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 ... -  《Frontiers in Oncology》

Preoperative CT Radiomics Predicting the SSIGN Risk Groups in Patients With Clear Cell Renal Cell Carcinoma: Development and Multicenter Validation.

Objective: The stage, size, grade, and necrosis (SSIGN) score can facilitate the assessment of tumor aggressiveness and the personal management for patients with clear cell renal cell carcinoma (ccRCC). However, this score is only available after the postoperative pathological evaluation. The aim of this study was to develop and validate a CT radiomic signature for the preoperative prediction of SSIGN risk groups in patients with ccRCC in multicenters. Methods: In total, 330 patients with ccRCC from three centers were classified into the training, external validation 1, and external validation 2 cohorts. Through consistent analysis and the least absolute shrinkage and selection operator, a radiomic signature was developed to predict the SSIGN low-risk group (scores 0-3) and intermediate- to high-risk group (score ≥ 4). An image feature model was developed according to the independent image features, and a fusion model was constructed integrating the radiomic signature and the independent image features. Furthermore, the predictive performance of the above models for the SSIGN risk groups was evaluated with regard to their discrimination, calibration, and clinical usefulness. Results: A radiomic signature consisting of sixteen relevant features from the nephrographic phase CT images achieved a good calibration (all Hosmer-Lemeshow p > 0.05) and favorable prediction efficacy in the training cohort [area under the curve (AUC): 0.940, 95% confidence interval (CI): 0.884-0.973] and in the external validation cohorts (AUC: 0.876, 95% CI: 0.811-0.942; AUC: 0.928, 95% CI: 0.844-0.975, respectively). The radiomic signature performed better than the image feature model constructed by intra-tumoral vessels (all p < 0.05) and showed similar performance with the fusion model integrating radiomic signature and intra-tumoral vessels (all p > 0.05) in terms of the discrimination in all cohorts. Moreover, the decision curve analysis verified the clinical utility of the radiomic signature in both external cohorts. Conclusion: Radiomic signature could be used as a promising non-invasive tool to predict SSIGN risk groups and to 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 ... -  《-》

Development and validation of a CT-based nomogram for preoperative prediction of clear cell renal cell carcinoma grades.

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

Zheng Z ，Chen Z ，Xie Y ，Zhong Q ，Xie W ... -  《-》

A CT-based radiomics nomogram for differentiation of renal angiomyolipoma without visible fat from homogeneous clear cell renal cell carcinoma.

Nie P ，Yang G ，Wang Z ，Yan L ，Miao W ，Hao D ，Wu J ，Zhao Y ，Gong A ，Cui J ，Jia Y ，Niu H ... -  《-》

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》