A CT-Based Radiomics Nomogram Integrated With Clinic-Radiological Features for Preoperatively Predicting WHO/ISUP Grade of Clear Cell Renal Cell Carcinoma.

This study aims to develop and validate a CT-based radiomics nomogram integrated with clinic-radiological factors for preoperatively differentiating high-grade from low-grade clear cell renal cell carcinomas (CCRCCs). 370 patients with complete clinical, pathological, and CT image data were enrolled in this retrospective study, and were randomly divided into training and testing sets with a 7:3 ratio. Radiomics features were extracted from nephrographic phase (NP) contrast-enhanced images, and then a radiomics model was constructed by the selected radiomics features using a multivariable logistic regression combined with the most suitable feature selection algorithm determined by the comparison among least absolute shrinkage and selection operator (LASSO), recursive feature elimination (RFE) and ReliefF. A clinical model was established using clinical and radiological features. A radiomics nomogram was constructed by integrating the radiomics signature and independent clinic-radiological features. Performance of these three models was assessed using receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA). Using multivariate logistic regression analysis, three clinic-radiological features including intratumoral necrosis (OR=3.00, 95% CI=1.30-6.90, p=0.049), intratumoral angiogenesis (OR=3.28, 95% CI=1.22-8.78, p=0.018), and perinephric metastasis (OR=2.90, 95% CI=1.03-8.17, p=0.044) were found to be independent predictors of WHO/ISUP grade in CCRCC. Incorporating the above clinic-radiological predictors and radiomics signature constructed by LASSO, a CT-based radiomics nomogram was developed, and presented better predictive performance than clinic-radiological model and radiomics signature model, with an AUC of 0.891 (95% CI=0.832-0.962) and 0.843 (95% CI=0.718-0.975) in the training and testing sets, respectively. DCA indicated that the nomogram has potential clinical usefulness. The CT-based radiomics nomogram is a promising tool to predict WHO/ISUP grade of CCRCC preoperatively and noninvasively.

Xv Y ，Lv F ，Guo H ，Liu Z ，Luo D ，Liu J ，Gou X ，He W ，Xiao M ，Zheng Y ... -  《Frontiers in Oncology》

Machine learning-based CT radiomics approach for predicting WHO/ISUP nuclear grade of clear cell renal cell carcinoma: an exploratory and comparative study.

To investigate the predictive performance of machine learning-based CT radiomics for differentiating between low- and high-nuclear grade of clear cell renal cell carcinomas (CCRCCs). This retrospective study enrolled 406 patients with pathologically confirmed low- and high-nuclear grade of CCRCCs according to the WHO/ISUP grading system, which were divided into the training and testing cohorts. Radiomics features were extracted from nephrographic-phase CT images using PyRadiomics. A support vector machine (SVM) combined with three feature selection algorithms such as least absolute shrinkage and selection operator (LASSO), recursive feature elimination (RFE), and ReliefF was performed to determine the most suitable classification model, respectively. Clinicoradiological, radiomics, and combined models were constructed using the radiological and clinical characteristics with significant differences between the groups, selected radiomics features, and a combination of both, respectively. Model performance was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses. SVM-ReliefF algorithm outperformed SVM-LASSO and SVM-RFE in distinguishing low- from high-grade CCRCCs. The combined model showed better prediction performance than the clinicoradiological and radiomics models (p < 0.05, DeLong test), which achieved the highest efficacy, with an area under the ROC curve (AUC) value of 0.887 (95% confidence interval [CI] 0.798-0.952), 0.859 (95% CI 0.748-0.935), and 0.828 (95% CI 0.731-0.929) in the training, validation, and testing cohorts, respectively. The calibration and decision curves also indicated the favorable performance of the combined model. A combined model incorporating the radiomics features and clinicoradiological characteristics can better predict the WHO/ISUP nuclear grade of CCRCC preoperatively, thus providing effective and noninvasive assessment.

Xv Y ，Lv F ，Guo H ，Zhou X ，Tan H ，Xiao M ，Zheng Y ... -  《-》

Multiparameter computed tomography (CT) radiomics signature fusion-based model for the preoperative prediction of clear cell renal cell carcinoma nuclear grade: a multicenter development and external validation study.

The preoperative prediction of the pathological nuclear grade of clear cell renal cell carcinoma (CCRCC) is crucial for clinical decision making. However, radiomics features from one or two computed tomography (CT) phases are required to predict the CCRCC grade, which reduces the predictive performance and generalizability of this method. We aimed to develop and externally validate a multiparameter CT radiomics-based model for predicting the World Health Organization/International Society of Urological Pathology (WHO/ISUP) grade of CCRCC. A total of 500 CCRCC patients at The First, Second, and Yongchuan Hospitals of Chongqing Medical University between January 2016 and May 2022 were retrospectively enrolled in this study. The patients were divided into the training set (n=268), internal testing set (n=115), and two external testing sets (testing set 1, n=62; testing set 2, n=55). Radiomics features were extracted from multi-phase CT images, and radiomics signatures (RSs) were created by least absolute shrinkage and selection operator (LASSO) regression. In addition, a clinical model was developed. A combined model was also established that integrated the RSs with the clinical factors, and was visualized via a nomogram. The performance of the established model was assessed using area under the curve (AUC) values, a calibration curve analysis, and a decision curve analysis (DCA). Among the four RSs and the clinical model, the RS-Triphasic had the best predictive performance with AUCs of 0.88 [95% confidence interval (CI): 0.85-0.91] and 0.84 (95% CI: 0.74-0.95) in the training and testing sets, respectively, and 0.82 (95% CI: 0.72-0.93) and 0.82 (95% CI: 0.71-0.93) in external testing sets 1 and 2. Integrating the RS-Triphasic, RS-corticomedullary phase (CMP), RS-nephrographic phase (NP), RS-non-contrast phase (NCP) with the clinical risk factors, a combined model was established with AUCs of 0.92 (95% CI: 0.89-0.94), 0.86 (95% CI: 0.76-0.95), 0.84 (95% CI: 0.73-0.95), and 0.82 (95% CI: 0.70-0.94) for the training, internal testing, and external testing sets 1 and 2, respectively. The DCA indicated that the nomogram had a greater overall net benefit than the clinical and radiomics models. The multiparameter CT RS fusion-based model had high accuracy in differentiating between high- and low-grade CCRCC preoperatively. Thus, it has great potential as a useful tool for personalized treatment planning and clinical decision making for CCRCC patients.

Xv Y ，Wei Z ，Lv F ，Jiang Q ，Guo H ，Zheng Y ，Zhang X ，Xiao M ... -  《-》

Multiphase CT radiomics nomogram for preoperatively predicting the WHO/ISUP nuclear grade of small (< 4 cm) clear cell renal cell carcinoma.

Gao Y ，Wang X ，Zhao X ，Zhu C ，Li C ，Li J ，Wu X ... -  《BMC CANCER》

A Computed Tomography-Based Radiomics Nomogram to Preoperatively Predict Tumor Necrosis in Patients With Clear Cell Renal Cell Carcinoma.

Objective: To develop and validate a radiomics nomogram for preoperative prediction of tumor necrosis in patients with clear cell renal cell carcinoma (ccRCC). Methods: In total, 132 patients with pathologically confirmed ccRCC in one hospital were enrolled as a training cohort, while 123 ccRCC patients from second hospital served as the independent validation cohort. Radiomic features were extracted from corticomedullary and nephrographic phase contrast-enhanced computed tomography (CT) images. A radiomics signature based on optimal features selected by consistency analysis and the least absolute shrinkage and selection operator was developed. An image features model was constructed based on independent image features according to visual assessment. By integrating the radiomics signature and independent image features, a radiomics nomograph was constructed. The predictive performance of the above models was evaluated using receiver operating characteristic (ROC) curve analysis. Furthermore, the nomogram was assessed using calibration curve and decision curve analysis. Results: Thirty-seven features were used to establish a radiomics signature, which demonstrated better predictive performance than did the image features model constructed using tumor size and intratumoral vessels in the training and validation cohorts (p <0.05). The radiomics nomogram demonstrated satisfactory discrimination in the training (area under the ROC curve [AUC] 0.93 [95% CI 0.87-0.96]) and validation (AUC 0.87 [95% CI 0.79-0.93]) cohorts and good calibration (Hosmer-Lemeshow p>0.05). Decision curve analysis verified that the radiomics nomogram had the best clinical utility compared with the other models. Conclusion: The radiomics nomogram developed in the present study is a promising tool to predict tumor necrosis and facilitate preoperative clinical decision-making for patients with ccRCC.

Jiang Y ，Li W ，Huang C ，Tian C ，Chen Q ，Zeng X ，Cao Y ，Chen Y ，Yang Y ，Liu H ，Bo Y ，Luo C ，Li Y ，Zhang T ，Wang R ... -  《-》