Application of artificial intelligence model in pathological staging and prognosis of clear cell renal cell carcinoma - Jing Yao; Lai Wei; Peipei Hao; Zhongliu Liu; Peijun Wang (Discover Oncology, 2024)
Abstract This study aims to develop a deep learning (DL) model based on whole-slide images (WSIs) to predict the pathological stage of clear cell renal cell carcinoma (ccRCC). The histopathological images of 513 ccRCC patients were downloaded from The Cancer Genome Atlas (TCGA) database and randomly divided into training set and validation set according to the ratio of 8∶2. The CLAM algorithm was used to establish the DL model, and the stability of the model was evaluated in the external validation set. DL features were extracted from the model to construct a prognostic risk model, which was validated in an external dataset. The results showed that the DL model showed excellent prediction ability with an area under the curve (AUC) of 0.875 and an average accuracy score of 0.809, indicating that the model could reliably distinguish ccRCC patients at different stages from histopathological images. In addition, the prognostic risk model constructed by DL characteristics showed that the overall survival rate of patients in the high-risk group was significantly lower than that in the low-risk group (P = 0.003), and AUC values for predicting 1-, 3- and 5-year overall survival rates were 0.68, 0.69 and 0.69, respectively, indicating that the prediction model had high sensitivity and specificity. The results of the validation set are consistent with the above results. Therefore, DL model can accurately predict the pathological stage and prognosis of ccRCC patients, and provide certain reference value for clinical diagnosis.