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Background: This study aims to construct predicting models using radiomic and clinical features in predicting first-line vascular endothelial growth factor receptor-tyrosine kinase inhibitor (VEGFR-TKI) early resistance in metastatic clear cell renal cell carcinoma (mccRCC) patients. We also aim to explore the correlation of predicting models with short and long-term survival of mccRCC patients., Materials and Methods: In this retrospective study, 110 mccRCC patients from 2009 to 2019 were included and assigned into training and test sets. Radiomic features were extracted from tumor 3D-ROI of baseline enhanced CT images. Radiomic features were selected by Lasso method to construct a radiomic score. A combined nomogram was established using the combination of radiomic score and clinical factors. The discriminative abilities of the radiomic, clinical and combined nomogram were quantified using ROC curve. Cox regression analysis was used to test the correlation of nomogram score with progression-free survival (PFS) and overall survival (OS). PFS and OS were compared between different risk groups by log-rank test., Results: The radiomic, clinical and combined nomogram demonstrated AUCs of 0.81, 0.75, and 0.83 in training set; 0.79, 0.77, and 0.88 in test set. Nomogram score ≥ 1.18 was an independent prognostic factor of PFS (HR 0.22 (0.10, 0.47), p < 0.001) and OS (HR 0.38 (0.20, 0.71), p = 0.002), in training set. PFS in low-risk group were significantly longer than high-risk group in training (p < 0.001) and test (p < 0.001) set, respectively. OS in low-risk group were significantly longer than high-risk group in training (p = 0.003) and test (p = 0.009) set, respectively., Conclusion: A nomogram combining baseline radiomic signature and clinical factors helped detecting first-line VEGFR-TKI early resistance and predicting short and long-term prognosis in mccRCC patients., Competing Interests: Declarations Ethics approval and consent to participate This is a single-center retrospective study approved by the institutional board from our institution and informed consent was waived. Consent for publication Written informed consent for publication of their clinical details and/or clinical images was obtained from the patients.A copy of the consent form is available for review by the Editor of this journal. Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

In the phase 3 ZIRCON trial, [ 89 Zr]Zr-girentuximab positron emission tomography-computed tomography (PET-CT) detected the presence of clear-cell renal cell carcinoma (ccRCC) with a sensitivity of 86% and a specificity of 87% in patients with an indeterminate renal mass undergoing nephrectomy. 1 This imaging technique could be a promising tool that could revolutionize the management of small renal masses (SRMs) and ccRCC., Competing Interests: Declaration of interests N.A. has received honorarium before May 2021 and during his lifetime for consulting to Astellas, AstraZeneca, Aveo, Bayer, Bristol Myers Squibb, Calithera, Clovis, Eisai, Eli Lilly, EMD Serono, Exelixis, Foundation Medicine, Genentech, Gilead, Janssen, Merck, MEI Pharma, Nektar, Novartis, Pfizer, Pharmacyclics, and Seattle Genetics; and has received research funding during his lifetime (to N.A.’s institution) from Arnivas, Astellas, AstraZeneca, Bavarian Nordic, Bayer, Bristol Meyers Squibb, Calithera, Celldex, Clovis, CRISPR Therapeutics, Eisai, Eli Lilly, EMD Serono, Exelixis, Genentech, Gilead, Glaxo Smith Kline, Immunomedics, Janssen, Lava, Medivation, Merck, Nektar, Neoleukin, New Link Genetics, Novartis, Oric, Pfizer, Prometheus, Rexahn, Roche, Sanofi, Seattle Genetics, Takeda, and Tracon., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Clinically, the body mass index remains the most frequently used metric of overall obesity, although it is flawed by its inability to account for different adipose (i.e., visceral, subcutaneous, and inter/intramuscular) compartments, as well as muscle mass. Numerous prior studies have demonstrated linkages between specific adipose or muscle compartments to outcomes of multiple diseases. Although there are no universally accepted standards for body composition measurement, many studies use a single slice at the L3 vertebral level. In this study, we use computed tomography (CT) studies from patients in The Cancer Genome Atlas (TCGA) to compare current L3-based techniques with volumetric techniques, demonstrating potential limitations with level-based approaches for assessing outcomes. In addition, we identify gene expression signatures in normal kidney that correlate with fat and muscle body composition traits that can be used to predict sex-specific outcomes in renal cell carcinoma., (© 2024. The Author(s).)

Background: The widespread use of cross-sectional imaging has led to the increased detection of urological incidentalomas. Incidental renal and adrenal masses are the most commonly detected urological incidentalomas and are often encountered by general practitioners., Objective: This review aims to provide an evidence-based approach to managing renal and adrenal masses., Discussion: Renal lesions occur in 14% of computed tomography (CT) scans. Differentials include cysts (benign or malignant), angiomyolipomas, oncocytomas and renal cell carcinomas (RCCs). The Bosniak classification should be used for cystic renal lesions. Active treatment should be considered for RCCs that are >4 cm, symptomatic or rapidly growing. Patients with adrenal lesions should undergo functional work-up. If clinically concerned, screening tests include 1 mg overnight dexamethasone suppression test and plasma or urinary metanephrines. In the presence of hypertension or hypokalaemia, screening for hyperaldosteronism with the plasma aldosterone-to-plasma renin ratio should be considered. Benign adrenal adenomas on CT are <4 cm, homogenous and hypodense (Hounsfield unit <10).

This study aimed to evaluate the clinical significance of multi-slice spiral CT renal angiography combined with intraoperative ultrasound in laparoscopic partial nephrectomy. Eighty patients were seen at the Affiliated Hospital of Hebei University from January 2021 to December 2022. The patients were divided into two groups, the experimental group and the control group, with 40 cases in each group. The experimental group received laparoscopic partial nephrectomy combined with intraoperative ultrasound, while the control group received only conventional laparoscopic partial nephrectomy. The experimental group had significantly shorter operative time and intraoperative thermal ischaemia time (p <0.05) and had significant advantage in the detection of microscopic cancer foci (p = 0.04). The experimental group also had significantly lower of positive rate of postoperative incisal margin (p = 0.01). The experimental group had significantly lower of recurrence rate (p = 0.03). The study concluded that multi-slice spiral CT renal angiography combined with intraoperative ultrasound boasts various benefits in the treatment of patients with renal cell carcinoma, it is safe and effective with no significant impact on renal function. Key Words: CT renal angiography, Intraoperative ultrasound, Laparoscopic partial nephrectomy, Renal cancer.

Abstract: Approximately 10% of all renal cell carcinoma (RCC) patients develop a venous tumor thrombus, a major negative prognostic factor. Surgical excision is commonly required for RCC patients with tumor thrombus. Accurate preoperative diagnosis and assessment of tumor thrombus level is vital because the level of thrombus may influence treatment decisions. MRI, contrast-enhanced CT, 18 F-FDG PET/CT, and 68 Ga-PSMA PET/CT have been used to diagnose and evaluate the extent of tumor thrombus in RCC. However, no case of RCC with inferior vena cava tumor thrombus showing 68 Ga-DOTATATE uptake has been reported yet. Herein, we report a case of metastatic RCC with tumor thrombus in the inferior vena cava on 68 Ga-DOTATATE PET/CT., Competing Interests: Conflicts of interest and sources of funding: None., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Purpose: Succinate dehydrogenase (SDH)-deficient renal cell carcinoma (RCC) is a newly defined, rare subtype of renal cancer, associated with pathogenic variations in the Succinate Dehydrogenase Subunit B (SDHB) gene. Our aim is to investigate the imaging findings of SDHB-associated renal tumors, utilizing cross-sectional and FDG-PET imaging in patients with pathogenic variations in SDHB gene, to facilitate accurate tumor characterization., Methods: Twenty SDH-deficient tumors from 16 patients with pathogenic variations in SDHB gene were retrospectively evaluated using cross-sectional and FDG-PET imaging. Clinical findings such as demographics, family history, extra-renal findings and metastases were recorded. Tumor imaging characteristics on CT/MRI included were laterality, size, homogeneity, morphology, margins, internal content, T1/T2 signal intensity, enhancement features, and restricted diffusion., Results: Sixteen patients (median age 31 years, IQR 19-41, 8 males) were identified with 68.8 % of patients having a known family history of SDHB variation. 81.3 % of lesions were solitary and majority were solid (86.7 % on CT, 87.5 % on MRI) with well-defined margins in >62.5 % of lesions, without evidence of internal fat, calcifications, or vascular invasion. 100 % of lesions demonstrated restricted diffusion and avid enhancement, with degree >75 % for most lesions on CT and MRI. On FDG-PET, all renal masses showed increased radiotracer uptake. 43.8 % of patients demonstrated extra-renal manifestations and 43.8 % had distant metastasis., Conclusion: SDHB-associated RCC is predominantly noted in young patients with no gender predilection. On imaging, SDH-deficient RCC are frequently unilateral, solitary, and solid with well-defined margins demonstrating avid enhancement with variability in enhancement pattern and showing restricted diffusion., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

Purpose: We aimed to assess the diagnostic accuracy of the clear cell likelihood score (ccLS) and value of other selected magnetic resonance imaging (MRI) features in the characterization of indeterminate small renal masses (SRMs)., Methods: Fifty patients with indeterminate SRMs discovered on MRI between 2012 and 2023 were included. The ccLS for the characterization of clear cell renal cell carcinoma (ccRCC) was calculated and compared to the final diagnosis (ccRCC vs. 'all other' masses)., Results: The ccLS = 5 had a satisfactory accuracy of 64.0% and a very high specificity of 96.3%; however, its sensitivity of 26.1% was relatively low. Receiver operating curve (ROC) analysis revealed that from the selected MRI features, only T1 ratio and arterial to delayed enhancement (ADER) were good discriminators between ccRCC and other types of renal masses (area under curve, AUC = 0.707, p = 0.01; AUC = 0.673, p = 0.03; respectively). The cut-off points determined in ROC analysis using the Youden index were 0.73 (p = 0.01) for T1 ratio and 0.99 for ADER (p = 0.03). The logistic regression model demonstrated that ccLS = 5 and T1 ratio (OR = 15.5 [1.1-218.72], p = 0.04; OR = 0.002 [0.00-0.81], p = 0.04) were significant predictors of ccRCC., Conclusions: The ccLS algorithm offers an encouraging method for the standardization of imaging protocols to aid in the diagnosis and management of SRMs in daily clinical practice by enhancing detectability of ccRCC and reducing the number of unnecessary invasive procedures for benign or indolent lesions. However, its diagnostic performance needs multi-center large cohort studies to validate it before it can be incorporated as a diagnostic algorithm and will guide future iterations of clinical guidelines. The retrospective nature of our study and small patient population confined to a single clinical center may impact the generalizability of the results; thus, future studies are required to define whether employment of the T1 ratio or ADER parameter may strengthen the diagnostic accuracy of ccRCC diagnosis., (© 2024. The Author(s).)

Purpose: The aim of this study was to evaluate the impact of perirenal fat volume and perirenal fat density on prognosis in surgically treated non-metastatic renal cell carcinomas (RCC)., Methods and Materials: All consecutive patients who underwent partial or total nephrectomy surgery between March 2019 and December 2021 were assessed. Measurements of perirenal fat volume and perirenal fat density were performed on computed tomography (CT) images. The relationship between progression and perirenal fat parameters was evaluated using ROC analysis, Cox regression analysis, and Kaplan-Meier analysis., Results: In the study population comprising 118 patients diagnosed with RCC (74.6% male, mean age of 59.1 ± 11.8 years), the median follow-up duration was 43 months (interquartile range: 33-51 months). Perirenal fat volume (AUC: 0.669, 95% CI 0.538-0.799, p = 0.011) and perirenal fat density (AUC: 0.680, 95% CI 0.558-0.803, p = 0.007) demonstrated acceptable discrimination performance in predicting progression. There was a significant association between high perirenal fat volume and high perirenal fat density with poor progression-free survival (HR: 1.007, 95% CI 1.003-1.011, p = 0.001 vs. HR: 1.084, 95% CI 1.033-1.137, p = 0.001; respectively)., Conclusion: High perirenal fat volume and high perirenal fat density are independent predictors for poor progression-free survival. Perirenal fat parameters, easily obtainable from preoperative CT images, may serve as potential tools in predicting the prognosis of non-metastatic RCC., (© 2024. The Author(s) under exclusive licence to Japan Radiological Society.)

Clear cell renal carcinoma (ccRCC) is one of the cancers that posed a severe threat to human life on a global scale. The ADAMTS family has been proven to be involved in a number of tumor types, although it is yet unknown how they relate to ccRCC. The mRNA expression matrix and other clinically relevant information of 607 ccRCC were sourced from TCGA database. The role of ADAMTS family genes in ccRCC was determined by differential gene expression analysis and gene set enrichment analysis (GSEA). Employing stage grading, gene mutation, and survival analysis, the genes most linked to the prognosis of ccRCC were identified. The influence of genes on the pathway was determined by Kyoto Encyclopedia of Genes and Genes (KEGG) analysis. Following that, the gene's impact on ccRCC was verified by qRT-PCR, WB, MTT, Transwell detection, and a wound healing assay. Bioinformatics analysis showed that ADAMTS10 was overexpressed in cancerous tissues of people with ccRCC and its expression increased with tumor grade. Mutation analysis showed that the main cause of mutation in the ADAMTS family gene was amplification. The prognosis and survival of the ADAMTS10 elevated expression group were lower than those of the poorly expressed group, as demonstrated by a survival analysis. On the basis of the findings of MRI, we examined 60 clinical patients and collected their cancer along with the surrounding tissues. The results of qPCR detection showed that the expression of ADAMTS10 was considerably higher in cancerous regions of 60 clinical users than it was in the tissues nearby. Inhibiting ADAMTS10 development prevents cancer cells from proliferating, invading, and migrating. The KEGG analysis links ADAMTS10 to the NF-κB signal pathway. WB experiment confirmed that inhibiting ADAMTS10 expression can inhibit the activation of the NF-κB signal pathway. ADAMTS10 may be a promising prognostic marker for ccRCC that can be employed independently., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Purpose: Most clear cell renal cell carcinoma (ccRCC) overexpresses carbonic anhydrase IX (CAIX). [ 68 Ga]Ga-NY104 is a small-molecule PET agent selectively targeting CAIX. This study aims to assess the efficacy of [ 68 Ga]Ga-NY104 PET/CT to identify ccRCC., Materials and Methods: Participants were prospectively recruited in the study (ClinicalTrials.gov: NCT05902377). They were further divided into two groups: group 1, patients with primary renal mass who were scheduled for surgery, group 2, patients with suspected/confirmed metastatic ccRCC. All patients underwent [ 68 Ga]Ga-NY104 PET/CT., Results: A total of 47 patients (mean age, 58.8 years ± 13.5, 34 men) were recruited, including 20 patients in group 1 and 27 patients in group 2. The patient-level sensitivity, specificity, and accuracy of [ 68 Ga]Ga-NY104 PET scan was 62%, 33%, 58% for group 1 and 95%, 100%, 96% for group 2. [ 68 Ga]Ga-NY104 PET identified additional 26 disease regions in 67% (14/21) of patients that were previously unknown. The tumor uptake was correlated with immunohistochemical staining results., Conclusions: [68Ga]Ga-NY104 PET/CT has a high diagnostic efficacy for patients with metastatic ccRCC, while it might be of limited value in the diagnosis of primary ccRCC., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Purpose: Stereotactic ablative body radiotherapy (SABR) is a novel option to treat primary renal cell carcinoma. However, a high radiation dose may be received by the treated kidney, which may affect its function posttreatment. This study investigates the dose-effect relationship of kidney SABR with posttreatment renal function., Methods and Materials: This was a prespecified secondary endpoint of the multicenter FASTRACK II (Focal Ablative STereotactic RAdiotherapy for Cancers of the Kidney phase II) clinical trial (National Clinical Trial 02613819). Patients received either 26 Gy in a single fraction (SF) for tumors with a maximal diameter of 4 cm or less or 42 Gy in 3 fractions (multifraction [MF]) for larger tumors. To determine renal function change, 99m Tc-dimercaptosuccinic acid (DMSA) single-photon emission computed tomography/computed tomography (SPECT/CT) scans were acquired, and the glomerular filtration rate was estimated at baseline, 12, and 24 months posttreatment. Imaging data sets were rigidly registered to the planning CT where kidneys were segmented to calculate dose-response curves., Results: From 71 enrolled patients, 36 (51%) and 26 (37%) patients were included in this study based on availability of posttreatment data at 12 and 24 months, respectively. The ipsilateral kidney glomerular filtration rate decreased from baseline by 42% and 39% in the SF cohort and by 45% and 62% in the MF cohort, at 12 and 24 months, respectively (P < .03). The loss in renal function was 3.6%/Gy ± 0.8%/Gy and 4.5%/Gy ± 1.0%/Gy in the SF cohort and 1.7%/Gy ± 0.1%/Gy and 1.7%/Gy ± 0.2%/Gy in the MF cohort at 12 and 24 months, respectively. The major loss in renal function occurred in high-dose regions, where dose-response curves converged to a plateau., Conclusions: For the first time in a multicenter study, the dose-effect relationship at 12 and 24 months post-SABR treatment for primary renal cell carcinoma was quantified. Kidney function reduces linearly with dose up to 100 Gy BED 3 ., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Objective: To explore the performance of ultrasound image-based radiomics in predicting World Health Organization (WHO)/International Society of Urological Pathology (ISUP) grading of clear-cell renal cell carcinoma (ccRCC)., Methods: A retrospective study was conducted via histopathological examination on participants with ccRCC from January 2021 to August 2023. Participants were randomly allocated to a training set and a validation set in a 3:1 ratio. The maximum cross-sectional image of the lesion on the preoperative ultrasound image was obtained, with the region of interest (ROI) delineated manually. Radiomic features were computed from the ROIs and subsequently normalized using Z-scores. Wilcoxon test and least absolute shrinkage and selection operator (LASSO) regression were applied for feature reduction and model development. The performance of the model was estimated by indicators including area under the curve (AUC), sensitivity and specificity., Results: A total of 336 participants (median age, 57 y; 106 women) with ccRCC were finally included, of whom 243 had low-grade tumors (grade 1-2) and 93 had high-grade tumors (grade 3-4). A total of 1163 radiomic features were extracted from the ROIs for model construction and 117 informative radiomics features selected by Wilcoxon test were submitted to LASSO. Our ultrasound-based radiomics model included 51 features and achieved AUCs of 0.90 and 0.79 for the training and validation sets, respectively. Within the training set, the sensitivity and specificity measured 0.75 and 0.92, respectively, whereas in the validation set, the sensitivity and specificity measured 0.65 and 0.84, respectively. In the subgroup analysis, for the training and validation sets Philips AUCs were 0.91 and 0.75, Toshiba AUCs were 0.82 and 0.90, and General Electric AUCs were 0.95 and 0.82, respectively., Conclusion: Ultrasound-based radiomics can effectively predict the WHO/ISUP grading of ccRCC., Competing Interests: Conflict of interest The authors declare no conflict of interest., (Copyright © 2024 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.)

Introduction: Preoperative evaluation of inferior vena cava (IVC) wall invasion is very important to improve outcomes of patients with renal cell carcinoma (RCC), and may allow surgical urologists to treat the IVC more effectively. The objective of this study was to evaluate preoperative 18 F-FDG PET/CT in patients with RCC and IVC tumor thrombus (IVCTT) for the diagnosis of IVC wall invasion., Methods: This retrospective case-control study evaluated 68 patients with RCC with level I-IV tumor thrombus. According to the histopathologic examination result, the patients were divided into IVC wall invasion group and non-invasion group. The 18 F-FDG PET/CT features between two groups were analyzed. Furthermore, a logistic regression model was used to determine if there was an association between PET/CT features and IVC wall invasion., Results: Sixty-eight patients were evaluated, and 55.9% (38/68) had IVC wall invasion. Compared with non-invasion group, invasion group had higher SUVmax of RCC, higher SURmax (tumor to tumor thrombus ratio, Tu/Th), higher IVCTT coronal diameter, and longer IVCTT craniocaudal extent (all p < 0.05). Multivariate analysis showed that SURmax (Tu/Th) (OR 8.760 [95%CI, 1.019-75.310]; p = 0.048) and the maximum coronal diameter of IVCTT (OR 1.143 [95%CI, 1.029-1.269]; p = 0.028) were predictors of IVC wall invasion. A model combining SURmax (Tu/Th) and the maximum coronal diameter of IVCTT achieved an AUC of 0.855 (95%CI, 0.757-0.954). The specificity and sensitivity for assessing IVC wall invasion was 92.1% and 76.7%, respectively., Conclusions: Increases in SURmax (Tu/Th) and the maximum coronal diameter of IVCTT are associated with a higher probability of IVC wall invasion. Preoperative 18 F-FDG PET/CT imaging may be used to assess IVC wall invasion., (© 2024. The Author(s).)

Background: Body composition may be related to survival in patients with clear-cell renal cell carcinoma (ccRCC), but studies have not simultaneously considered adipose and muscle tissue quantity and radiodensity., Methods: We analyzed data from 1,022 patients with ccRCC who underwent nephrectomy between 2000 and 2020 at Memorial Sloan Kettering Cancer Center. Skeletal muscle, visceral adipose tissue, and subcutaneous adipose tissue indexes (cm2/m2) and radiodensities [Hounsfield units (HU)] were assessed from noncontrast presurgical CT scans; clinical and demographic characteristics were available from the time of surgery. HRs and confidence intervals were estimated for overall (OS) and disease-free survival (DFS) through March 2023 in multivariable models that simultaneously accounted for all body composition measures., Results: The median age of the patients was 58 years, 69% were male, and 90% were White. There were 169 OS events over 8,392 person-years and 253 DFS events over 7,753 person-years of follow-up. In adjusted analyses, poor OS was associated with lower skeletal muscle radiodensity [-10 HU, HR (95% confidence interval), 1.37 (1.05-1.77)] and greater visceral adipose tissue radiodensity [+10 HU, 1.66 (1.06-2.59)], with similar findings for DFS. Poor survival was also associated with greater visceral adipose tissue index [+40 cm2/m2, OS: 1.32 (0.97, 1.79); DFS: 1.33 (1.04, 1.71)]. Associations with skeletal muscle radiodensity were limited to patients with stage 1/2 disease., Conclusions: Radiodensities of skeletal muscle and visceral adipose tissues may be novel presurgical prognostic factors for patients with ccRCC., Impact: The findings underscore the importance of evaluating the full range of body composition features simultaneously in multivariable models., (©2024 American Association for Cancer Research.)

Background: With limitations of conventional imaging and biopsy, accurate, non-invasive techniques to detect clear-cell renal cell carcinoma in patients with renal masses remain an unmet need. 89 Zr-labelled monoclonal antibody ([ 89 Zr]Zr-girentuximab) has high affinity for carbonic anhydrase 9, a tumour antigen highly expressed in clear-cell renal cell carcinoma. We aimed to evaluate [ 89 Zr]Zr-girentuximab PET-CT imaging for detection and characterisation of clear-cell renal cell carcinoma., Methods: ZIRCON was a prospective, open-label, multicentre, phase 3 trial conducted at 36 research hospitals and practices across nine countries (the USA, Australia, Canada, the UK, Türkiye, Belgium, the Netherlands, Spain, and France). Patients aged 18 years or older with an indeterminate renal mass 7 cm or smaller (cT1) suspicious for clear-cell renal cell carcinoma and scheduled for nephrectomy received a single dose of [ 89 Zr]Zr-girentuximab (37 MBq ±10%; 10 mg girentuximab) intravenously followed by abdominal PET-CT imaging 5 days (±2 days) later. Surgery was performed no later than 90 days after administration of [ 89 Zr]Zr-girentuximab. Blinded central review, conducted by three independent readers, determined the histology from surgical samples. The coprimary endpoints, determined for each individual reader, were the sensitivity and specificity of [ 89 Zr]Zr-girentuximab PET-CT imaging to detect clear-cell renal cell carcinoma, with histopathological confirmation as standard of truth. Analyses were on the full analysis set of patients, defined as patients who had evaluable PET-CT imaging and a confirmed histopathological diagnosis. The trial is registered with ClinicalTrials.gov, NCT03849118, and EUDRA Clinical Trials Register, 2018-002773-21, and is closed to enrolment., Findings: Between Aug 14, 2019, and July 8, 2022, 371 patients were screened for eligibility, 332 of whom were enrolled. 300 patients received [ 89 Zr]Zr-girentuximab (214 [71%] male and 86 [29%] female). 284 (95%) evaluable patients were included in the primary analysis. The mean sensitivity was 85·5% (95% CI 81·5-89·6) and mean specificity was 87·0% (81·0-93·1). No safety signals were observed. Most adverse events were not or were unlikely to be related to [ 89 Zr]Zr-girentuximab, with most (193 [74%] of 261 events) occurring during or after surgery. The most common grade 3 or worse adverse events were post-procedural haemorrhage (in six [2%] of 261 patients), urinary retention (three [1%]), and hypertension (three [1%]). In 25 (8%) of 300 patients, 52 serious adverse events were reported, of which 51 (98%) occurred after surgery. There were no treatment-related deaths., Interpretation: Our results suggest that [ 89 Zr]Zr-girentuximab PET-CT has a favourable safety profile and is a highly accurate, non-invasive imaging modality for the detection and characterisation of clear-cell renal cell carcinoma, which has the potential to be practice changing., Funding: Telix Pharmaceuticals., Competing Interests: Declaration of interests BS has served as a consultant for Telix Pharmaceuticals, Veracyte, Merck, and Johnson & Johnson; has served as a speaker for Merck; and received travel support from Histosonics. AJP has received institutional funding and medical writing support from Telix Pharmaceuticals for this trial. J-CB has served as a consultant or advisor, and received honoraria and research funding from Bristol Myers Squibb, Conmed, Ipsen Pharmaceuticals, Merck Sharp and Dohme, Pfizer, and Intuitive Surgical. MAM has received institutional funding from Telix Pharmaceuticals; served as a board member for Oranomed, RayzeBio, Fusion, Advanced Molecular Imaging and Therapy, American College of Nuclear Medicine, and Society for Nuclear Medicine and Molecular Imaging; and holds stock or stock options from Advanced Molecular Imaging and Therapy, Gentem Health, and SoftThread. VM has received honoraria from Ethicon and Exelixis. AMS has received institutional research funding from Telix Pharmaceuticals, National Health and Medical Research Council, National Imaging Facility, National Breast Cancer Foundation, Medical Research Future Fund, Australian Cancer Research Foundation, and Victorian Cancer Agency; holds intellectual property licensing with Humanigen, AbbVie, and Life Science Pharmaceuticals; served as an advisory board member and consultant for ImmunOs and Imagion Bio; served as a board member, committee member, or chair for Fusion, Telix Pharmaceuticals, Australian and New Zealand Society of Nuclear Medicine, Australian and New Zealand Urogenital and Prostate Cancer trials group, Cooperative Trials Group for Neuro-Oncology, National Imaging Facility Molecular Imaging Theme, Australian Academy of Health and Medical Sciences, Melbourne Academic Centre for Health Molecular Imaging Theme, World Federation of Nuclear Medicine and Biology, Victorian Comprehensive Cancer Centre, Australian Nuclear Science and Technology Organisation External Advisory Board, and International Centers for Precision Oncology Foundation. CvP has served as consultant or advisor for Astellas and Merck; received honoraria from Astellas; and received travel funding from Ipsen. CBa has served as a data safety monitoring board or advisory board member for Telix Pharmaceuticals. BO has received research funding from Telix Pharmaceuticals. TA has received honoraria and research funding from Telix Pharmaceuticals. RM has received research funding from Telix Pharmaceuticals. DMS has received institutional research funding from Telix Pharmaceuticals; served as a consultant for Global Medical Solutions Taiwan, Progenics Pharmaceuticals, Heidelberg University, and DuChemBio; received payments or honoraria from the School of Breast Oncology, PrecisCa, and US Department of Justice; and served as an associate chair of Society for Nuclear Medicine and Molecular Imaging Scientific Program Committee. STL has received research funding from Telix Pharmaceuticals. NP-T has received honoraria from Actinium Pharmaceuticals; served as a consultant and advisor for Illumina, Imaginab, Actinium, and Progenics/Lantheus; a speaker for Actinium Pharmaceuticals and Telix Pharmaceuticals; and received institutional research funding from Actinium Pharmaceuticals, Imaginab, Regeneron Pharmaceuticals, Bristol Myers Squibb, Janssen, Clarity, Bayer Health, Telix Pharmaceuticals, and Ymabs. ACF is a board member of Molecular Decisions; a consultant or advisor for Verily; has received research funding from Earli, Filtricine, MagARRAY, and Calithera; and holds founders stock at Molecular Decisions, ACF's spouse is an employee or owner of Silverado Hospice and Antelope Valley Hospice. PA is an employee of Premier Research, and a consultant for Premier Research. KS is an employee of ABX-CRO advanced pharmaceutical services Forschungsgesellschaft. LT is an employee of Telix Pharmaceuticals and holds stock from Novartis. MW is an employee and holds patents and stock from Telix Pharmaceuticals. CBe is an employee of Telix Pharmaceuticals. CRWH was an employee of Telix Pharmaceuticals at the time of the trial. PM has received research funding from Telix Pharmaceuticals., (Copyright © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Abstract: Targeted immunotherapy became the most advanced approach for cancer treatment. Programmed death-1 (PD-1) expressed on activated T cells can reverse immune suppression and cause T-cell activation. Nivolumab, a PD-1 immune checkpoint inhibitor antibody that is a fully human immunoglobulin G4, blocks PD-1 and promotes antitumor immunity. Cabozantinib (tyrosine kinase inhibitor) inhibits the tyrosine kinase activity of vascular endothelial growth factor receptors 1, 2, and 3. As a result of enhancing immune response in normal tissues, immune-related adverse events can occur. Thyroid dysfunction is a common form of immune-related adverse event and seen on 18 F-FDG PET/CT scans post therapy., Competing Interests: Conflicts of interest and sources of funding: none declared., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Multilocular cystic nephroma (MLCN) is an unusual, benign slow-growing renal cystic neoplasm which mimics other cystic renal lesions and has such clinical, radiological, and morphological features that causes diagnostic dilemma. MLCN lies in the spectrum of mixed epithelial and stromal tumor (MEST) family of kidney. According to World Health Organization (WHO 2016 classification), MEST encompasses spectrum of tumors ranging from predominantly cystic tumors, adult cystic nephroma (ACN) to tumors that are variably solid (MEST), thus creating diagnostic dilemma. Moreover, it has several benign and malignant differentials due to its several overlapping histomorphological features which when not cautiously dealt with may result in misdiagnosing it as malignant lesion. We hereby present a case of a woman in late twenties who presented with left flank swelling and pain since 6 months which was misdiagnosed as renal cell carcinoma on radiology which turned out to be ACN on histology and further verified on immunohistochemistry., (© 2024. The Author(s), under exclusive licence to Japanese Society of Nephrology.)

Objectives: To evaluate radiomics features' reproducibility using inter-package/inter-observer measurement analysis in renal masses (RMs) based on MRI and to employ machine learning (ML) models for RM characterization., Methods: 32 Patients (23M/9F; age 61.8 ± 10.6 years) with RMs (25 renal cell carcinomas (RCC)/7 benign masses; mean size, 3.43 ± 1.73 cm) undergoing resection were prospectively recruited. All patients underwent 1.5 T MRI with T2-weighted (T2-WI), diffusion-weighted (DWI)/apparent diffusion coefficient (ADC), and pre-/post-contrast-enhanced T1-weighted imaging (T1-WI). RMs were manually segmented using volume of interest (VOI) on T2-WI, DWI/ADC, and T1-WI pre-/post-contrast imaging (1-min, 3-min post-injection) by two independent observers using two radiomics software packages for inter-package and inter-observer assessments of shape/histogram/texture features common to both packages (104 features; n = 26 patients). Intra-class correlation coefficients (ICCs) were calculated to assess inter-observer and inter-package reproducibility of radiomics measurements [good (ICC ≥ 0.8)/moderate (ICC = 0.5-0.8)/poor (ICC < 0.5)]. ML models were employed using reproducible features (between observers and packages, ICC > 0.8) to distinguish RCC from benign RM., Results: Inter-package comparisons demonstrated that radiomics features from T1-WI-post-contrast had the highest proportion of good/moderate ICCs (54.8-58.6% for T1-WI-1 min), while most features extracted from T2-WI, T1-WI-pre-contrast, and ADC exhibited poor ICCs. Inter-observer comparisons found that radiomics measurements from T1-WI pre/post-contrast and T2-WI had the greatest proportion of features with good/moderate ICCs (95.3-99.1% T1-WI-post-contrast 1-min), while ADC measurements yielded mostly poor ICCs. ML models generated an AUC of 0.71 [95% confidence interval = 0.67-0.75] for diagnosis of RCC vs. benign RM., Conclusion: Radiomics features extracted from T1-WI-post-contrast demonstrated greater inter-package and inter-observer reproducibility compared to ADC, with fair accuracy for distinguishing RCC from benign RM., Clinical Relevance: Knowledge of reproducibility of MRI radiomics features obtained on renal masses will aid in future study design and may enhance the diagnostic utility of radiomics models for renal mass characterization., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Purpose: Fumarate hydratase-deficient renal cell cancer (FHRCC) is a rare and aggressive form of renal cell carcinoma. The diagnostic value of 68 Ga-FAPI PET/CT for FHRCC remains unexplored. Therefore, we compared the potential value of 68 Ga-FAPI-04 and 18 F-FDG PET/CT in FHRCC., Patients and Methods: Patients with FHRCC underwent 68 Ga-FAPI-04 and 18 F-FDG PET/CT from May 2022 to December 2023. The SUV max and tumor-to-liver ratio (TLR) of both tracers were compared using the Wilcoxon signed rank test., Results: Eleven patients with 83 lesions were enrolled. The rate of 18 F-FDG PET/CT in detecting lesions was higher than that of 68 Ga-FAPI-04 PET/CT: primary tumors: 75.0% (6/8) versus 50.0% (4/8); lymph nodes: 94.9% (37/39) versus 89.7% (35/39); and bone lesions: 100.0% (21/21) versus 90.5% (19/21). The median SUV max of primary and metastatic lesions on 18 F-FDG PET/CT was comparable to 68 Ga-FAPI-04 PET/CT in semiquantitative analysis (primary lesions: 13.86 vs 16.35, P = 1.000; lymph nodes: 10.04 vs 9.33, P = 0.517; bone lesions: 13.49 vs 9.84, P = 0.107; visceral lesions: 8.54 vs 4.20, P = 0.056). However, the median TLRs of primary and metastatic lesions on 68 Ga-FAPI-04 PET/CT were higher than that of 18 F-FDG PET/CT (primary lesions: 30.44 vs 5.41, P = 0.010; lymph nodes: 17.71 vs 3.95, P = 0.000; bone lesions: 15.94 vs 5.21, P = 0.000; visceral lesions: 9.26 vs 3.44, P = 0.003)., Conclusions: 18 F-FDG PET/CT detected more primary and metastatic FHRCC lesions than 68 Ga-FAPI-04 PET/CT. However, the higher TLR in FHRCC on 68 Ga-FAPI-04 PET/CT may indicate therapeutic potential in targeting fibroblast activation protein in FHRCC., Competing Interests: Conflicts of interest and sources of funding: none declared., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Rationale and Objectives: This study aimed to develop a diagnostic model based on clinical and CT features for identifying clear cell renal cell carcinoma (ccRCC) in small renal masses (SRMs)., Material and Methods: This retrospective multi-centre study enroled patients with pathologically confirmed SRMs. Data from three centres were used as training set (n = 229), with data from one centre serving as an independent test set (n = 81). Univariate and multivariate logistic regression analyses were utilised to screen independent risk factors for ccRCC and build the classification and regression tree (CART) diagnostic model. The area under the curve (AUC) was used to evaluate the performance of the model. To demonstrate the clinical utility of the model, three radiologists were asked to diagnose the SRMs in the test set based on professional experience and re-evaluated with the aid of the CART model., Results: There were 310 SRMs in 309 patients and 71% (220/310) were ccRCC. In the testing cohort, the AUC of the CART model was 0.90 (95% CI: 0.81, 0.97). For the radiologists' assessment, the AUC of the three radiologists based on the clinical experience were 0.78 (95% CI:0.66,0.89), 0.65 (95% CI:0.53,0.76), and 0.68 (95% CI:0.57,0.79). With the CART model support, the AUC of the three radiologists were 0.93 (95% CI:0.86,0.97), 0.87 (95% CI:0.78,0.95) and 0.87 (95% CI:0.78,0.95). Interobserver agreement was improved with the CART model aids (0.323 vs 0.654, P < 0.001)., Conclusion: The CART model can identify ccRCC with better diagnostic efficacy than that of experienced radiologists and improve diagnostic performance, potentially reducing the number of unnecessary biopsies., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Purpose: Indeterminate renal masses are increasingly incidentally found on cross-sectional imaging. 99m Tc-sestamibi single-photon emission computed tomography/computed tomography (SPECT/CT) scans can be used to identify oncocytomas and oncocytic renal neoplasms, including a subset of chromophobe renal cell carcinomas (chRCCs), which are viewed as false-positive., Procedure: Patients imaged with renal sestamibi scans between 2014 and 2023 were reviewed. Those patients with solitary tumors that were originally classified as chRCC were included in the analysis. Imaging with SPECT/CT from the liver dome down had been carried out 75 min after the administration of 925 MBq of 99m Tc-sestamibi. All available H&E and immunostained slides were re-reviewed and classified according to WHO 2022 criteria. Confirmatory immunohistochemical stains were performed in tumors considered morphologically suspicious for non-chRCC entities., Result: A total of 18 patients with solitary tumors were included in the final analysis. 13/18 (72.2%) tumors in this cohort remained classified as chRCC, with 4/18 (22.2%) being eosinophilic-variant chRCC. The reclassified tumors (5/18 [27.8%]) included 2/18 (11.1%) low-grade oncocytic tumor (LOT), 1/18 (5.5%) eosinophilic vacuolated tumor (EVT), and 2/18 (11.1%) unclassified low-grade oncocytic neoplasms. As such, only 2/9 (22.2%) qualitatively "hot" tumors were chRCC other than eosinophilic-variant and only 1/9 (11.1%) "cold" tumors was a histology other than chRCC., Conclusion: Based on current histopathologic classification methods, it is likely that the "false-positive" rate of uptake on renal sestamibi scans with chRCC has been over-stated. Further study is warranted to better refine the optimal utility of renal sestamibi scans for non-invasive risk stratification of indeterminate renal masses., (© 2024. The Author(s), under exclusive licence to World Molecular Imaging Society.)

Purpose: This study aimed to develop and validate a computed tomography-based nomogram assessing visceral and subcutaneous adiposity for predicting outcomes in localized clear cell renal cell carcinoma (ccRCC)., Methods: A cohort of 364 patients with pathologically confirmed ccRCC participated in this retrospective study, with 254 patients assigned to the training set and 110 to the validation set (a 7:3 distribution ratio). The adipose score (AS) was generated using the least absolute shrinkage and selection operator Cox regression. Subsequently, a nomogram was constructed by integrating the clinical independent predictor with the AS to predict disease-free survival (DFS) in localized ccRCC after surgery. The performance of the nomogram was compared with the University of California, Los Angeles, Integrated Staging System (UISS), and the Stage, Size, Grade, and Necrosis (SSIGN) score., Results: In both the training and validation cohorts, the nomogram exhibited superior discrimination compared to SSIGN and UISS (C-index: 0.897 vs. 0.781 vs. 0.776 in the training cohort, and 0.752 vs. 0.596 vs. 0.686 in the validation cohort; 5 year AUC: 0.907 vs. 0.805 vs. 0.820 in the training cohort, and 0.832 vs. 0.577 vs. 0.726 in the validation cohort). Decision curve analysis (DCA) revealed a superior net benefit across a wider range of threshold probabilities for predicting 5 year DFS compared to UISS and SSIGN scores., Conclusions: The developed prognostic nomogram demonstrated high accuracy and overall superior performance compared to existing prognostic models., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, 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: Dialysis patients are at an increased risk of developing renal cell carcinoma (RCC); however, differentiating between RCC and benign cysts can sometimes be difficult using modalities, such as computed tomography (CT) and ultrasonography. 18 F-Fluorodeoxyglucose positron emission tomography (FDG-PET)/CT efficiently detects malignant tumors; however, physiological accumulation of FDG in the kidney limits its efficacy in detecting renal tumors. However, in patients with severely impaired renal function, the renal accumulation of FDG is decreased, possibly improving the detection of renal malignancies in this patient population. This study evaluated the usefulness of FDG-PET/CT as a screening tool for detecting RCC in patients with end-stage renal disease., Materials and Methods: This prospective study recruited 150 participants from 2012 to 2016 who were on dialysis or underwent renal transplantation and were on dialysis until transplantation. FDG-PET/CT was performed to screen for RCC. Three radiologists independently evaluated the images. No protocol was defined for the additional management of positive examinations, leaving decisions to the discretion of each participant. Negative examinations were observed until the end of 2019., Results: In total, 150 participants (mean age, 58 ± 13 years; 105 men) underwent FDG-PET/CT. Twenty patients (13.4%) were diagnosed as positive. Fifteen patients underwent additional examinations and/or procedures, and RCC was found in seven patients. Of the four patients who underwent surgical resection, the pathological results were clear cell RCC in one, papillary RCC in one, and acquired cystic disease-associated RCC in two. Two participants were diagnosed with RCC on bone biopsy, and one was diagnosed on dynamic CT but opted for observation. The sensitivity, specificity, and negative predictive value were 100%, 93.9%, and 100%, respectively., Conclusion: FDG-PET/CT was useful for detecting RCC in patients with end-stage renal disease. Our findings show the potential use of FDG-PET/CT as a screening tool for RCC in this patient population., (© 2024. The Author(s).)

Laminin, fibronectin, and collagen IV are pivotal extracellular matrix (ECM) components. The ECM environment governs the fundamental properties of tumors, including proliferation, vascularization, and invasion. Given the critical role of cell-matrix adhesion in malignant tumor progression, we hypothesize that the concentrations of these proteins may be altered in the plasma of patients with clear cell renal cell carcinoma (ccRCC). This study aimed to evaluate the serum, urine, and tissue levels of laminin-5, collagen IV, and fibronectin among a control group and ccRCC patients, with the latter divided into stages T1-T2 and T3-T4 according to the TNM classification. We included 60 patients with histopathologically confirmed ccRCC and 26 patients diagnosed with chronic cystitis or benign prostatic hyperplasia (BPH). Collagen IV, laminin-5, and fibronectin were detected using Surface Plasmon Resonance Imaging biosensors. Significant differences were observed between the control group and ccRCC patients, as well as between the T1-T2 and T3-T4 subgroups. Levels were generally higher in plasma and tissue for fibronectin and collagen IV in ccRCC patients and lower for laminin. The ROC (Receiver operating characteristic) analysis yielded satisfactory results for differentiating between ccRCC patients and controls (AUC 0.84-0.93), with statistical significance for both fibronectin and laminin in plasma and urine. Analysis between the T1-T2 and T3-T4 groups revealed interesting findings for all examined substances in plasma (AUC 0.8-0.95). The results suggest a positive correlation between fibronectin and collagen levels and ccRCC staging, while laminin shows a negative correlation, implying a potential protective role. The relationship between plasma and urine concentrations of these biomarkers may be instrumental for tumor detection and staging, thereby streamlining therapeutic decision-making.

Objectives: To develop radiomics models based on multi-sequence MRI from two centers for the preoperative prediction of the WHO/ISUP grade of Clear Cell Renal Cell Carcinoma (ccRCC)., Methods: This retrospective study included 334 ccRCC patients from two centers. Significant clinical factors were identified through univariate and multivariate analyses. MRI sequences included Dynamic contrast-enhanced MRI, axial fat-suppressed T2-weighted imaging, diffusion-weighted imaging, and in-phase/out-of-phase images. Feature selection methods and logistic regression (LR) were used to construct clinical and radiomics models, and a combined model was developed using the Rad-score and significant clinical factors. Additionally, seven classifiers were used to construct the combined model and different folds LR was used to construct the combined model to evaluate its performance. Models were evaluated using receiver operating characteristic (ROC) curves, area under the curve (AUC), and decision curve analysis (DCA). The Delong test compared ROC performance, with p < 0.050 considered significant., Results: Multivariate analysis identified intra-tumoral vessels as an independent predictor of high-grade ccRCC. In the external validation set, the radiomics model (AUC = 0.834) outperformed the clinical model (AUC = 0.762), with the combined model achieving the highest AUC (0.855) and significantly outperforming the clinical model (p = 0.003). DCA showed that the combined model had a higher net benefit within the 0.04-0.54 risk threshold range than clinical model. Additionally, the combined model constructed using logistic regression has a higher priority compared to other classifiers. Additionally, 10-fold cross-validation with LR for the combined model showed consistent AUC values (0.849-0.856) across different folds., Conclusion: The radiomics models based on multi-sequence MRI might be a noninvasive and effective tool, demonstrating good efficacy in preoperatively predicting the WHO/ISUP grade of ccRCC., (© 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.)

Purpose: We aimed to develop and externally validate a CT-based deep learning radiomics model for predicting overall survival (OS) in clear cell renal cell carcinoma (ccRCC) patients, and investigate the association of radiomics with tumor heterogeneity and microenvironment., Methods: The clinicopathological data and contrast-enhanced CT images of 512 ccRCC patients from three institutions were collected. A total of 3566 deep learning radiomics features were extracted from 3D regions of interest. We generated the deep learning radiomics score (DLRS), and validated this score using an external cohort from TCIA. Patients were divided into high and low-score groups by the DLRS. Sequencing data from the corresponding TCGA cohort were used to reveal the differences of tumor heterogeneity and microenvironment between different radiomics score groups. What's more, univariate and multivariate Cox regression were used to identify independent risk factors of poor OS after operation. A combined model was developed by incorporating the DLRS and clinicopathological features. The SHapley Additive exPlanation method was used for interpretation of predictive results., Results: At multivariate Cox regression analysis, the DLRS was identified as an independent risk factor of poor OS. The genomic landscape of different radiomics score groups was investigated. The heterogeneity of tumor cell and tumor microenvironment significantly varied between both groups. In the test cohort, the combined model had a great predictive performance, with AUCs (95%CI) for 1, 3 and 5-year OS of 0.879(0.868-0.931), 0.854(0.819-0.899) and 0.831(0.813-0.868), respectively. There was a significant difference in survival time between different groups stratified by the combined model. This model showed great discrimination and calibration, outperforming the existing prognostic models (all p values < 0.05)., Conclusion: The combined model allowed for the prognostic prediction of ccRCC patients by incorporating the DLRS and significant clinicopathologic features. The radiomics features could reflect the tumor heterogeneity and microenvironment., (© 2024. The Author(s).)

Background: Renal epithelioid angiomyolipoma is a rare and unique subtype of classic angiomyolipoma, characterized by the presence of epithelioid cells. It often presents with nonspecific symptoms and can be easily misdiagnosed due to its similarity to renal cell carcinoma and classic angiomyolipoma in clinical and radiological features. This case report is significant for its demonstration of the challenges in diagnosing epithelioid angiomyolipoma and its emphasis on the importance of accurate differentiation from renal cell carcinoma and classic angiomyolipoma., Case Presentation: A 58-year-old Asian female presented with sudden left flank pain and was initially diagnosed with a malignant renal tumor based on imaging studies. She underwent laparoscopic radical nephrectomy, and postoperative histopathology confirmed the diagnosis of epithelioid angiomyolipoma. The patient recovered well and is currently in good health with regular follow-ups. This case highlights the diagnostic challenges, with a focus on the clinical, radiological, and histopathological features that eventually led to the identification of epithelioid angiomyolipoma., Conclusions: Epithelioid angiomyolipoma is easily misdiagnosed in clinical work. When dealing with these patients, it is necessary to make a comprehensive diagnosis based on clinical symptoms, imaging manifestations, and pathological characteristics., (© 2024. The Author(s).)

Objective: Contrast-enhanced computed tomography (CECT) improves lesion contrast with surrounding tissues through the injection of contrast agents. This enhancement allows for more precise lesion characterization, aiding in the early diagnosis of clear cell renal cell carcinoma (ccRCC). This meta-analysis aims to assess the diagnostic efficacy of CECT in ccRCC and to provide an ideal imaging examination method for the preoperative diagnosis of ccRCC., Methods: We conducted a comprehensive search across six major online databases: PubMed, Web of Science, Cochrane Library, WANFANG DATA, China National Knowledge Infrastructure, and Chinese BioMedical Literature Database (CBM). The objective was to collate and analyze studies that evaluate the diagnostic utility of CECT in the identification of ccRCC. Meta-disc 1.4 and Stata 16.0 were used to conduct a meta-analysis and evaluate the diagnostic accuracy of CECT for ccRCC., Results: The meta-analysis included 17 relevant studies investigating the diagnostic value of CECT for ccRCC. The combined sensitivity and specificity of CECT were 0.88 (95% confidence interval: 0.83-0.91) and 0.82 (95%CI: 0.75-0.87), respectively. Positive diagnostic likelihood ratio = 4.87 (95%CI: 3.47-6.84), negative diagnostic likelihood ratio = 0.15 (95%CI: 0.11-0.21), and diagnostic odds ratio = 32.67 (95%CI: 18.21-58.61). In addition, the area under the ROC curve was 0.92 (95%CI: 0.89-0.94), indicating that CECT has a decent discriminative ability in diagnosing ccRCC., Conclusions: CECT is recognized as a highly effective imaging tool for diagnosing ccRCC. It provides valuable guidance in the preoperative assessment and planning of surgical strategies for patients with ccRCC., (© 2024. The Author(s).)

Purpose: Inferior vena cava (IVC) involvement by renal cell carcinoma (RCC) is associated with a higher disease stage and is considered a risk factor for poor prognosis. This study aimed to investigate the role of the apparent diffusion coefficient (ADC) of MRI 3D texture analysis in the differentiation of solid and friable tumour thrombus in patients with RCC., Materials and Methods: The study involved 27 patients with RCC with tumour thrombus in the renal vein or IVC, surgically treated with nephrectomy and thrombectomy and in whom preoperatively abdominal MRI including the DWI sequence was conducted. For 3D texture analysis, the ADC map was used, and the first-order radiomic features were calculated from the whole volume of the thrombus. All tumour thrombi were histologically classified as solid or friable., Results: The solid and friable thrombus was detected in 51.9 ​% and 48.1 ​% of patients, respectively. No differences in mean values of range, 90th percentile, interquartile range, kurtosis, uniformity and variance were found between groups. Equal sensitivity and specificity (93 ​% and 69 ​%, respectively) of ADC mean, median and entropy in differentiation between solid and friable tumour thrombus, with the highest AUC for entropy (0.808), were observed. Applying the skewness threshold value of 0.09 allowed us to achieve a sensitivity of 86 ​% and a specificity of 92 ​%., Conclusions: In patients with RCC and tumour thrombus in the renal vein or IVC, the 3D texture analysis based on ADC-map allows for precise differentiation of a solid from a friable thrombus., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Medical University of Bialystok. Published by Elsevier B.V. All rights reserved.)

Anatomic imaging with contrast-enhanced computed tomography (CT) and magnetic resonance imaging (MRI) has long been the mainstay of renal mass characterization. However, those modalities are often unable to adequately characterize indeterminate, solid, enhancing renal masses - with some exceptions, such as the development of the clear-cell likelihood score on multi-parametric MRI. As such, molecular imaging approaches have gained traction as an alternative to anatomic imaging. Mitochondrial imaging with 99m Tc-sestamibi single-photon emission computed tomography/CT is a cost-effective means of non-invasively identifying oncocytomas and other indolent renal masses. On the other end of the spectrum, carbonic anhydrase IX agents, most notably the monoclonal antibody girentuximab - which can be labeled with positron emission tomography radionuclides such as zirconium-89 - are effective at identifying renal masses that are likely to be aggressive clear cell renal cell carcinomas. Renal mass biopsy, which has a relatively high non-diagnostic rate and does not definitively characterize many oncocytic neoplasms, nonetheless may play an important role in any algorithm targeted to renal mass risk stratification. The combination of molecular imaging and biopsy in selected patients with other advanced imaging methods, such as artificial intelligence/machine learning and the abstraction of radiomics features, offers the optimal way forward for maximization of the information to be gained from risk stratification of indeterminate renal masses. With the proper application of those methods, inappropriately aggressive therapy for benign and indolent renal masses may be curtailed., Competing Interests: Declaration of competing interest SPR is a consultant for, has equity in, and has received research funding from Precision Molecular, Inc. SPR and MAG are consultants for Lantheus. SPR and MAG are consultants for Telix. JDO has been a consultant for Telix., (Copyright © 2024 Société française de radiologie. Published by Elsevier Masson SAS. All rights reserved.)

Renal cell carcinoma (RCC) extremely rarely metastasizes to the testis. To date, only three reports have documented bilateral testicular metastasis of RCC. Here, we review the ultrasonographic characteristics of testicular metastasis of renal cell carcinoma cases in the literature and present a 43-year-old patient with von Hippel-Lindau (VHL) disease who was diagnosed with bilateral testicular metastasis of RCC many years after nephrectomy, with ultrasonographic-pathological correlation., (© 2024 Wiley Periodicals LLC.)

Rationale and Objectives: Radiogenomics of clear cell renal cell carcinoma (ccRCC) has been developed thanks to the availability of genomic data, both gene expressions and gene mutations, obtained through the sequencing of ccRCC genome. These data are collected in the Cancer Genome Atlas (TCGA) Research Network-work. Disintegrin and metalloproteinase domain-containing protein 12 (ADAM12) gene belongs to the family of genes coding for multidomain and multifunctional type I transmembrane proteins ADAMs. These proteins are fundamental for regulating cell adhesion and mediating proteolysis of a series of cell surface receptors and signal molecules extracellular domains. Recently, a correlation was detected between ADAM12 expression in ccRCC and tumor aggressiveness in terms of cell proliferation, migration, invasion, tumor progression, metastases, and poor prognosis, suggesting ADAM12 as a prognostic marker and therapeutic target in ccRCC. The computed tomography (CT) imaging phenotype of ADAM12 expression in ccRCC has never been studied. The aim of this study is to investigate the CT imaging phenotype of ADAM12 expression in ccRCC patients., Materials and Methods: In this retrospective study, we enrolled 202 ccRCC patients divided into two groups: ccRCC patients with ADAM12 expression (n = 35) and ccRCC patients without ADAM12 expression (n = 167). Different imaging features were evaluated on CT scan at first diagnosis. The statistical significance threshold was set at p < 0.05., Results: A statistically significant correlation was found with larger primary tumor size (p = 0.020), ill-defined tumor margins (p = 0.044), tumor necrosis (p = 0.011), and collecting system invasion (p = 0.014)., Conclusion: This study demonstrates CT imaging features associated to ADAM12 expression in ccRCC. These results could help delve into ADAM12 gene status through CT approach and to further investigate towards the development of targeted therapies in ccRCC., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Abstract: A 47-year-old man with a history of metastatic non-clear cell left renal cell carcinoma, unclassified subtype, status post left radical nephrectomy 4 years prior, and treated with immunotherapy for approximately 2½ years, presented for 18 F-FDG PET/CT exam 7 months after immunotherapy was stopped. A contrast-enhanced CT exam performed 3 weeks prior demonstrated a new small bowel intussusception in the left upper quadrant. The PET/CT demonstrated focal FDG uptake in the segment of small bowel involved in the intussusception. Pathology from small bowel resection demonstrated metastatic renal cell carcinoma., Competing Interests: Conflicts of interest and sources of funding: none declared., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Objectives: To develop radiomics-based classifiers for preoperative prediction of fibrous capsule invasion in renal cell carcinoma (RCC) patients by CT images., Methods: In this study, clear cell RCC (ccRCC) patients who underwent both preoperative abdominal contrast-enhanced CT and nephrectomy surgery at our hospital were analysed. By transfer learning, we used base model obtained from Kidney Tumour Segmentation challenge dataset to semi-automatically segment kidney and tumours from corticomedullary phase (CMP) CT images. Dice similarity coefficient (DSC) was measured to evaluate the performance of segmentation models. Ten machine learning classifiers were compared in our study. Performance of the models was assessed by their accuracy, precision, recall, and area under the receiver operating characteristic curve (AUC). The reporting and methodological quality of our study was assessed by the CLEAR checklist and METRICS score., Results: This retrospective study enrolled 163 ccRCC patients. The semiautomatic segmentation model using CMP CT images obtained DSCs of 0.98 in the training cohort and 0.96 in the test cohort for kidney segmentation, and DSCs of 0.94 and 0.86 for tumour segmentation in the training and test set, respectively. For preoperative prediction of renal capsule invasion, the AdaBoost had the best performance in batch 1, with accuracy, precision, recall, and F1-score equal to 0.8571, 0.8333, 0.9091, and 0.8696, respectively; and the same classifier was also the most suitable for this classification in batch 2. The AUCs of AdaBoost for batch 1 and batch 2 were 0.83 (95% CI: 0.68-0.98) and 0.74 (95% CI: 0.51-0.97), respectively. Nine common significant features for classification were found from 2 independent batch datasets, including morphological and texture features., Conclusions: The CT-based radiomics classifiers performed well for the preoperative prediction of fibrous capsule invasion in ccRCC., Advances in Knowledge: Noninvasive prediction of renal fibrous capsule invasion in RCC is rather difficult by abdominal CT images before surgery. A machine learning classifier integrated with radiomics features shows a promising potential to assist surgical treatment options for RCC patients., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Institute of Radiology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Clear cell papillary renal cell carcinoma (CCPRCC) is a newly classified renal cell carcinoma with a low degree of malignancy. Its imaging features have not been studied deeply. Therefore, we reviewed the imaging features of CCPRCC. Solid CCPRCC shows high echo or isoecho mass on conventional ultrasound. Contrast enhanced ultrasound shows "fast forward and slow backward, uneven high enhancement". Computed tomography shows high enhancement and maximum enhancement in the cortical-medullary phase. Magnetic resonance imaging shows slightly low T1WI and high T2WI. This article aims to improve the understanding of CCPRCC by clinical radiologists and promote the accurate., (© 2024 Wiley Periodicals LLC.)

Rationale and Objectives: To establish a computed tomography (CT)-based radiomics model to predict Fork head box M1(FOXM1) expression levels and develop a combined model for prognostic prediction in patients with clear cell renal cell carcinoma (ccRCC)., Materials and Methods: A total of 529 patients were utilized to assess the prognostic significance of FOXM1 expression and were subsequently categorized into low and high FOXM1 expression groups. 184 patients with CT images were randomly divided into training and validation cohorts. Radiomics signature (Rad-score) for predicting FOXM1 expression level was developed in the training cohort. The predictive performance was evaluated using receiver operating characteristic (ROC) curves. A clinical model based on clinical factors and a combined model incorporating clinical factors and Rad-score were developed to predict ccRCC prognosis using Cox regression analyses. The concordance index(C-index) was employed to assess and compare the predictive capabilities of the Rad-score, TNM stage, clinical model, and combined model. The likelihood ratio test was used to compare the models' performance., Results: The Rad-score demonstrated high predictive accuracy for high FOXM1 expression with areas under the ROC curves of 0.713 and 0.711 in the training and validation cohorts. In the training cohort, the C-indexes for the Rad-score, TNM Stage, clinical model, and combined model were 0.657, 0.711, 0.737, and 0.741, respectively. Correspondingly, in the validation cohort, the C-indexes were 0.670, 0.712, 0.736, and 0.745. The combined model had the highest C-index, significantly outperforming the other models., Conclusion: The Rad-score accurately predicts FOXM1 expression levels and is an independent prognostic factor for ccRCC., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Purpose: To differentiate mixed epithelial and stromal tumor family (MESTF) of the kidney from predominantly cystic renal cell carcinoma (RCC) using the magnetic resonance imaging (MRI)-based Bosniak classification system version 2019 (v2019)., Materials and Methods: The study included 36 consecutive patients with MESTF and 77 with predominantly cystic RCC who underwent preoperative renal MRI. One radiologist evaluated and documented the clinical and MRI characteristics (age, sex, laterality, R.E.N.A.L. Nephrometry Score [RNS], surgical approach, the signal intensity on T 2 -weighted imaging, restricted diffusion and enhancement features in corticomedullary phase). Blinded to clinical and pathological information, another two radiologists independently evaluated Bosniak category of all masses. Interobserver agreement based on Bosniak classification system v2019 was measured by the weighted Cohen/Conger's Kappa coefficient. Furthermore, predominantly cystic RCCs and MESTFs were divided into low (categories I, II, and IIF) and high-class (categories III, and IV) tumors. The independent sample t test (Mann-Whitney U test) or Pearson Chi-square test (Fisher's exact probability test) was utilized to compare clinical and imaging characteristics between MESTFs and predominantly cystic RCCs. The performance of the Bosniak classification system v2019 in distinguishing MESTF from predominantly cystic RCC was investigated via receiver operating characteristic curve analysis., Results: MESTF and predominantly cystic RCC groups significantly differed in terms of age, lesion size, RNS, restricted diffusion, and obvious enhancement in corticomedullary phase, but not sex, laterality, surgical approach, and the signal intensity on T 2 WI. Interobserver agreement was substantially based on the Bosniak classification system v2019. There were 24 low-class tumors and 12 high-class tumors in the MESTF group. Meanwhile, 13 low-class tumors and 64 high-class tumors were observed in the predominantly cystic RCC group. The distribution of low- or high-class tumors significantly differed between the MESTF and predominantly cystic RCC groups. Bosniak classification system v2019 had excellent discrimination (cutoff value = category III), and an area under curve value was 0.81; accuracy, 80.5%; sensitivity, 87.0%; and specificity, 66.7%., Conclusion: The MRI-based Bosniak classification system v2019 can effectively distinguish MESTF from predominantly cystic RCC if category III was used as a cutoff reference., (© 2024. The Author(s) under exclusive licence to Japan Radiological Society.)

Background: Pathology grading is an essential step for the treatment and evaluation of the prognosis in patients with clear cell renal cell carcinoma (ccRCC)., Purpose: To investigate the utility of texture analysis in evaluating Fuhrman grades of renal tumors in patients with Von Hippel-Lindau (VHL)-associated ccRCC, aiming to improve non-invasive diagnosis and personalized treatment., Study Type: Retrospective analysis of a prospectively maintained cohort., Population: One hundred and thirty-six patients, 84 (61%) males and 52 (39%) females with pathology-proven ccRCC with a mean age of 52.8 ± 12.7 from 2010 to 2023., Field Strength and Sequences: 1.5 and 3 T MRIs. Segmentations were performed on the T1-weighted 3-minute delayed sequence and then registered on pre-contrast, T1-weighted arterial and venous sequences., Assessment: A total of 404 lesions, 345 low-grade tumors, and 59 high-grade tumors were segmented using ITK-SNAP on a T1-weighted 3-minute delayed sequence of MRI. Radiomics features were extracted from pre-contrast, T1-weighted arterial, venous, and delayed post-contrast sequences. Preprocessing techniques were employed to address class imbalances. Features were then rescaled to normalize the numeric values. We developed a stacked model combining random forest and XGBoost to assess tumor grades using radiomics signatures., Statistical Tests: The model's performance was evaluated using positive predictive value (PPV), sensitivity, F1 score, area under the curve of receiver operating characteristic curve, and Matthews correlation coefficient. Using Monte Carlo technique, the average performance of 100 benchmarks of 85% train and 15% test was reported., Results: The best model displayed an accuracy of 0.79. For low-grade tumor detection, a sensitivity of 0.79, a PPV of 0.95, and an F1 score of 0.86 were obtained. For high-grade tumor detection, a sensitivity of 0.78, PPV of 0.39, and F1 score of 0.52 were reported., Data Conclusion: Radiomics analysis shows promise in classifying pathology grades non-invasively for patients with VHL-associated ccRCC, potentially leading to better diagnosis and personalized treatment., Level of Evidence: 1 TECHNICAL EFFICACY: Stage 2., (Published 2024. This article is a U.S. Government work and is in the public domain in the USA.)

Renal cell carcinoma (RCC) is a growing problem in global oncology, with a steadily increasing incidence, especially in developed regions. Its different histologic subtypes present different challenges in diagnosis and management. Advanced imaging techniques have a crucial role in distinguishing between these subtypes by highlighting unique radiographic features such as exophytic growth patterns, cystic components, and enhancement patterns. Practical suggestions discussed in this review include using chemical shift MRI to differentiate fat-poor angiomyolipomas from RCC, recognizing specific imaging markers such as pseudo-capsule in papillary RCC, and understanding the implications of negative pixel count in computed tomography scans., Competing Interests: Disclosure None., (Published by Elsevier Inc.)

Objective: To explore the differences between SonoVue and Sonazoid contrast-enhanced ultrasound (CEUS) in evaluating enhancement features of renal masses and determine the diagnostic value of CEUS in clear cell renal cell carcinoma (ccRCC)., Methods: A total of 57 eligible patients were enrolled and divided into the ccRCC, papillary renal cell carcinoma (pRCC), non-ccRCC and non-pRCC malignancy groups, and benign mass groups based on their postsurgical histopathologic diagnosis. The enhancement features of renal masses following SonoVue and Sonazoid CEUS in each group were analyzed. Diagnostic efficiencies of SonoVue and Sonazoid CEUS for ccRCC and non-ccRCC were determined., Results: There were no significant differences in the enhancement features of renal masses with SonoVue and Sonazoid imaging in the four groups (p > .05). Both SonoVue CEUS and Sonazoid CEUS showed good diagnostic performance for the differential diagnosis of ccRCC and non-ccRCC, with sensitivities of 88.6% and 85.7%, specificities of 76.5% and 88.2%, accuracies of 84.6% and 86.5%, positive predictive values of 88.6% and 93.8%, and negative predictive values of 76.5% and 75%, respectively. There were no statistically significant differences in any of the diagnostic performance indices between the two methods (p > .05)., Conclusion: The CEUS features of SonoVue and Sonazoid in evaluating renal masses were similar in the vascular phase. Both SonoVue and Sonazoid CEUS showed good diagnostic performance for the differential diagnosis of ccRCC and non-ccRCC., Competing Interests: Conflict of interest The authors declare no competing interests., (Copyright © 2024 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.)

Abstract: The development of late metastatic recurrence after nephrectomy is a well-known characteristic of renal cell carcinoma (RCC). Correct diagnosis is often challenging due to the long time between primary tumor excision and the development of metastases. 68 Ga-DOTATATE PET/CT has the potential to be employed in the follow-up of RCC, because RCC expresses somatostatin receptors. However, limited cases have been reported demonstrating 68 Ga-DOTATATE uptake in the metastases of RCC. Herein, we describe a unique case of RCC with multiple atypical metastatic sites, emphasizing the usefulness of 68 Ga-DOTATATE PET/CT in the evaluation and follow-up of patients with RCC., Competing Interests: Conflicts of interest and sources of funding: none declared., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Objective: Accurate assessment of Fuhrman grade is crucial for optimal clinical management and personalized treatment strategies in patients with clear cell renal cell carcinoma (CCRCC). In this study, we developed a predictive model using ultrasound (US) images to accurately predict the Fuhrman grade., Methods: Between March 2013 and July 2023, a retrospective analysis was conducted on the US imaging and clinical data of 235 patients with pathologically confirmed CCRCC, including 67 with Fuhrman grades Ⅲ and Ⅳ. This study included 201 patients from Hospital A who were divided into training set (n = 161) and an internal validation set (n = 40) in an 8:2 ratio. Additionally, 34 patients from Hospital B were included for external validation. US images were delineated using ITK software, and radiomics features were extracted using PyRadiomics software. Subsequently, separate models for clinical factors, radiomics features, and their combinations were constructed. The model's performance was assessed by calculating the area under the receiver operating characteristic curve (AUC), calibration curve and decision curve analysis (DCA)., Results: In total, 235 patients diagnosed with CCRCC, comprising 168 low-grade and 67 high-grade tumors, were included in this study. A comparison of the predictive performances of different models revealed that the logistic regression model exhibited relatively good stability and robustness. The AUC of the combined model for the training, internal validation and external validation sets were 0.871, 0.785 and 0.826, respectively, which were higher than those of the clinical and imaging histology models. Furthermore, the calibration curve demonstrated excellent concordance between the predicted Fuhrman grade probability of CCRCC using the combined model and the observed values in both the training and validation sets. Additionally, within the threshold range of 0-0.93, the combined model demonstrated substantial clinical utility, as evidenced by DCA., Conclusion: The application of US radiomics techniques enabled objective prediction of Fuhrman grading in patients with CCRCC. Nevertheless, certain clinical indicators remain indispensable, underscoring the pressing need for their integrated use in clinical practice., Advances in Knowledge: Previous studies have predominantly focused on using computed tomography or magnetic resonance imaging modalities to predict the Fuhrman grade of CCRCC. Our findings demonstrate that a prediction model based on US images is more cost-effective, easily accessible and exhibits commendable performance. Consequently, this study offers a promising approach to maximizing the use of US examinations in future research., Competing Interests: Conflict of interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article., (Copyright © 2024 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.)

The detection of solid renal masses has increased over time due to incidental findings during imaging studies conducted for unrelated medical conditions. Approximately 20% of lesions measuring less than 4 cm are benign and 80% are malignant. Clear cell renal cell carcinoma (ccRCC) is the most frequent among renal carcinomas, responsible for 65-80% of cases. The increased detection of renal masses facilitates early diagnosis and treatment. However, it also leads to more invasive interventions, which result in higher morbidity and costs. Currently, only histological analysis can offer an accurate diagnosis. Surgical nephron loss significantly elevates morbidity and mortality rates. Active surveillance represents a conservative management approach for patients diagnosed with a solid renal mass that is endorsed by both American Urological Association and the European Society for Medical Oncology. However, active surveillance is used in a minority of patients and varies across institutions. The lack of clinical studies using a standardized approach to incidentally detected small renal masses precludes the widespread use of active surveillance. Hence, there is an urgent need for better patient selection, distinguishing those who require surgery from those suitable for active surveillance. The clear cell likelihood score (ccLS) represents a novel MRI tool for assessing the probability of a renal mass being a ccRCC. In this study, we present a comprehensive review of renal masses and their evaluation using the ccLS to facilitate shared decision between urologists and patients., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)