Preoperative CT radiomics-based model for predicting Ki-67 expression in clear cell renal cell carcinoma patients

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma (RCC), and developing personalized treatment strategies is crucial for improving patient prognosis. This study aims to develop and validate a preoperative computer tomography (CT) radiomics-based predictive model to estimate Ki-67 expression in ccRCC patients, thereby assisting in clinical treatment decisions and prognosis prediction.

A retrospective analysis was conducted on 214 ccRCC patients who underwent surgical treatment at Gansu Provincial Hospital between January 2018 and November 2023. Patients were classified into high Ki-67 expression (n=123) and low Ki-67 expression (n=91) groups based on postoperative immunohistochemical staining results. The dataset was randomly divided in a 7꞉3 ratio into a training set (n=149) and a validation set (n=65). Preoperative contrast-enhanced urinary CT images and clinical data were collected. After preprocessing, 5 mm arterial-phase CT images were manually segmented layer by layer to delineate the region of interest (ROI) using ITK-SNAP 3.8 software. Radiomic features were then extracted using the FeAture Explorer (FAE) package. Dimensionality reduction and feature selection were performed using the least absolute shrinkage and selection operator (LASSO) algorithm, yielding the optimal feature set. Three classification models were constructed using logistic regression (LR), multilayer perceptron (MLP), and support vector machine (SVM). The receiver operating characteristic (ROC) curve, area under the curve (AUC), decision curve analysis (DCA), and calibration curves were used for model evaluation.

A total of 107 radiomic features were extracted from 5 mm arterial-phase CT images, and twenty-one features significantly associated with Ki-67 expression were selected using the LASSO algorithm. Predictive models were developed using LR, MLP, and SVM classifiers. In the training and validation sets, the AUC values for each model were 0.904 (95% CI 0.852 to 0.956) and 0.818 (95% CI 0.710 to 0.926) for the LR model, 0.859 (95% CI 0.794 to 0.923) and 0.823 (95% CI 0.716 to 0.929) for the MLP model, and 0.917 (95% CI 0.865 to 0.969) and 0.857 (95% CI 0.760 to 0.953) for the SVM model. DCA demonstrated that all models had good clinical net benefit, while calibration curves indicated high accuracy of the predictions, supporting the robustness and reliability of the models.

A CT radiomics-based model for predicting Ki-67 expression in ccRCC was successfully developed. This model provides valuable guidance for treatment planning and prognostic assessment in ccRCC patients.