Estimation of TP53 mutations for endometrial cancer based on diffusion-weighted imaging deep learning and radiomics features

作者：Lei Shen, Bo Dai, Shewei Dou, Fengshan Yan, Tianyun Yang, Yaping Wu · 发表于：BMC Cancer · 年份：2025 · DOI：10.1186/s12885-025-13424-5 · 被引用次数：12 · 研究领域：Endometrial and Cervical Cancer Treatments、Radiomics and Machine Learning in Medical Imaging、Cancer Genomics and Diagnostics

To construct a prediction model based on deep learning (DL) and radiomics features of diffusion weighted imaging (DWI), and clinical variables for evaluating TP53 mutations in endometrial cancer (EC). DWI and clinical data from 155 EC patients were included in this study, consisting of 80 in the training set, 35 in the test set, and 40 in the external validation set. Radiomics features, convolutional neural network-based DL features, and clinical variables were analyzed. Feature selection was performed using Mann-Whitney U test, LASSO regression, and SelectKBest. Prediction models were established by gaussian process (GP) and decision tree (DT) algorithms and evaluated by the area under the receiver operating characteristic curve (AUC), net reclassification index (NRI), calibration curves, and decision curve analysis (DCA). Compared to the DL (AUC training = 0.830, AUC test = 0.779, and AUC validation = 0.711), radiomics (AUC training = 0.810, AUC test = 0.710, and AUC validation = 0.839), and clinical (AUC training = 0.780, AUC test = 0.685, and AUC validation = 0.695) models, the combined model based on the GP algorithm, which consisted of four DL features, five radiomics features, and two clinical variables, not only demonstrated the highest diagnostic efficacy (AUC training = 0.949, AUC test = 0.877, and AUC validation = 0.914) but also led to an improvement in risk reclassification of the TP53 mutation (NIR training = 66.38%, 56.98%, and 83.48%, NIR test = 50.72%, 80.43%...