文献类型：期刊文献

英文题名：The predictive value of radiomics and deep learning for synchronous distant metastasis in clear cell renal cell carcinoma

作者：He, Wan-Bin[1];Zhou, Chuan[1];Yang, Zhi-Jun[1];Zhang, Yun-Feng[1];Zhang, Wen-Bo[2];He, Han[1];Wang, Jia[2];Zhou, Feng-Hai[1,3]

第一作者：He, Wan-Bin

通信作者：Zhou, FH[1];Zhou, FH[2]

机构：[1]Lanzhou Univ, Clin Med Coll 1, Lanzhou 73000, Peoples R China;[2]Gansu Univ Chinese Med, Clin Med Coll 1, Lanzhou 730000, Peoples R China;[3]Gansu Prov Hosp, Dept Urol, Lanzhou 730000, Peoples R China

第一机构：Lanzhou Univ, Clin Med Coll 1, Lanzhou 73000, Peoples R China

通信机构：[1]corresponding author), Lanzhou Univ, Clin Med Coll 1, Lanzhou 73000, Peoples R China;[2]corresponding author), Gansu Prov Hosp, Dept Urol, Lanzhou 730000, Peoples R China.

年份：2025

卷号：16

期号：1

外文期刊名：DISCOVER ONCOLOGY

收录：;Scopus(收录号：2-s2.0-85217661422);WOS:【SCI-EXPANDED(收录号:WOS:001405617300002)】；

基金：We would like to thank Gansu Provincial People's Hospital, Gansu Provincial Second People's Hospital, Lanzhou University, and Gansu University of Chinese Medicine for their guidance and advice during the implementation of this project; we thank the onekey AI platform for providing technical support for this study.

语种：英文

外文关键词：Radiomics; Deep learning; Renal clear cell carcinoma; Simultaneous distant metastasis

摘要：ObjectiveThe objective of this research was to devise and authenticate a predictive model that employs CT radiomics and deep learning methodologies for the accurate prediction of synchronous distant metastasis (SDM) in clear cell renal cell carcinoma (ccRCC).MethodsA total of 143 ccRCC patients were included in the training cohort, and 62 ccRCC patients were included in the validation cohort. The CT images from all patients were normalized, and the tumor regions were manually segmented via ITK-SNAP software. Radiomic features were extracted via the FAE toolkit. The least absolute shrinkage and selection operator (LASSO) algorithm was employed to select features and build various machine learning models. Additionally, the largest cross-section of the tumor was cropped to train the deep learning model. Multiple deep learning models were trained to predict SDM in ccRCC patients. The results of the best machine learning model were then fused with those of the deep learning model to create a combined model.ResultsOf the 944 radiomic features identified, 15 were closely associated with SDM. With these 15 features, the support vector machine (SVM) model emerged as the most effective, demonstrating areas under the curve (AUC) of 0.860 and 0.813 in the training and validation cohort, respectively. Among the deep learning models, ResNet101 performed optimally, achieving AUC of 0.815 and 0.743 in the training and validation cohort, respectively. The combined model yielded an AUC of 0.863. Decision curve analysis suggested that the combined model offers superior clinical applicability.ConclusionThe model integrates radiomics and deep learning, showing significant potential in predicting SDM in ccRCC patients. It holds promise for supporting clinical decision-making, reducing missed diagnoses of SDM, and guiding patients in further enhancing their systemic examinations.