Development and validation of a CT based radiomics nomogram for preoperative prediction of ISUP/WHO grading in renal clear cell carcinoma.

Background: Nuclear grading of clear cell renal cell carcinoma (ccRCC) is crucial for its diagnosis and treatment.

Objective: To develop and validate a machine learning model for preoperative assessment of ccRCC nuclear grading using CT radiomics.

Materials and methods: This retrospective study analyzed 146 ccRCC patients who underwent surgery between June 2016 and January 2022 at two hospitals (the Quzhou Affiliated Hospital of Wenzhou Medical University with 117 cases and the Affiliated Cancer Hospital of University of Chinese Academy of Sciences with 29 cases). Radiomic features were extracted from preoperative abdominal CT images. Features reduction and selection were carried out using intraclass correlation efficient (ICCs), Spearman rank correlation coefficientsand and the Least Absolute Shrinkage and Selection Operator (LASSO) regression method. Radiomics and clinical models were developed utilizing Support Vector Machine (SVM), Extremely Randomized Trees (Extra Trees), Light Gradient Boosting Machine (LightGBM), Random Forest (RF) and K-Nearest Neighbors (KNN) algorithms. Subsequently, the radiomics nomogramwas developed incorporating independent clinical predictors and Rad_signature. Model performance was evaluated using the area under the curve (AUC), accuracy, sensitivity, and specificity, with decision curve analysis (DCA) assessing its clinical utility.

Results: We extracted 1834 radiomic features from each CT sequence, with 1320 features passing through the ICCs screening process. 480 radiomics features were screened by Spearson correlation coefficient. Then, 15 radiomic features with non-zero coefficient values were determined by Lasso dimensionality reduction technique. The five machine learning methods effectively distinguished nuclear grades. The radiomics nomogram outperformed clinical radiological models and radiomics feature models in predictive performance, with an AUC of 0.936 (95% CI 0.885-0.986) for the training set and 0.896 (95% CI 0.716-1.000) for the external verification set. DCA indicated potential clinical applicability of the nomogram.

Conclusion: The radiomics nomogram, developed by integrating clinically independent risk factors and and Rad_signature, demonstrated robust performance in preoperative ccRCC grading. It offers a non-invasive tool that aids in ccRCC grading and clinical decision-making, with potential to enhance treatment strategies.