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Accurate prediction of bone metastasis-free survival (BMFS) after complete surgical resection in patients with non-small cell lung cancer (NSCLC) may facilitate appropriate follow-up planning. The aim of this study was to establish and validate a preoperative CT-based deep learning (DL) signature to predict BMFS in NSCLC patients. We performed a retrospective analysis of 1547 NSCLC patients who underwent complete surgical resection, followed by at least 36 months of monitoring at two hospitals. We constructed a DL signature from multiparametric CT images using 3D convolutional neural networks, and we integrated this signature with clinical-imaging factors to establish a deep learning clinical-imaging signature (DLCS). We evaluated performance using Harrell's concordance index (C-index) and the time-dependent receiver operating characteristic. We also assessed the risk of bone metastasis (BM) in NSCLC patients at different clinical stages using DLCS. The DL signature successfully predicted BM, with C-indexes of 0.799 and 0.818 for the validation cohorts. DLCS outperformed the DL signature with corresponding C-indexes of 0.806 and 0.834. Ranges for area under the curve at 1, 2, and 3 years were 0.820–0.865 for internal and 0.860–0.884 for external validation cohorts. Furthermore, DLCS successfully stratified patients with different clinical stages of NSCLC as high- and low-risk groups for BM (p < 0.05). CT-based DL can predict BMFS in NSCLC patients undergoing complete surgical resection, and may assist in the assessment of BM risk for patients at different clinical stages. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the efficacy of the CT-based intratumoral, peritumoral, and combined radiomics signatures in predicting progression-free survival (PFS) of patients with chondrosarcoma (CS). Methods: In this study, patients diagnosed with CS between January 2009 and January 2022 were retrospectively screened, and 214 patients with CS from two centers were respectively enrolled into the training cohorts (institution 1, n = 113) and test cohorts (institution 2, n = 101). The intratumoral and peritumoral radiomics features were extracted from CT images. The intratumoral, peritumoral, and combined radiomics signatures were constructed respectively, and their radiomics scores (Rad-score) were calculated. The performance of intratumoral, peritumoral, and combined radiomics signatures in PFS prediction in patients with CS was evaluated by C-index, time-dependent area under the receiver operating characteristics curve (time-AUC), and time-dependent C-index (time C-index). Results: Eleven, 7, and 16 features were used to construct the intratumoral, peritumoral, and combined radiomics signatures, respectively. The combined radiomics signature showed the best prediction ability in the training cohort (C-index, 0.835; 95%; confidence interval [CI], 0.764–0.905) and the test cohort (C-index, 0.800; 95% CI, 0.681–0.920). Time-AUC and time C-index showed that the combined signature outperformed the intratumoral and peritumoral radiomics signatures in the prediction of PFS. Conclusion: The CT-based combined signature incorporating intratumoral and peritumoral radiomics features can predict PFS in patients with CS, which might assist clinicians in selecting individualized surveillance and treatment plans for CS patients. Critical relevance statement: Develop and validate CT-based intratumoral, peritumoral, and combined radiomics signatures to evaluate the efficacy in predicting prognosis of patients with CS. Key points: • Reliable prognostic models for preoperative chondrosarcoma are lacking. • Combined radiomics signature incorporating intratumoral and peritumoral features can predict progression-free survival in patients with chondrosarcoma. • Combined radiomics signature may facilitate individualized stratification and management of patients with chondrosarcoma. [ABSTRACT FROM AUTHOR]

Objectives: To explore the potential of radiomics features to predict the histologic grade of nonfunctioning pancreatic neuroendocrine tumor (NF-PNET) patients using non-contrast sequence based on MRI. Methods: Two hundred twenty-eight patients with NF-PNETs undergoing MRI at 5 centers were retrospectively analyzed. Data from center 1 (n = 115) constituted the training cohort, and data from centers 2–5 (n = 113) constituted the testing cohort. Radiomics features were extracted from T2-weighted images and the apparent diffusion coefficient. The least absolute shrinkage and selection operator was applied to select the most important features and to develop radiomics signatures. The area under receiver operating characteristic curve (AUC) was performed to assess models. Results: Tumor boundary, enhancement homogeneity, and vascular invasion were used to construct the radiological model to stratify NF-PNET patients into grade 1 and 2/3 groups, which yielded AUC of 0.884 and 0.684 in the training and testing groups. A radiomics model including 4 features was constructed, with an AUC of 0.941 and 0.871 in the training and testing cohorts. The fusion model combining the radiomics signature and radiological characteristics showed good performance in the training set (AUC = 0.956) and in the testing set (AUC = 0.864), respectively. Conclusion: The developed model that integrates radiomics features with radiological characteristics could be used as a non-invasive, dependable, and accurate tool for the preoperative prediction of grade in NF-PNETs. Clinical relevance statement: Our study revealed that the fusion model based on a non-contrast MR sequence can be used to predict the histologic grade before operation. The radiomics model may be a new and effective biological marker in NF-PNETs. Key Points: The diagnostic performance of the radiomics model and fusion model was better than that of the model based on clinical information and radiological features in predicting grade 1 and 2/3 of nonfunctioning pancreatic neuroendocrine tumors (NF-PNETs). Good performance of the model in the four external testing cohorts indicated that the radiomics model and fusion model for predicting the grades of NF-PNETs were robust and reliable, indicating the two models could be used in the clinical setting and facilitate the surgeons' decision on risk stratification. The radiomics features were selected from non-contrast T2-weighted images (T2WI) and diffusion-weighted imaging (DWI) sequence, which means that the administration of contrast agent was not needed in grading the NF-PNETs. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Objectives: The aim of the study was to evaluate the association between the radiomics-based intratumoral heterogeneity (ITH) and the recurrence risk in hepatocellular carcinoma (HCC) patients after liver transplantation (LT), and to assess its incremental to the Milan, University of California San Francisco (UCSF), Metro-Ticket 2.0, and Hangzhou criteria. Methods: A multicenter cohort of 196 HCC patients were investigated. The endpoint was recurrence-free survival (RFS) after LT. A CT-based radiomics signature (RS) was constructed and assessed in the whole cohort and in the subgroups stratified by the Milan, UCSF, Metro-Ticket 2.0, and Hangzhou criteria. The R-Milan, R-UCSF, R-Metro-Ticket 2.0, and R-Hangzhou nomograms which combined RS and the four existing risk criteria were developed respectively. The incremental value of RS to the four existing risk criteria in RFS prediction was evaluated. Results: RS was significantly associated with RFS in the training and test cohorts as well as in the subgroups stratified by the existing risk criteria. The four combined nomograms showed better predictive capability than the existing risk criteria did with higher C-indices (R-Milan [training/test] vs. Milan, 0.745/0.765 vs. 0.677; R-USCF vs. USCF, 0.748/0.767 vs. 0.675; R-Metro-Ticket 2.0 vs. Metro-Ticket 2.0, 0.756/0.783 vs. 0.670; R-Hangzhou vs. Hangzhou, 0.751/0.760 vs. 0.691) and higher clinical net benefit. Conclusions: The radiomics-based ITH can predict outcomes and provide incremental value to the existing risk criteria in HCC patients after LT. Incorporating radiomics-based ITH in HCC risk criteria may facilitate candidate selection, surveillance, and adjuvant trial design. Key Points: • Milan, USCF, Metro-Ticket 2.0, and Hangzhou criteria may be insufficient for outcome prediction in HCC after LT. • Radiomics allows for the characterization of tumor heterogeneity. • Radiomics adds incremental value to the existing criteria in outcome prediction. [ABSTRACT FROM AUTHOR]

Purpose: Tumor heterogeneity, which is associated with poor outcomes, has not been exhibited in the University of California, Los Angeles, Integrated Staging System (UISS), and the Stage, Size, Grade and Necrosis (SSIGN) scores. Radiomics allows an in-depth characterization of heterogeneity across the tumor, but its incremental value to the existing prognostic models for clear cell renal cell carcinoma (ccRCC) outcome is unknown. The purpose of this study was to evaluate the association between the radiomics-based tumor heterogeneity and postoperative risk of recurrence in localized ccRCC, and to assess its incremental value to UISS and SSIGN. Methods: A multicenter 866 ccRCC patients derived from 12 Chinese hospitals were studied. The endpoint was recurrence-free survival (RFS). A CT-based radiomics signature (RS) was developed and assessed in the whole cohort and in the subgroups stratified by UISS and SSIGN. Two combined nomograms, the R-UISS (combining RS and UISS) and R-SSIGN (combining RS and SSIGN), were developed. The incremental value of RS to UISS and SSIGN in RFS prediction was evaluated. R statistical software was used for statistics. Results: Patients with low radiomics scores were 4.44 times more likely to experience recurrence than those with high radiomics scores (P<0.001). Stratified analysis suggested the association is significant among low- and intermediate-risk patients identified by UISS and SSIGN. The R-UISS and R-SSIGN showed better predictive capability than UISS and SSIGN did with higher C-indices (R-UISS vs. UISS, 0.74 vs. 0.64; R-SSIGN vs. SSIGN, 0.78 vs. 0.76) and higher clinical net benefit. Conclusions: The radiomics-based tumor heterogeneity can predict outcome and add incremental value to the existing prognostic models in localized ccRCC patients. Incorporating radiomics-based tumor heterogeneity in ccRCC prognostic models may provide the opportunity to better surveillance and adjuvant clinical trial design. [ABSTRACT FROM AUTHOR]

Purpose: Renal oncocytoma (RO) is the most commonly resected benign renal tumor because of misdiagnosis as renal cell carcinoma. This misdiagnosis is generally owing to overlapping imaging features. This study describes the building of a radiomics nomogram based on clinical data and radiomics signature for the preoperative differentiation of RO from clear cell renal cell carcinoma (ccRCC) on tri-phasic contrast-enhanced CT. Methods: A total of 122 patients (85 in training set and 37 in external validation set) with ROs (n = 46) or ccRCCs (n = 76) were enrolled. Patient characteristics and tri-phasic contrast-enhanced CT imaging features were evaluated to build a clinical factors model. A radiomics signature was constructed by extracting radiomics features from tri-phasic contrast-enhanced CT images and a radiomics score (Rad-score) was calculated. A radiomics nomogram was then built by incorporating the Rad-score and significant clinical factors according to a multivariate logistic regression analysis. The diagnostic performance of the above three models was evaluated in training and validation sets. Results: Central stellate area and perirenal fascia thickening were selected to build the clinical factors model. Eleven radiomics features were combined to construct the radiomics signature. The AUCs of the radiomics nomogram, which was based on the selected clinical factors and Rad-score, were 0.960 and 0.898 in the training and validation sets, respectively. The decision curves of the radiomics nomogram and radiomics signature in the validation set indicated an overall net benefit over the clinical factors model. Conclusion: Our radiomics nomogram can effectively predict the preoperative diagnosis of ROs and may therefore be of assistance in sparing unnecessary surgery and tailoring precise therapy. The ROC curves of the clinical model, the radiomics signature and the radiomics nomogram for the validation set. RO = Renal oncocytoma; ccRCC = Clear cell renal cell carcinoma. [ABSTRACT FROM AUTHOR]

Purpose: To construct an FDG PET/CT metabolic parameter-based model to predict early recurrence of hepatocellular carcinoma (HCC) after liver transplantation (LT). Methods: A total of 62 patients with HCC after LT were enrolled with a follow-up period of 1 year. Basic clinical, pathology, and laboratory data, CT features (CPLC), and PET metabolic parameters (CPLCP) were collected for model construction. A CPLC nomogram without metabolic parameters and a CPLCP nomogram with metabolic parameters were established. The net reclassification index (NRI) and integrated discrimination improvement (IDI) of the two models were calculated. The constructed model was compared with Milan criteria and University of California San Francisco (UCSF) criteria. The time-dependent area under the receiver operating characteristic curve (time-AUC) was used to compare the efficiency of the models, and the bootstrap method was used to for verification. Harrell's concordance index (C-index) was used to evaluate the performance of these models. Decision curve analysis (DCA) was used to evaluate the clinical practicability of each model. Results: Thirty out of 62 patients experienced a recurrence during the 1-year follow-up. BCLC stage (P = 0.009), MVI (P = 0.032), AFP (P = 0.004), CTdmax (P = 0.033), and MTV (P = 0.039) were the independent predictors. The CPLC nomogram and the CPLCP nomogram were established. Compared with the CPLC nomogram, the NRI of the CPLCP nomogram increased by 38.98% (95% CI = −18.77–60.43%) and the IDI increased by 4.40% (95% CI = −1.00–16.62%). The AUC value of the CPLCP nomogram was higher than those of Milan criteria and UCSF criteria in the time-AUC curve. Moreover, the CPLCP nomogram had a higher C-index (0.774) than other models. Finally, the DCA curve showed that clinical practicability of the CPLCP nomogram outperformed the Milan criteria and UCSF criteria. Conclusions: The CPLCP nomogram combining basic clinical data, pathology data, laboratory data, CT features, and PET metabolic parameters showed good efficacy and high clinical practicability in predicting the early recurrence of HCC after LT. [ABSTRACT FROM AUTHOR]

Purpose: Lymphovascular invasion (LVI) impairs surgical outcomes in lung adenocarcinoma (LAC) patients. Preoperative prediction of LVI is challenging by using traditional clinical and imaging parameters. The purpose of this study was to investigate the value of the radiomics nomogram integrating clinical factors, CT features, and maximum standardized uptake value (SUVmax) to predict LVI and outcome in LAC and to evaluate the additional value of the SUVmax to the PET/CT-based radiomics nomogram. Methods: A total of 272 LAC patients (87 LVI-present LACs and 185 LVI-absent LACs) with PET/CT scans were retrospectively enrolled, and 160 patients with SUVmax ≥ 2.5 of them were used for PET radiomics analysis. Clinical data and CT features were analyzed to select independent LVI predictors. The performance of the independent LVI predictors and SUVmax was evaluated. Two-dimensional (2D) and three-dimensional (3D) CT radiomics signatures (RSs) and PET-RS were constructed with the least absolute shrinkage and selection operator algorithm and radiomics scores (Rad-scores) were calculated. The radiomics nomograms, incorporating Rad-score and independent clinical and CT factors, with SUVmax (RNWS) or without SUVmax (RNWOS) were built. The performance of the models was assessed with respect to calibration, discrimination, and clinical usefulness. All the clinical, PET/CT, pathologic, therapeutic, and radiomics parameters were assessed to identify independent predictors of progression-free survival (PFS). Results: CT morphology was the independent LVI predictor. SUVmax provided better discrimination capability compared with CT morphology in the training set (P < 0.001) and test set (P = 0.042). A total of 1409 CT and PET radiomics features were extracted and reduced to 8, 8, and 10 features to build the 2D CT-RS, 3D CT-RS, and the PET-RS, respectively. There was no significant difference in AUC between the 2D-RS and 3D-RS (P > 0.05), and 2D CT-RS showed a relatively higher AUC than 3D CT-RS. The CT-RS, the CT-RNWOS, and the CT-RNWS showed good discrimination in the training set (AUC [area under the curve], 0.799, 0.796, and 0.851, respectively) and the test set (AUC, 0.818, 0.822, and 0.838, respectively). There was significant difference in AUC between the CT-RNWS and CT-RNWOS (P = 0.044) in the training set. Decision curve analysis (DCA) demonstrated the CT-RNWS outperformed the CT-RS and the CT-RNWOS in terms of clinical usefulness. Furthermore, DCA showed the PETCT-RNWS provided the highest net benefit compared with the PET-RNWS and CT-RNWS. PFS was significantly different between the pathologic and RNWS-predicted LVI-present and LVI-absent patients (P < 0.001). Carbohydrate antigen 125 (CA125), carcinoembryonic antigen (CEA), neuron-specific enolase (NSE), pathologic LVI, histologic subtype, and SUVmax were independent predictors of PFS in the 244 CT-RNWS-predicted cohort; and CA125, NSE, pathologic LVI, and SUVmax were the independent predictors of PFS in the 141 PETCT-RNWS-predicted cohort. Conclusions: The radiomics nomogram, incorporating Rad-score, clinical and PET/CT parameters, shows favorable predictive efficacy for LVI status in LAC. Pathologic LVI and SUVmax are associated with LAC prognosis. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Figures 1 and 4 are incorrect in the original manuscript. [ABSTRACT FROM AUTHOR]

So far, there is no satisfactory imaging modality to monitor antiangiogenesis therapy of ovarian cancer noninvasively. The aim of this study was to evaluate the effectiveness and sensibility of an F labeled Arg-Gly-Asp (RGD) peptide in imaging and monitoring antiangiogenic responds in SKOV-3 xenograft-bearing mice. F-FB-NH-PEG-E[PEG-c(RGDfK)] (denoted as F-RGD) was synthesized and employed in this study. Mice bearing ovarian cancer SKOV-3 tumors were used for biodistribution and microPET imaging studies compared with F-FDG imaging. Animals were treated with low-dose paclitaxel and the effect of paclitaxel therapy on F-RGD accumulation was investigated. Microvascular density (MVD) of SKOV-3 tumors was detected to assess the reliability of F-RGD in antiangiogenesis monitoring. Biodistribution studies for F-RGD revealed favorable in vivo pharmacokinetic properties, with significant levels of receptor-specific tumor uptake determined via blocking studies. MicroPET imaging results demonstrated high contrast visualization of SKOV-3 tumors. And tumor to background ratio (T/NT) of F-RGD uptake was significantly higher than that of F-FDG. Studies on antiangiogenic therapy demonstrated percentage of injected dose per gram of tissue (%ID/g) tumor uptake of F-RGD which was obviously decreased in the treatment group than the control group, especially at 60 min (by 31.31 ± 7.18 %, P = 0.009) and 120 min (by 38.92 ± 8.31 %, P < 0.001) after injection of F-RGD. MVD measurement of SKOV-3 tumors confirmed the finding of the biodistribution studies in monitoring antiangiogenesis therapy. F-RGD, with favorable biodistribution properties and specific affinity, is a promising tracer for tumor imaging and monitoring antiangiogenesis therapy in ovarian cancer SKOV-3 xenograft-bearing mice. [ABSTRACT FROM AUTHOR]

Objectives: To compare accuracy, image quality and radiation dose between high-pitch spiral and sequential modes on 128-slice dual-source computed tomographic (DSCT) angiography in children with congenital heart disease (CHD).Methods: Forty patients suspected with CHD underwent 128-slice DSCT angiography with high-pitch mode and sequential mode respectively. All the anomalies were confirmed by the surgical and/or the conventional cardiac angiography (CCA) findings. The diagnostic accuracy, the subjective and objective image quality and effective radiation doses were compared.Results: There was no significant difference in diagnostic accuracy (χ ( 2 ) = 0.963, P > 0.05), the objective parameters for image quality (P > 0.05) and the image quality of great vessels (u = 167.500, P > 0.05) between the two groups. The image quality of intracardiac structures and coronary arteries was significantly better in the sequential mode group than that in the high-pitch group (u = 112.500 and 100.000, P < 0.05). The mean effective dose in high-pitch group (0.17 ± 0.05 mSv) was significantly lower (t = 5.287, P < 0.05) than that in the sequential mode group (0.29 ± 0.09 mSv).Conclusions: Both the high-pitch and the sequential modes for 128-slice DSCT angiography provide high accuracy for the assessment of CHD in children, while the high-pitch mode, even with some image quality decrease, further significantly lowers the radiation dose.Key Points: • Modern CT provides excellent anatomical detail of congenital heart disease. • Dual source CT systems offer high-pitch spiral and sequential modes. • The high-pitch mode provides high accuracy for the assessment of CHD. • A few images using the high-pitch mode were occasionally slightly degraded. • But the high-pitch mode significantly lowers the radiation dose. [ABSTRACT FROM AUTHOR]

The article presents a case study of a 58-year-old man who was admitted into hospital as he was suffering from fever and jaundice and diagnosis needed for hepatic lymphoma were showed by images of Positron emission tomography-computed tomography (PET\CT).

Objectives: To develop and validate a radiomics nomogram combining radiomics features and clinical factors for preoperative evaluation of Ki-67 expression status and prognostic prediction in clear cell renal cell carcinoma (ccRCC)., Methods: Two medical centers of 185 ccRCC patients were included, and each of them formed a training group (n = 130) and a validation group (n = 55). The independent predictor of Ki-67 expression status was identified by univariate and multivariate regression, and radiomics features were extracted from the preoperative CT images. The maximum relevance minimum redundancy (mRMR) and the least absolute shrinkage and selection operator algorithm (LASSO) were used to identify the radiomics features that were most relevant for high Ki-67 expression. Subsequently, clinical model, radiomics signature (RS), and radiomics nomogram were established. The performance for prediction of Ki-67 expression status was validated using area under curve (AUC), calibration curve, Delong test, decision curve analysis (DCA). Prognostic prediction was assessed by survival curve and concordance index (C-index)., Results: Tumour size was the only independent predictor of Ki-67 expression status. Five radiomics features were finally identified to construct the RS (AUC: training group, 0.821; validation group, 0.799). The radiomics nomogram achieved a higher AUC (training group, 0.841; validation group, 0.814) and clinical net benefit. Besides, the radiomics nomogram provided a highest C-index (training group, 0.841; validation group, 0.820) in predicting prognosis for ccRCC patients., Conclusions: The radiomics nomogram can accurately predict the Ki-67 expression status and exhibit a great capacity for prognostic prediction in patients with ccRCC and may provide value for tailoring personalized treatment strategies and facilitating comprehensive clinical monitoring for ccRCC patients., (© 2024. The Author(s).)

Objective: The preoperative identification of tumor grade in chondrosarcoma (CS) is crucial for devising effective treatment strategies and predicting outcomes. The study aims to build and validate a CT-based radiomics nomogram (RN) for the preoperative identification of tumor grade in CS, and to evaluate the correlation between the RN-predicted tumor grade and postoperative outcome., Methods: A total of 196 patients (139 in the training cohort and 57 in the external validation cohort) were derived from three different centers. A clinical model, radiomics signature (RS) and RN (which combines significant clinical factors and RS) were developed and validated to assess their ability to distinguish low-grade from high-grade CS with area under the curve (AUC). Additionally, Kaplan-Meier survival analysis was applied to examine the association between RN-predicted tumor grade and recurrence-free survival (RFS) of CS. The predictive accuracy of the RN was evaluated using Harrell's concordance index (C-index), hazard ratio (HR) and AUC., Results: Size, endosteal scalloping and active periostitis were selected to build the clinical model. Three radiomics features, based on CT images, were selected to construct the RS. Both the RN (AUC, 0.842) and RS (AUC, 0.835) were superior to the clinical model (AUC, 0.776) in the validation set (P = 0.003, 0.040, respectively). A correlation between Nomogram score (Nomo-score, derived from RN) and RFS was observed through Kaplan-Meier survival analysis in the training and test cohorts (log-rank P < 0.050). Patients with high Nomo-score tumors were 2.669 times more likely to suffer recurrence than those with low Nomo-score tumors (HR, 2.669, P < 0.001)., Conclusions: The CT-based RN performed well in predicting both the histologic grade and outcome of CS., (© 2024. The Author(s).)

Background: To construct and assess a computed tomography (CT)-based deep learning radiomics nomogram (DLRN) for predicting the pathological grade of bladder cancer (BCa) preoperatively., Methods: We retrospectively enrolled 688 patients with BCa (469 in the training cohort, 219 in the external test cohort) who underwent surgical resection. We extracted handcrafted radiomics (HCR) features and deep learning (DL) features from three-phase CT images (including corticomedullary-phase [C-phase], nephrographic-phase [N-phase] and excretory-phase [E-phase]). We constructed predictive models using 11 machine learning classifiers, and we developed a DLRN by combining the radiomic signature with clinical factors. We assessed performance and clinical utility of the models with reference to the area under the curve (AUC), calibration curve, and decision curve analysis (DCA)., Results: The support vector machine (SVM) classifier model based on HCR and DL combined features was the best radiomic signature, with AUC values of 0.953 and 0.943 in the training cohort and the external test cohort, respectively. The AUC values of the clinical model in the training cohort and the external test cohort were 0.752 and 0.745, respectively. DLRN performed well on both data cohorts (training cohort: AUC = 0.961; external test cohort: AUC = 0.947), and outperformed the clinical model and the optimal radiomic signature., Conclusion: The proposed CT-based DLRN showed good diagnostic capability in distinguishing between high and low grade BCa., (© 2023. International Cancer Imaging Society (ICIS).)

Background: The purpose of this study was to develop and validate a radiomics nomogram for preoperative differentiating focal nodular hyperplasia (FNH) from hepatocellular carcinoma (HCC) in the non-cirrhotic liver., Methods: A total of 156 patients with FNH (n = 55) and HCC (n = 101) were divided into a training set (n = 119) and a validation set (n = 37). Radiomics features were extracted from triphasic contrast CT images. A radiomics signature was constructed with the least absolute shrinkage and selection operator algorithm, and a radiomics score (Rad-score) was calculated. Clinical data 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 by multivariate logistic regression analysis. Nomogram performance was assessed with respect to discrimination and clinical usefulness., Results: Four thousand two hundred twenty-seven features were extracted and reduced to 10 features as the most important discriminators to build the radiomics signature. The radiomics signature showed good discrimination in the training set (AUC [area under the curve], 0.964; 95% confidence interval [CI], 0.934-0.995) and the validation set (AUC, 0.865; 95% CI, 0.725-1.000). Age, Hepatitis B virus infection, and enhancement pattern were the independent clinical factors. The radiomics nomogram, which incorporated the Rad-score and clinical factors, showed good discrimination in the training set (AUC, 0.979; 95% CI, 0.959-0.998) and the validation set (AUC, 0.917; 95% CI, 0.800-1.000), and showed better discrimination capability (P < 0.001) compared with the clinical factors model (AUC, 0.799; 95% CI, 0.719-0.879) in the training set. Decision curve analysis showed the nomogram outperformed the clinical factors model 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 FNH from HCC in the non-cirrhotic liver, which might facilitate clinical decision-making process.

Background: Hepatic PEComas are very rare. Few systematic reports are available characterizing the imaging and pathological features of hepatic PEComa. The aim of this study was to investigate the imaging findings of primary hepatic perivascular epithelioid cell tumors (PEComa) and its correlation with histopathological features., Methods: The CT, MRI and ultrasound images and pathological findings of 22 patients with primary hepatic PEComa were retrospectively reviewed., Results: More females (14/22) were affected with the mean age of 47.1 years. Most patients (17/22) were asymptomatic and the routine laboratory tests were normal. More tumors occurred in the right lobe (13/22) with a mean diameter of 76.7 mm. Surgery was performed in 21 patients, and biopsy was performed in 1 patient. Immunohistochemical studies showed the expression rate of HMB-45 and Melan A was 100% (22/22) and 86.4% (19/22) within the tumor cells. The pathology diagnoses were angiomyolipoma (n = 18), lymphangioleiomyoma (n = 2), clear-cell myomelanocytic tumor of falciform ligament/ligamentum teres (n = 1), and not otherwise specified (n = 1). Fifteen cases were classified as uncertain malignant potential (n = 13) or malignant (n = 2). CT, MRI and ultrasound features included well-defined margins (19/22), internal heterogeneity (20/22), arterial enhancement (20/22), dysmorphic vessels (17/22), fat (9/22), hemorrhage (3/22), necrosis (8/22), and calcification (2/22). The diagnostic accuracy was only 27.3% (6/22). No local recurrence or metastasis was found in the follow-up patients (12/22)., Conclusions: On CT, MRI and ultrasound images, most hepatic PEComas are well-defined, heterogeneous, arterial enhanced masses with dysmorphic vessels, with or without fat, especially in middle-aged females. With the potential to be malignant, timely surgical resection and long-term follow-up may be helpful for improving the prognosis.