Preoperative CT radiomics-based model for predicting Ki-67 expression in clear cell renal cell carcinoma patients.
Year: 2024
Type: article
Study design: Observational Study
Abstract: OBJECTIVES: 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. METHODS: =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. RESULTS: 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. CONCLUSIONS: 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.
Source: PubMed
Authors: Zhijun Yang, Han He, Yunfeng Zhang, Jia Wang, Wenbo Zhang, Fenghai Zhou
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W4409148352