文献类型：期刊文献

英文题名：Multimodal data integration for predicting progression risk in castration-resistant prostate cancer using deep learning: a multicenter retrospective study

作者：Zhou, Chuan[1,2];Zhang, Yun-Feng[3];Guo, Sheng[3];Huang, Yu-Qian[4];Qiao, Xiao-Ni[5];Wang, Rong[1,6];Zhao, Lian-Ping[3,6];Chang, De-Hui[3,5];Zhao, Li-Ming[7];Da, Ming-Xu[1,2,3];Zhou, Feng-Hai[1,2,3,8]

第一作者：Zhou, Chuan

通信作者：Da, MX[1];Zhou, FH[1];Da, MX[2];Zhou, FH[2];Da, MX[3];Zhou, FH[3];Zhou, FH[4]

机构：[1]Lanzhou Univ, Clin Med Coll 1, Lanzhou, Peoples R China;[2]Gansu Prov Hosp, Natl Hlth Commiss Peoples Republ China NHC Key Lab, Lanzhou, Peoples R China;[3]Gansu Univ Chinese Med, Clin Med Coll 1, Lanzhou, Peoples R China;[4]Chengdu Second Peoples Hosp, Dept Ctr Med Cosmetol, Chengdu, Peoples R China;[5]940 Hosp Joint Logist Support Force Chinese PLA, Dept Urol, Lanzhou, Peoples R China;[6]Gansu Prov Hosp, Dept Radiol, Lanzhou, Peoples R China;[7]Second Peoples Hosp Gansu Prov, Dept Urol, Lanzhou, Peoples R China;[8]Gansu Prov Hosp, Dept Urol, Lanzhou, Peoples R China

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

通信机构：[1]corresponding author), Lanzhou Univ, Clin Med Coll 1, Lanzhou, Peoples R China;[2]corresponding author), Gansu Prov Hosp, Natl Hlth Commiss Peoples Republ China NHC Key Lab, Lanzhou, Peoples R China;[3]corresponding author), Gansu Univ Chinese Med, Clin Med Coll 1, Lanzhou, Peoples R China;[4]corresponding author), Gansu Prov Hosp, Dept Urol, Lanzhou, Peoples R China.|[10735]甘肃中医药大学;

年份：2024

卷号：14

外文期刊名：FRONTIERS IN ONCOLOGY

收录：;Scopus(收录号：2-s2.0-85188911581);WOS:【SCI-EXPANDED(收录号:WOS:001191951500001)】；

基金：We thank for Chinese Medical Association of Gansu Province Geriatrics Society providing guidance and suggestions during the project. We thank for Dr.Yu-Qian Huang contributed to all the Figures in this paper.r The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was funded by grants from the following sources: National Natural Science Foundation of China (No.81860047), The Natural Science Foundation of Gansu Province (No.22JR5RA650), Key Science and Technology Program in Gansu Province (NO.21YF5FA016).

语种：英文

外文关键词：radiomics; pathomics; castration-resistant prostate cancer; deep learning; multi-modal

摘要：Purpose Patients with advanced prostate cancer (PCa) often develop castration-resistant PCa (CRPC) with poor prognosis. Prognostic information obtained from multiparametric magnetic resonance imaging (mpMRI) and histopathology specimens can be effectively utilized through artificial intelligence (AI) techniques. The objective of this study is to construct an AI-based CRPC progress prediction model by integrating multimodal data.Methods and materials Data from 399 patients diagnosed with PCa at three medical centers between January 2018 and January 2021 were collected retrospectively. We delineated regions of interest (ROIs) from 3 MRI sequences viz, T2WI, DWI, and ADC and utilized a cropping tool to extract the largest section of each ROI. We selected representative pathological hematoxylin and eosin (H&E) slides for deep-learning model training. A joint combined model nomogram was constructed. ROC curves and calibration curves were plotted to assess the predictive performance and goodness of fit of the model. We generated decision curve analysis (DCA) curves and Kaplan-Meier (KM) survival curves to evaluate the clinical net benefit of the model and its association with progression-free survival (PFS).Results The AUC of the machine learning (ML) model was 0.755. The best deep learning (DL) model for radiomics and pathomics was the ResNet-50 model, with an AUC of 0.768 and 0.752, respectively. The nomogram graph showed that DL model contributed the most, and the AUC for the combined model was 0.86. The calibration curves and DCA indicate that the combined model had a good calibration ability and net clinical benefit. The KM curve indicated that the model integrating multimodal data can guide patient prognosis and management strategies.Conclusion The integration of multimodal data effectively improves the prediction of risk for the progression of PCa to CRPC.