cell carcinoma and is on a par with human radiologists. • Quantitative decomposition of the predictive model shows that specific texture features contribute to histologic subtype and tumor stage classification. • Structural equation modeling shows the associations of genomic characteristics to CT texture features. Overall survival and molecular characteristics can be inferred by quantitative CT texture analysis in renal cell carcinoma., (© 2021. The Author(s), under exclusive licence to European Society of Radiology.)

Objectives: To evaluate the utility of CT-based radiomics signatures in discriminating low-grade (grades 1-2) clear cell renal cell carcinomas (ccRCC) from high-grade (grades 3-4) and low TNM stage (stages I-II) ccRCC from high TNM stage (stages III-IV)., Methods: A total of 587 subjects (mean age 60.2 years ± 12.2; range 22-88.7 years) with ccRCC were included. A total of 255 tumors were high grade and 153 were high stage. For each subject, one dominant tumor was delineated as the region of interest (ROI). Our institutional radiomics pipeline was then used to extract 2824 radiomics features across 12 texture families from the manually segmented volumes of interest. Separate iterations of the machine learning models using all extracted features (full model) as well as only a subset of previously identified robust metrics (robust model) were developed. Variable of importance (VOI) analysis was performed using the out-of-bag Gini index to identify the top 10 radiomics metrics driving each classifier. Model performance was reported using area under the receiver operating curve (AUC)., Results: The highest AUC to distinguish between low- and high-grade ccRCC was 0.70 (95% CI 0.62-0.78) and the highest AUC to distinguish between low- and high-stage ccRCC was 0.80 (95% CI 0.74-0.86). Comparable AUCs of 0.73 (95% CI 0.65-0.8) and 0.77 (95% CI 0.7-0.84) were reported using the robust model for grade and stage classification, respectively. VOI analysis revealed the importance of neighborhood operation-based methods, including GLCM, GLDM, and GLRLM, in driving the performance of the robust models for both grade and stage classification., Conclusion: Post-validation, CT-based radiomics signatures may prove to be useful tools to assess ccRCC grade and stage and could potentially add to current prognostic models. Multiphase CT-based radiomics signatures have potential to serve as a non-invasive stratification schema for distinguishing between low- and high-grade as well as low- and high-stage ccRCC., Key Points: • Radiomics signatures derived from clinical multiphase CT images were able to stratify low- from high-grade ccRCC, with an AUC of 0.70 (95% CI 0.62-0.78). • Radiomics signatures derived from multiphase CT images yielded discriminative power to stratify low from high TNM stage in ccRCC, with an AUC of 0.80 (95% CI 0.74-0.86). • Models created using only robust radiomics features achieved comparable AUCs of 0.73 (95% CI 0.65-0.80) and 0.77 (95% CI 0.70-0.84) to the model with all radiomics features in classifying ccRCC grade and stage, respectively., (© 2021. European Society of Radiology.)

Objectives: We updated the experience on percutaneous microwave ablation for renal cell carcinoma with five-center data and long-term follow-up., Methods: This retrospective study reviewed the T1N0M0 renal cell carcinoma patients who underwent microwave ablation between April 2006 and December 2019. Clinicopathological and procedural data were collected. Technical effectiveness and complications were assessed, and the Kaplan-Meier method was used for cancer-specific survival, disease-free survival, overall survival, and local neoplastic process analyses., Results: A total of 323 consecutive patients (mean age, 62.9 years ± 14.0) with 371 biopsy-proved tumors (mean diameter, 2.9 cm ± 1.2) were enrolled, and 42.6% of the tumors were located adjacent to collecting system/bowel and technical effectiveness was achieved in 360 (97.0%) tumors. For 275 cT1a patients, during median follow-up time of 66.0 months (IQR, 58.4-73.6), 10-year local neoplastic processes, cancer-specific survival, disease-free survival, and overall survival rates were 1.9%, 87.4%, 71.8, and 67.5%, respectively. For 48 cT1b patients, during the median follow-up time of 30.4 months (IQR, 17.7-44.8), 5-year local tumor progression, cancer-specific survival, disease-free survival, and overall survival rates were 11.3%, 91.4%, 69.1, and 89.2%, respectively. Major complications showed no differences between cT1a (3.5%) and cT1b (6.9%) patients (p = 0.28). A clinical risk stratification system was developed based on multivariable model to predict DFS and CSS with c-indexes of 0.78 (95% CI: 0.71-0.85) and 0.77 (95% CI: 0.65-0.90), respectively., Conclusions: With matured follow-up at five institutions, ultrasound-guided percutaneous microwave ablation is a reliable treatment option for cT1a renal cell carcinoma even in dangerous location and appears to be promising for cT1b tumors., Key Points: • To our knowledge, this is the first multicenter cohort of long-term oncologic outcomes with percutaneous MWA of cT1 RCC. • The predicting model we developed is accurate to predict the long-term DFS and CSS, which can help to provide a better MWA prognostication over routinely available clinical information. • The available evidence shows that microwave ablation of clinical stage T1 RCC is safe and reliable with promising long-term oncologic outcomes, especially for cT1a RCC with excellent 10-year results., (© 2021. European Society of Radiology.)

Objectives: Nuclear grades are proved to be one of the most significant prognostic factors for clear cell renal cell carcinoma (ccRCC). Radiomics nomogram is a widely used noninvasive tool that could predict tumor phenotypes. In this study, we performed radiomics analysis to develop and validate a CT-based nomogram for the preoperative prediction of nuclear grades in ccRCC., Method: CT images and clinical data of 258 ccRCC patients were retrieved from the Cancer Imaging Archive (TCIA). Radiomics features were extracted from arterial-phase CT images using 3D Slicer software. LASSO regression model was performed to develop a radiomics signature in the training set (n = 143). A radiomics nomogram was constructed combining radiomics signature and selected clinical predictors. Receiver operating characteristic (ROC) curve and calibration curve were used to determine the performance of the radiomics nomogram in the training and validation set (n = 115). Decision curve analysis was used to assess the clinical usefulness of the CT-based nomogram., Results: One thousand three hundred sixteen radiomics features were extracted from arterial-phase CT images. A radiomics signature, consisting of 20 features, was developed and showed a favorable performance in discriminating nuclear grades with an area under the curve (AUC) of 0.914 and 0.846 in the training and validation set, respectively. The CT-based nomogram, including the radiomics signature and the CT-determined T stage, achieved good calibration and discrimination in the training set (AUC, 0.929; 95% CI, 0.886-0.972) and validation set (AUC, 0.876; 95% CI, 0.812-0.939). Decision curve analysis demonstrated the clinical usefulness of the CT-based nomogram., Conclusion: The noninvasive CT-based nomogram, including radiomics signature and CT-determined T stage, could improve the accuracy of preoperative grading of ccRCC and provide individualized treatment for ccRCC patients., Key Points: • Contrast-enhanced CT may help in preoperative grading of ccRCC. • The CT-based nomogram incorporated a radiomics signature and CT-determined T stage could preoperatively predict ccRCC grades. • The CT-based nomogram has the potential to improve individualized treatment and assist clinical decision making of ccRCC patients., (© 2021. European Society of Radiology.)

Objectives: To retrospectively evaluate whether the early dark cortical band (EDCB) on CT can be a predictor to differentiate clear cell renal cell carcinoma (ccRCC) from fat poor angiomyolipoma (Fp-AML) and to detect peritumoral pseudocapsules in ccRCC., Methods: The EDCBs, which are comprised of unenhanced thin lines at the tumor-renal cortex border in the corticomedullary phase, on the CT images of 342 patients who underwent partial nephrectomy were evaluated. Independent predictors among the clinical and CT findings for differentiating ccRCC from Fp-AML were identified using multivariate analyses. The diagnostic performance of the EDCB for diagnosing peritumoral pseudocapsule in ccRCC and differentiating ccRCC from Fp-AML was calculated., Results: The EDCB was observed in 157 of 254 (61.8%) ccRCCs, 4 of 31 (12.9%) chromophobe RCCs, 1 of 21 (4.8%) papillary RCCs, 3 of 11 (27.3%) clear cell papillary RCCs, 3 of 8 (37.5%) oncocytomas, and 0 of 17 (0%) Fp-AMLs. There was substantial interobserver agreement for the EDCB (k = 0.719). The EDCB was a significant predictor for differentiating ccRCC from Fp-AML (p < 0.001). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value of the EDCB for differentiating ccRCC from Fp-AML were 61.8%, 100%, 100%, and 14.9%, respectively, and those for detecting pseudocapsule in 236 ccRCCs were 62.3%, 68.8%, 96.5%, and 11.7%, respectively., Conclusion: Although diagnostic accuracy of the EDCB for detecting peritumoral pseudocapsule in RCC is inadequate, it can be a predictor for differentiating ccRCC from Fp-AML with high specificity and PPV., Key Points: • The early dark cortical band (EDCB) sign is observed in nearly two-thirds of clear cell renal cell carcinoma (ccRCC) that are treated by partial nephrectomy and have substantial interobserver agreement. • The EDCB is a significant predictor for differentiating ccRCCs from fat poor angiomyolipomas, with a high specificity and positive predictive value. • Diagnostic accuracy of the EDCB for detecting peritumoral pseudocapsule in ccRCC is inadequate, though better than those in the nephrographic and excretory-phase images., (© 2021. European Society of Radiology.)

Objective: To evaluate the effect of intravenous iodinated contrast on estimated glomerular filtration rate (eGFR) when administered immediately after thermal ablation of clinically localized T1a (cT1a) renal cell carcinoma (RCC)., Methods: This HIPAA-compliant, dual-center retrospective study was performed under a waiver of informed consent. Three hundred forty-two consecutive patients with cT1a biopsy-proven RCC were treated with percutaneous ablation between January 2010 and December 2017. Immediate post-ablation contrast-enhanced CT was the routine standard of care at one institution (contrast group), but not the other (control group). One-month pre- and 6-month post-ablation eGFR were compared using the Wilcoxon signed-rank test or the Kruskal-Wallis test. Multivariate linear regression was used to determine the effect of contrast on eGFR. A 1:1 propensity score matching was performed for all patients with a logistic model using patient, tumor, and procedural covariates., Results: In total, 246 patients (158 M; median age 69 years, IQR 62-74) were included. Median tumor diameter (2.4 vs 2.5, p = 0.23) and RENAL nephrometry scores (6 vs 6, p = 0.92), surrogates for ablation zone size, were similar. Baseline kidney function was similar for the control and contrast groups, respectively (median eGFR: 70 vs 74 mL/min/1.73 m 2 , p = 0.29). There was an expected mild decline in eGFR after ablation (control: 70 vs 60 mL/min/1.73 m 2 , p < 0.001; contrast: 75 vs 71 mL/min/1.73 m 2 , p = 0.001). Intravenous iodinated contrast was not associated with a decline in eGFR on multivariate linear regression (1.91, 95% CI - 3.43-7.24, p = 0.46) or 1:1 propensity score-matched model (- 0.33, 95% CI - 6.81-6.15, p = 0.92)., Conclusion: Intravenous iodinated contrast administered during ablation of cT1a RCC has no effect on eGFR., Key Points: • Intravenous iodinated contrast administered during thermal ablation of clinically localized T1a renal cell carcinoma has no effect on kidney function. • Thermal ablation of clinically localized T1a renal cell carcinoma results in a mild decline in kidney function. • A decline in kidney function is similar for radiofrequency and microwave ablation of clinically localized T1a renal cell carcinoma., (© 2021. European Society of Radiology.)

Purpose: To assess the safety and efficacy of contrast-enhanced ultrasound (CEUS) imaging for monitoring small (< 4 cm) renal masses (SRM) in patients undergoing active surveillance (AS)., Methods: We retrospectively selected all consecutive patients with SRMs who underwent AS for at least 6 months at our Institution between January 2014 and December 2018. CEUS imaging was performed by two experienced genitourinary radiologists at established time points. The accuracy of CEUS for monitoring SRM size was compared with that of CT scan. For solid SRMs, four enhancement patterns (EP) were recorded. Radiological progression was defined as SRM growth rate ≥ 5 mm/year., Results: Overall, 158/1049 (15.1%) patients with SRMs underwent AS. At a median follow-up of 25 months (IQR 13-39), no patient died due to renal cell carcinoma (RCC). No patients experienced CEUS-related adverse events. There was a large variability in the pattern of growth of SRMs (overall median growth rate: 0.40 mm/year), with 9.5% of SRMs showing radiological progression. The median SRM size was comparable between CEUS and CT scan examinations at all time points. The vast majority (92.7%) of SRMs did not show a change in their EP over time; and there was no association between the SRM's EP and radiological progression or SRM size. Overall, 43 (27.2%) patients underwent delayed intervention (DI); median SRM size, and median growth rate were significantly higher in these patients as compared to those continuing AS., Conclusion: In experienced hands, CEUS is a safe and effective strategy for active monitoring of SRMs in well-selected patients undergoing AS., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.)

Objectives: To develop a radiomics model using preoperative multiphasic CT for predicting distant metastasis after surgical resection in patients with localized clear cell renal cell carcinoma (ccRCC) and to identify key biological pathways underlying the predictive radiomics features using RNA sequencing data., Methods: In this multi-institutional retrospective study, a CT radiomics metastasis score (RMS) was developed from a radiomics analysis cohort (n = 184) for distant metastasis prediction. Using a gene expression analysis cohort (n = 326), radiomics-associated gene modules were identified. Based on a radiogenomics discovery cohort (n = 42), key biological pathways were enriched from the gene modules. Furthermore, a multigene signature associated with RMS was constructed and validated on an independent radiogenomics validation cohort (n = 37)., Results: The 9-feature-based RMS predicted distant metastasis with an AUC of 0.861 in validation set and was independent with clinical factors (p < 0.001). A gene module comprising 114 genes was identified to be associated with all nine radiomics features (p < 0.05). Four enriched pathways were identified, including ECM-receptor interaction, focal adhesion, protein digestion and absorption, and PI3K-Akt pathways. Most of them play important roles in tumor progression and metastasis. A 19-gene signature was constructed from the radiomics-associated gene module and predicted metastasis with an AUC of 0.843 in the radiogenomics validation cohort., Conclusions: CT radiomics features can predict distant metastasis after surgical resection of localized ccRCC while the predictive radiomics phenotypes may be driven by key biological pathways related to cancer progression and metastasis., Key Points: • Radiomics features from primary tumor in preoperative CT predicted distant metastasis after surgical resection in patients with localized ccRCC. • CT radiomics features predictive of distant metastasis were associated with key signaling pathways related to tumor progression and metastasis. • Gene signature associated with radiomics metastasis score predicted distant metastasis in localized ccRCC.

Objective: To evaluate the local tumour progression-free survival (LTPFS), metastasis-free survival (MFS), cancer-specific survival (CSS) and overall survival (OS) of healthy surgical candidates who underwent percutaneous thermoablation (TA) as a first-line therapy for small renal masses (T1a)., Methods: The institutional review board approved this bi-institutional retrospective study of 85 consecutive surgical candidates with 97 biopsy-proven malignant renal masses (T1a) treated with percutaneous TA from 2008 to 2016. The LTPFS, MFS, CSS and OS rates were calculated using the Kaplan-Meier method. Descriptive analysis was also performed., Results: The median tumour size was 2.3 cm (range, 0.7-3.9 cm). The minimal and mean follow-up periods were 24 and 56 months, respectively. Local recurrence was detected in four patients (4.7%) at 8.5, 13.8, 58.0 and 64.0 months of follow-up and retreated successfully with percutaneous TA. No patient developed metastatic renal cell carcinoma, and none died due to renal oncologic complications. One patient died of heart attack. The 5-year LTPFS, OS, MFS and CSS rates were 93.0%, 98.4%, 100% and 100%, respectively. Only two patients (2.3%) had major complications (Clavien-Dindo grade > II), including ureteropelvic junction stenosis and urinary obstruction due to ureteral blood clots., Conclusions: Our study demonstrates that percutaneous TA is a feasible and effective first-line therapy for healthy surgical candidates with small renal masses (T1a). The 5-year LTPFS, OS, CSS and MFS rates were 93.0%, 98.4%, 100% and 100%, respectively, with a major complication rate of only 2.3%., Key Points: • Image-guided percutaneous thermoablation of small renal malignancies was effective in 95.3% of the healthy surgical candidates. • Major complications were detected in 2.3% of the patients. • The local tumour progression-free survival rate was 97.6% and 93.0% at 3 and 5 years, respectively.

Background: Patients with tuberous sclerosis complex (TSC) can develop solid kidney masses from childhood. Imaging surveillance is done to detect renal cell carcinoma (RCC) and angiomyolipomas (AML), including AMLs at risk for hemorrhage. Intravenous contrast-enhanced ultrasound (CEUS) may be useful for screening as ultrasound is well tolerated by children and ultrasound contrast agents (UCA) are not nephrotoxic., Methods: Retrospective review of kidney CEUS exams of pediatric TSC patients. Qualitative CEUS analysis by consensus of 3 radiologists assessed rate, intensity, and pattern of lesion enhancement. Quantitative CEUS analysis was performed using Vuebox®. Where available, abdominal MRI was analyzed qualitatively for the same features and quantitatively by in-house-developed software. Time-intensity curves were generated from both CEUS and MRI where possible. Appearance of lesions were compared between CEUS and MRI and histology where available., Results: Nine masses in 5 patients included one histologically proven RCC and 8 AMLs diagnosed by imaging. Quantitative CEUS of RCC showed malignant features including increased peak enhancement 162%, rapid wash-in rate 162%, and elevated washout rate 156% compared to normal kidney tissue; versus AML which was 68%, 105%, and 125%, respectively. All masses were hypoenhancing on MRI compared to normal kidney tissue; MR dynamic contrast study offered no distinction between RCC and AML. The only MRI feature differentiating RCC from AML was absence of fat., Conclusion: Temporal resolution afforded by CEUS was useful to distinguish malignant from benign kidney masses. CEUS may prove useful for screening, characterizing, and follow-up of kidney lesions in pediatric TSC patients.

Objectives: To illustrate tumor contour irregularity on preoperative imaging with a practical method and further determine its value in predicting disease-free survival (DFS) in patients with pRCC (papillary renal cell carcinoma)., Methods: We performed a retrospective single-institution review of 267 Chinese pRCC patients between March 2009 and May 2019. Contour irregularity on cross-section was classified into smooth but distorted margin, unsmooth and sharply nodular margin, and blurred margin. Then, the ratio of the cross-section numbers of irregularity and the total tumor was defined as the contour irregular degree (CID). Cox regression and Kaplan-Meier analysis were performed to analyze the impact of CID on DFS. Then, the prognostic performance of CID was compared with pRCC risk stratification published by Leibovich et al. RESULTS: The median follow-up was 45 months (IQR: 23-69), in which 27 (10%) patients had metastasis or recurrence. Observed DFS rates were 95%, 90%, and 88% at 1, 3, and 5 years. The CID was an independent prognostic factor of DFS (HR = 1.048, 95% CI = 1.029-1.068, p < 0.001). The Kaplan-Meier plot showed that high-risk patients (CID ≥ 50%) tended to have a significantly shorter DFS (p < 0.001). The CID and Leibovich's pRCC model for DFS prediction had a C-index of 0.934 (95% CI = 0.907-0.961) and 0.833 (95% CI = 0.739-0.927) respectively., Conclusions: With our standard and practical method, the CID can be a reliable imaging marker for DFS prediction in patients with pRCC., Key Points: • The updated contour irregularity was an independent parameter for predicting disease-free survival in patients with pRCC. • High-risk pRCC patients (contour irregular degree ≥ 50%) tended to have a shorter disease-free survival. • Tumor contour irregularity in pRCC risk stratification outperformed Leibovich's model from our cohort.

Objective: To compare texture analysis (TA) features of solid renal masses on renal protocol (non-contrast enhanced [NECT], corticomedullary [CM], nephrographic [NG]) CT., Materials and Methods: A total of 177 consecutive solid renal masses (116 renal cell carcinoma [RCC]; 51 clear cell [cc], 40 papillary, 25 chromophobe, and 61 benign masses; 49 oncocytomas, 12 fat-poor angiomyolipomas) with three-phase CT between 2012 and 2017 were studied. Two blinded radiologists independently assessed tumor heterogeneity (5-point Likert scale) and segmented tumors. TA features (N = 25) were compared between groups and between phases. Accuracy (area under the curve [AUC]) for RCC versus benign and cc-RCC versus other masses was compared., Results: Subjectively, tumor heterogeneity differed between phases (p < 0.01) and between tumors within the same phase (p = 0.03 [NECT] and p < 0.01 [CM, NG]). Inter-observer agreement was moderate to substantial (intraclass correlation coefficient = 0.55-0.73). TA differed in 92.0% (23/25) features between phases (p < 0.05) except for GLNU and f6. More TA features differed significantly on CM (80.0% [20/25]) compared with NG (40.0% [10/25]) and NECT (16.0% [4/25]) (p < 0.01). For RCC versus benign, AUCs of texture features did not differ comparing CM and NG (p > 0.05), but were higher for 20% (5/25) and 28% (7/25) of features comparing CM and NG with NECT (p < 0.05). For cc-RCC versus other, 36% (9/25) and 40% (10/25) features on CM had higher AUCs compared with NECT and NG images (p < 0.05)., Conclusion: Texture analysis of renal masses differs, when evaluated subjectively and quantitatively, by phase of CT enhancement. The corticomedullary phase had the highest discriminatory value when comparing masses and for differentiating cc-RCC from other masses., Key Points: • Subjectively evaluated renal tumor heterogeneity on CT differs by phase of enhancement. • Quantitative CT texture analysis features in renal tumors differ by phases of enhancement with the corticomedullary phase showing the highest number and most significant differences compared with non-contrast-enhanced and nephrographic phase images. • For diagnosis of clear cell RCC, corticomedullary phase texture analysis features had improved accuracy of classification in approximately 40% of features studied compared with non-contrast-enhanced and nephrographic phase images.

Objectives: Using a radiomics framework to quantitatively analyze tumor shape and texture features in three dimensions, we tested its ability to objectively and robustly distinguish between benign and malignant renal masses. We assessed the relative contributions of shape and texture metrics separately and together in the prediction model., Materials and Methods: Computed tomography (CT) images of 735 patients with 539 malignant and 196 benign masses were segmented in this retrospective study. Thirty-three shape and 760 texture metrics were calculated per tumor. Tumor classification models using shape, texture, and both metrics were built using random forest and AdaBoost with tenfold cross-validation. Sensitivity analyses on five sub-cohorts with respect to the acquisition phase were conducted. Additional sensitivity analyses after multiple imputation were also conducted. Model performance was assessed using AUC., Results: Random forest classifier showed shape metrics featuring within the top 10% performing metrics regardless of phase, attaining the highest variable importance in the corticomedullary phase. Convex hull perimeter ratio is a consistently high-performing shape feature. Shape metrics alone achieved an AUC ranging 0.64-0.68 across multiple classifiers, compared with 0.67-0.75 and 0.68-0.75 achieved by texture-only and combined models, respectively., Conclusion: Shape metrics alone attain high prediction performance and high variable importance in the combined model, while being independent of the acquisition phase (unlike texture). Shape analysis therefore should not be overlooked in its potential to distinguish benign from malignant tumors, and future radiomics platforms powered by machine learning should harness both shape and texture metrics., Key Points: • Current radiomics research is heavily weighted towards texture analysis, but quantitative shape metrics should not be ignored in their potential to distinguish benign from malignant renal tumors. • Shape metrics alone can attain high prediction performance and demonstrate high variable importance in the combined shape and texture radiomics model. • Any future radiomics platform powered by machine learning should harness both shape and texture metrics, especially since tumor shape (unlike texture) is independent of the acquisition phase and more robust from the imaging variations.

Objective: To evaluate the performance of a multiparametric MRI radiomics-based nomogram for the individualised prediction of synchronous distant metastasis (SDM) in patients with clear cell renal cell carcinoma (ccRCC)., Methods: Two-hundred and one patients (training cohort: n = 126; internal validation cohort: n = 39; external validation cohort: n = 36) with ccRCC were retrospectively enrolled between January 2013 and June 2019. In the training cohort, the optimal MRI radiomics features were selected and combined to calculate the radiomics score (Rad-score). Incorporating Rad-score and SDM-related clinicoradiologic characteristics, the radiomics-based nomogram was established by multivariable logistic regression analysis, then the performance of the nomogram (discrimination and clinical usefulness) was evaluated and validated subsequently. Moreover, the prediction efficacy for SDM in ccRCC subgroups of different sizes was also assessed., Results: Incorporating Rad-score derived from 9 optimal MR radiomics features (age, pseudocapsule and regional lymph node), the radiomics-based nomogram was capable of predicting SDM in the training cohort (area under the ROC curve (AUC) = 0.914) and validated in both the internal and external cohorts (AUC = 0.854 and 0.816, respectively) and also showed a convincing predictive power in ccRCC subgroups of different sizes (≤ 4 cm, AUC = 0.875; 4-7 cm, AUC = 0.891; 7-10 cm, 0.908; > 10 cm, AUC = 0.881). Decision curve analysis indicated that the radiomics-based nomogram is of clinical usefulness., Conclusions: The multiparametric MRI radiomics-based nomogram could achieve precise individualised prediction of SDM in patients with ccRCC, potentially improving the management of ccRCC., Key Points: • Radiomics features derived from multiparametric magnetic resonance images showed relevant association with synchronous distant metastasis in clear cell renal cell carcinoma. • MRI radiomics-based nomogram may serve as a potential tool for the risk prediction of synchronous distant metastasis in clear cell renal cell carcinoma.

Objectives: Solid renal masses have unknown malignant potential with commonly utilized imaging. Biopsy can offer a diagnosis of cancer but has a high non-diagnostic rate and complications. Reported use of multiparametric magnetic resonance imaging (mpMRI) to diagnose aggressive histology (i.e., clear cell renal cell carcinoma (ccRCC)) via a clear cell likelihood score (ccLS) was based on retrospective review of cT1a tumors. We aim to retrospectively assess the diagnostic performance of ccLS prospectively assigned to renal masses of all stages evaluated with mpMRI prior to histopathologic evaluation., Methods: In this retrospective cohort study from June 2016 to November 2019, 434 patients with 454 renal masses from 2 institutions with heterogenous patient populations underwent mpMRI with prospective ccLS assignment and had pathologic diagnosis. ccLS performance was assessed by contingency table analysis. The association between ccLS and ccRCC was assessed with logistic regression., Results: Mean age and tumor size were 60 ± 13 years and 5.4 ± 3.8 cm. Characteristics were similar between institutions except for patient age and race (both p < 0.001) and lesion laterality and histology (both p = 0.04). The PPV of ccLS increased with each increment in ccLS (ccLS1 5% [3/55], ccLS2 6% [3/47], ccLS3 35% [20/57], ccLS4 78% [85/109], ccLS5 93% [173/186]). Pooled analysis for ccRCC diagnosis revealed sensitivity 91% (258/284), PPV 87% (258/295) for ccLS ≥ 4, and specificity 56% (96/170), NPV 94% (96/102) for ccLS ≤ 2. Diagnostic performance was similar between institutions., Conclusions: We confirm the optimal diagnostic performance of mpMRI to identify ccRCC in all clinical stages. High PPV and NPV of ccLS can help inform clinical management decision-making., Key Points: • The positive predictive value of the clear cell likelihood score (ccLS) for detecting clear cell renal cell carcinoma was 5% (ccLS1), 6% (ccLS2), 35% (ccLS3), 78% (ccLS4), and 93% (ccLS5). Sensitivity of ccLS ≥ 4 and specificity of ccLS ≤ 2 were 91% and 56%, respectively. • When controlling for confounding variables, ccLS is an independent risk factor for identifying clear cell renal cell carcinoma. • Utilization of the ccLS can help guide clinical care, including the decision for renal mass biopsy, reducing the morbidity and risk to patients.

Objectives: To evaluate the relationship between imperceptible T1 enhancement of papillary renal cell carcinoma (pRCC) on MR and intratumoral hemosiderin deposition., Methods: One hundred ten pRCCs (≤ 7 cm) were evaluated by MR with in- and opposed-phase spoiled gradient echo (GRE) and T1-weighted spoiled GRE with fat suppression before and after contrast. Hemosiderin deposition was assessed by SI index and D index on in- and opposed-phase images. SI index and D index are calculated as (SI in - SI opp )/(SI in ) × 100, where SI in and SI opp are tumor signal intensities on in- and opposed-phase images and (D in )/(D opp ), where D in and D opp are tumor diameters on in- and opposed-phase images, respectively. The degree of tumor enhancement was classified as grade 1 (no), grade 2 (subtle), or grade 3 (definite). Tumor enhancement on CT was assessed when available., Results: Five (5%), 10 (9%), and 95 (86%) tumors were categorized as grades 1, 2, and 3 enhancement, respectively. The mean SI index was - 33.9, - 25.3, and 1.00, whereas the mean D index was 1.26, 1.05, and 1.00 in tumors with grades 1, 2, and 3 enhancement, respectively. Tumors with grade 1 enhancement had significantly lower SI index (p = 0.001) and higher D index (p = 0.005) than those with grade 3 enhancement. Among six tumors with grade 1 or 2 enhancement and available CT, four tumors showed > 20 HU enhancement., Conclusions: pRCC with no subjective enhancement on contrast-enhanced MR showed hemosiderin deposition evident by lower SI index and higher D index . Hemosiderin deposition might mask the tumor enhancement on MR., Key Points: • 5% of papillary renal cell carcinoma showed imperceptible enhancement on contrast-enhanced MR. • Hemosiderin deposition in papillary renal cell carcinoma might mask the tumor enhancement on contrast-enhanced MR due to T2/T2*-shortening effects. • A renal lesion with extensive hemosiderin deposition but no perceptible enhancement on MR should be considered suspicious for papillary renal cell carcinoma.

Objectives: To explore whether clear cell renal cell carcinoma (ccRCC), papillary renal cell carcinoma (pRCC), and chromophobe renal cell carcinoma (cRCC) can be distinguished using radiomics features extracted from magnetic resonance (MR) images., Methods: Seventy-seven patients (ccRCC = 32, pRCC = 23, cRCC = 22) underwent MRI before surgery between May 2013 and August 2018 in this retrospective study. Thirty-nine radiomics features were extracted from tumor volumes on three sequences (T2WI, EN-T1WI CMP, and EN-T1WI NP). The Kruskal-Wallis test with Bonferonni correction and variance threshold were used for feature selection among the three RCC subtypes. ROC curves for the three subtypes were generated based on radiomics features. AUC, accuracy, sensitivity, and specificity for subtype differentiation are reported. Linear discriminant analysis (LDA) was used to assess the discriminative ability of these radiomics features., Results: Significant radiomics features among the three subtypes were identified, and ROC curves achieved excellent AUCs for T2WI, EN-T1WI CMP, EN-T1WI NP, and combined three MR sequences (0.631, 0.790, 0.959, and 0.959 between ccRCC and cRCC; 0.688, 0.854, 0.909, and 0.955 between pRCC and cRCC; 0.747, 0.810, 0.814, and 0.890 between ccRCC and pRCC). In addition, LDA demonstrated the three RCC subtypes were correctly classified by radiomics analysis (66.2% for EN-T1WI CMP, 71.4% for EN-T1WI NP, 55.8% for T2WI, and 71.4% for the combined three MR sequences)., Conclusions: Radiomics analysis can be used to differentiate among ccRCC, pRCC, and cRCC based on radiomics features extracted from multiple-sequence MRI and may help diagnose and treat RCC patients in the future, while further study is still needed., Key Points: • Radiomics features on multiple-sequence MRI can help differentiate the three subtypes of renal cell carcinoma (clear cell, papillary renal cell, and chromophobe renal cell carcinoma). • Radiomics features based on MRI indicate greater textural heterogeneity on ccRCCs than pRCCs and cRCCs (the highest AUCs on EN-T1WI NP are 0.814 for ccRCCs vs pRCCs and 0.959 for ccRCCs vs cRCCs, respectively). • There is a significant difference in the textural heterogeneity of radiomics features between pRCCs and cRCCs (the AUC is 0.909, 0.854, and 0.688 on EN-T1WI NP, EN-T1WI CMP, and T2WI, respectively).

Objectives: To develop a deep learning-based method for automated classification of renal cell carcinoma (RCC) from benign solid renal masses using contrast-enhanced computed tomography (CECT) images., Methods: This institutional review board-approved retrospective study evaluated CECT in 315 patients with 77 benign (57 oncocytomas, and 20 fat-poor angiomyolipoma) and 238 malignant (RCC: 123 clear cell, 69 papillary, and 46 chromophobe subtypes) tumors identified consecutively between 2015 and 2017. We employed a decision fusion-based model to aggregate slice level predictions determined by convolutional neural network (CNN) via a majority voting system to evaluate renal masses on CECT. The CNN-based model was trained using 7023 slices with renal masses manually extracted from CECT images of 155 patients, cropped automatically around kidneys, and augmented artificially. We also examined the fully automated approach for renal mass evaluation on CECT. Moreover, a 3D CNN was trained and tested using the same datasets and the obtained results were compared with those acquired from slice-wise algorithms., Results: For differentiation of RCC versus benign solid masses, the semi-automated majority voting-based CNN algorithm achieved accuracy, precision, and recall of 83.75%, 89.05%, and 91.73% using 160 test cases, respectively. Fully automated pipeline yielded accuracy, precision, and recall of 77.36%, 85.92%, and 87.22% on the same test cases, respectively. 3D CNN reported accuracy, precision, and recall of 79.24%, 90.32%, and 84.21% using 160 test cases, respectively., Conclusions: A semi-automated majority voting CNN-based methodology enabled accurate classification of RCC from benign neoplasms among solid renal masses on CECT., Key Points: • Our proposed semi-automated majority voting CNN-based algorithm achieved accuracy of 83.75% for the diagnosis of RCC from benign solid renal masses on CECT images. • A fully automated CNN-based methodology classified solid renal masses with moderate accuracy of 77.36% using the same test images. • Employing 3D CNN-based methodology yielded slightly lower accuracy for renal mass classification compared with the semi- automated 2D CNN-based algorithm (79.24%).

Objectives: To perform a systematic review on apparent diffusion coefficient (ADC) values of renal tumor subtypes and meta-analysis on the diagnostic performance of ADC for differentiation of localized clear cell renal cell carcinoma (ccRCC) from other renal tumor types., Methods: Medline, Embase, and the Cochrane Library databases were searched for studies published until May 1, 2019, that reported ADC values of renal tumors. Methodological quality was evaluated. For the meta-analysis on diagnostic test accuracy of ADC for differentiation of ccRCC from other renal lesions, we applied a bivariate random-effects model and compared two subgroups of ADC measurement with vs. without cystic and necrotic areas., Results: We included 48 studies (2588 lesions) in the systematic review and 13 studies (1126 lesions) in the meta-analysis. There was no significant difference in ADC of renal parenchyma using b values of 0-800 vs. 0-1000 (p = 0.08). ADC measured on selected portions (sADC) excluding cystic and necrotic areas differed significantly from whole-lesion ADC (wADC) (p = 0.002). Compared to ccRCC, minimal-fat angiomyolipoma, papillary RCC, and chromophobe RCC showed significantly lower sADC while oncocytoma exhibited higher sADC. Summary estimates of sensitivity and specificity to differentiate ccRCC from other tumors were 80% (95% CI, 0.76-0.88) and 78% (95% CI, 0.64-0.89), respectively, for sADC and 77% (95% CI, 0.59-0.90) and 77% (95% CI, 0.69-0.86) for wADC. sADC offered a higher area under the receiver operating characteristic curve than wADC (0.852 vs. 0.785, p = 0.02)., Conclusions: ADC values of kidney tumors that exclude cystic or necrotic areas more accurately differentiate ccRCC from other renal tumor types than whole-lesion ADC values., Key Points: • Selective ADC of renal tumors, excluding cystic and necrotic areas, provides better discriminatory ability than whole-lesion ADC to differentiate clear cell RCC from other renal lesions, with area under the receiver operating characteristic curve (AUC) of 0.852 vs. 0.785, respectively (p = 0.02). • Selective ADC of renal masses provides moderate sensitivity and specificity of 80% and 78%, respectively, for differentiation of clear cell renal cell carcinoma (RCC) from papillary RCC, chromophobe RCC, oncocytoma, and minimal-fat angiomyolipoma. • Selective ADC excluding cystic and necrotic areas are preferable to whole-lesion ADC as an additional tool to multiphasic MRI to differentiate clear cell RCC from other renal lesions whether the highest b value is 800 or 1000.

ALK-rearranged renal cell carcinoma is a provisional entity in the 2016 WHO Classification of Tumors of the Urinary System and Male Genital Organs. The reported fusion partners included VCL, TPM3, EML4, STRN, and HOOK1. Herein, we present a peculiar renal cell carcinoma morphologically resembling metanephric adenoma and harboring a novel PLEKHA7-ALK fusion. Microscopically, the tumor is composed of bland epithelial cells with scant to moderate amount of amphophilic cytoplasm, round and uniform nuclei, delicate chromatin, and inconspicuous nucleoli, arranged in tightly packed small acini and angulated tubules. Papillary formation, intraluminal glomeruloid tufts, microcysts, and solid nests were focally observed. Psammomatous calcifications were evident. The tumor cells were diffusely reactive for CK7, AMACR, PAX8, and ALK, while non-reactive for WT1, BRAF V600E, CD57, carbonic anhydrase IX, TFE3, and cathepsin K. Fluorescence in situ hybridization showed breaking apart of ALK. A novel PLEKHA7exon18-ALKexon20 fusion was detected using ArcherDX FusionPlex next-generation sequencing panel and was further confirmed with reverse-transcriptase PCR. Our case demonstrates that in contrast to prior cases showing high-grade tumor cells, ALK-rearranged renal cell carcinoma may also present as a low-grade renal tumor mimicking metanephric adenoma. Immunohistochemistry and molecular testing are helpful to identify this tumor, which may be eligible for ALK inhibitor-targeted therapy.

Objectives: (1) To assess the methodological quality of radiomics studies investigating histological subtypes, therapy response, and survival in patients with renal cell carcinoma (RCC) and (2) to determine the risk of bias in these radiomics studies., Methods: In this systematic review, literature published since 2000 on radiomics in RCC was included and assessed for methodological quality using the Radiomics Quality Score. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool and a meta-analysis of radiomics studies focusing on differentiating between angiomyolipoma without visible fat and RCC was performed., Results: Fifty-seven studies investigating the use of radiomics in renal cancer were identified, including 4590 patients in total. The average Radiomics Quality Score was 3.41 (9.4% of total) with good inter-rater agreement (ICC 0.96, 95% CI 0.93-0.98). Three studies validated results with an independent dataset, one used a publically available validation dataset. None of the studies shared the code, images, or regions of interest. The meta-analysis showed moderate heterogeneity among the included studies and an odds ratio of 6.24 (95% CI 4.27-9.12; p < 0.001) for the differentiation of angiomyolipoma without visible fat from RCC., Conclusions: Radiomics algorithms show promise for answering clinical questions where subjective interpretation is challenging or not established. However, the generalizability of findings to prospective cohorts needs to be demonstrated in future trials for progression towards clinical translation. Improved sharing of methods including code and images could facilitate independent validation of radiomics signatures., Key Points: • Studies achieved an average Radiomics Quality Score of 10.8%. Common reasons for low Radiomics Quality Scores were unvalidated results, retrospective study design, absence of open science, and insufficient control for multiple comparisons. • A previous training phase allowed reaching almost perfect inter-rater agreement in the application of the Radiomics Quality Score. • Meta-analysis of radiomics studies distinguishing angiomyolipoma without visible fat from renal cell carcinoma show moderate diagnostic odds ratios of 6.24 and moderate methodological diversity.

Objective: To investigate externally validated magnetic resonance (MR)-based and computed tomography (CT)-based machine learning (ML) models for grading clear cell renal cell carcinoma (ccRCC)., Materials and Methods: Patients with pathologically proven ccRCC in 2009-2018 were retrospectively included for model development and internal validation; patients from another independent institution and The Cancer Imaging Archive dataset were included for external validation. Features were extracted from T1-weighted, T2-weighted, corticomedullary-phase (CMP), and nephrographic-phase (NP) MR as well as precontrast-phase (PCP), CMP, and NP CT. CatBoost was used for ML-model investigation. The reproducibility of texture features was assessed using intraclass correlation coefficient (ICC). Accuracy (ACC) was used for ML-model performance evaluation., Results: Twenty external and 440 internal cases were included. Among 368 and 276 texture features from MR and CT, 322 and 250 features with good to excellent reproducibility (ICC ≥ 0.75) were included for ML-model development. The best MR- and CT-based ML models satisfactorily distinguished high- from low-grade ccRCCs in internal (MR-ACC = 73% and CT-ACC = 79%) and external (MR-ACC = 74% and CT-ACC = 69%) validation. Compared to single-sequence or single-phase images, the classifiers based on all-sequence MR (71% to 73% in internal and 64% to 74% in external validation) and all-phase CT (77% to 79% in internal and 61% to 69% in external validation) images had significant increases in ACC., Conclusions: MR- and CT-based ML models are valuable noninvasive techniques for discriminating high- from low-grade ccRCCs, and multiparameter MR- and multiphase CT-based classifiers are potentially superior to those based on single-sequence or single-phase imaging., Key Points: • Both the MR- and CT-based machine learning models are reliable predictors for differentiating high- from low-grade ccRCCs. • ML models based on multiparameter MR sequences and multiphase CT images potentially outperform those based on single-sequence or single-phase images in ccRCC grading.

Background: The most common primary CNS tumor in children is the medulloblastoma, which generally occurs in the posterior fossa and can spread through the CNS and spinal cord. Although the recurrence of renal cell carcinoma as a secondary tumor to neuroblastoma has been reported with successful anti-neoplastic treatment, the rare occurrence of a child who initially had medulloblastoma and then developed translocation renal cell carcinoma has never been reported before., Case Presentation: We report the case of a 12-year-old boy who initially presented with complaints of vomiting and headache. An MRI head confirmed the presence of 4 × 4 × 3 cm lesion which was resected completely and histopathology report confirmed the diagnosis of medulloblastoma Grade IV. Four years later, the child came for a follow-up visit and during routine screening, a CT scan showed heterogeneous lesion arising from the lower pole calyx of right kidney. The patient was referred to pediatric surgery for right radical nephrectomy involving the right adrenal gland. The histopathology report was consistent with the diagnosis of translocation renal cell carcinoma., Conclusion: Central nervous system (CNS) tumors remain the leading cause of death among pediatric neoplasms. We advise genetic testing of index cases and the establishment of an international tumor registry for a challenging disease.

Objectives: To develop and validate a radiomics nomogram for preoperative differentiating renal angiomyolipoma without visible fat (AML.wovf) from homogeneous clear cell renal cell carcinoma (hm-ccRCC)., Methods: Ninety-nine patients with AML.wovf (n = 36) and hm-ccRCC (n = 63) were divided into a training set (n = 80) and a validation set (n = 19). Radiomics features were extracted from corticomedullary phase and nephrographic phase CT images. A radiomics signature was constructed and a radiomics score (Rad-score) was calculated. Demographics and CT findings were assessed to build a clinical factors model. Combined with the Rad-score and independent clinical factors, a radiomics nomogram was constructed. Nomogram performance was assessed with respect to calibration, discrimination, and clinical usefulness., Results: Fourteen features were used to build the radiomics signature. The radiomics signature showed good discrimination in the training set (AUC [area under the curve], 0.879; 95%; confidence interval [CI], 0.793-0.966) and the validation set (AUC, 0.846; 95% CI, 0.643-1.000). The radiomics nomogram showed good calibration and discrimination in the training set (AUC, 0.896; 95% CI, 0.810-0.983) and the validation set (AUC, 0.949; 95% CI, 0.856-1.000) and showed better discrimination capability (p < 0.05) compared with the clinical factor model (AUC, 0.788; 95% CI, 0.683-0.893) in the training set. Decision curve analysis demonstrated the nomogram outperformed the clinical factors model and radiomics signature in terms of clinical usefulness., Conclusions: The CT-based radiomics nomogram, a noninvasive preoperative prediction tool that incorporates the Rad-score and clinical factors, shows favorable predictive efficacy for differentiating AML.wovf from hm-ccRCC, which might assist clinicians in tailoring precise therapy., Key Points: • Differential diagnosis between AML.wovf and hm-ccRCC is rather difficult by conventional imaging modalities. • A radiomics nomogram integrated with the radiomics signature, demographics, and CT findings facilitates differentiation of AML.wovf from hm-ccRCC with improved diagnostic efficacy. • The CT-based radiomics nomogram might spare unnecessary surgery for AML.wovf.

Purpose: To evaluate accuracy of MRI in detecting renal tumor pseudocapsule (PC) invasion and to propose a classification based on imaging of PC status in patients with renal cell carcinoma., Methods: From January 2017 to June 2018, 58 consecutive patients with localized renal cell carcinoma were prospectively enrolled. MRI was performed preoperatively and PC was classified, according to its features, as follows: MRI-Cap 0 (absence of PC), MRI-Cap 1 (presence of a clearly identifiable PC), MRI-Cap 2 (focally interrupted PC), and MRI-Cap 3 (clearly interrupted and infiltrated PC). A 3D image reconstruction showing MRI-Cap score was provided to both surgeon and pathologist to obtain complete preoperative evaluation and to compare imaging and pathology reports. All patients underwent laparoscopic partial nephrectomy. In surgical specimens, PC was classified according to the renal tumor capsule invasion scoring system (i-Cap)., Results: A concordance between MRI-Cap and i-Cap was found in 50/58 (86%) cases. ρ coefficient for each MRI-cap and iCap categories was: MRI-Cap 0: 0.89 (p < 0.0001), MRI-Cap1: 0.75 (p < 0.0001), MRI-Cap 2: 0.76 (p < 0.0001), and MRI-Cap3: 0.87 (p < 0.0001). Sensitivity, specificity, positive predictive value, negative predictive value, and AUC were: MRI-Cap 0: Se 97.87% Spec 83.3%, PPV 95.8%, NPV 90.9%, and AUC 90.9; MRI-Cap 1: Se 77% Spec 95.5%, PPV 83.3%, NPV 93.5%, and AUC 0.86; MRI-Cap 2- iCap 2: Se 88% Spec 90%, PPV 79%, NPV 95%, and AUC 0.89; MRI-Cap 3: Se 94% Spec 95%, PPV 88%, NPV 97%, and AUC 0.94., Conclusions: MRI-Cap classification is accurate in evaluating renal tumor PC features. PC features can provide an imaging-guided landmark to figure out where a minimal margin could be preferable during nephron-sparing surgery.

Objective: To investigate the discriminative capabilities of different machine learning-based classification models on the differentiation of small (< 4 cm) renal angiomyolipoma without visible fat (AMLwvf) and renal cell carcinoma (RCC)., Methods: This study retrospectively collected 163 patients with pathologically proven small renal mass, including 118 RCC and 45 AMLwvf patients. Target region of interest (ROI) delineation, followed by texture feature extraction, was performed on a representative slice with the largest lesion area on each phase of the four-phase CT images. Fifteen concatenations of the four-phasic features were fed into 224 classification models (built with 8 classifiers and 28 feature selection methods), classification performances of the 3360 resultant discriminative models were compared, and the top-ranked features were analyzed., Results: Image features extracted from the unenhanced phase (UP) CT image demonstrated dominant classification performances over features from other three phases. The two discriminative models "SVM + t&#95;score" and "SVM + relief" achieved the highest classification AUC of 0.90. The 10 top-ranked features from UP included 1 shape feature, 5 first-order statistics features, and 4 texture features, where the shape feature and the first-order statistics features showed superior discriminative capabilities in differentiating RCC vs. AMLwvf through the t-SNE visualization., Conclusion: Image features extracted from UP are sufficient to generate accurate differentiation between AMLwvf and RCC using machine learning-based classification model., Key Points: • Radiomics extracted from unenhanced CT are sufficient to accurately differentiate angiomyolipoma without visible fat and renal cell carcinoma using machine learning-based classification model. • The highest discriminative models achieved an AUC of 0.90 and were based on the analysis of unenhanced CT, alone or in association with images obtained at the nephrographic phase. • Features related to shape and to histogram analysis (first-order statistics) showed superior discrimination compared with gray-level distribution of the image (second-order statistics, commonly called texture features).