Accurate prognosis prediction is essential for guiding cancer treatment and improving patient outcomes.While recent studies have demonstrated the potential of histopathological images in survival analysis,existing mod...Accurate prognosis prediction is essential for guiding cancer treatment and improving patient outcomes.While recent studies have demonstrated the potential of histopathological images in survival analysis,existing models are typically developed in a cancerspecific manner,lack extensive external validation,and often rely on molecular data that are not routinely available in clinical practice.To address these limitations,we present PROGPATH,a unified model capable of integrating histopathological image features with routinely collected clinical variables to achieve pancancer prognosis prediction.PROGPATH employs a weakly supervised deep learning architecture built upon the foundation model for image encoding.Morphological features are aggregated through an attention-guided multiple instance learning module and fused with clinical information via a cross-attention transformer.A router-based classification strategy further refines the prediction performance.PROGPATH was trained on 7999 whole-slide images(WSIs)from 6,670 patients across 15 cancer types,and extensively validated on 17 external cohorts with a total of 7374 WSIs from 4441 patients,covering 12 cancer types from 8 consortia and institutions across three continents.PROGPATH achieved consistently superior performance compared with state-of-the-art multimodal prognosis prediction models.It demonstrated strong generalizability across cancer types and robustness in stratified subgroups,including early-and advancedstage patients,treatment cohorts(radiotherapy and pharmaceutical therapy),and biomarker-defined subsets.We further provide model interpretability by identifying pathological patterns critical to PROGPATH’s risk predictions,such as the degree of cell differentiation and extent of necrosis.Together,these results highlight the potential of PROGPATH to support pancancer outcome prediction and inform personalized cancer management strategies.展开更多
基金supported in part by the National Cancer Institute under award numbers R01CA268287A1,U01CA269181,R01CA26820701A1,R01CA249992-01A1,R01CA202752-01A1,R01CA208236-01A1,R01CA216579-01A1,R01CA220581-01A1,R01CA257612-01A1,1U01CA239055-01,1U01CA248226-01,1U54CA254566-01National Heart,Lung and Blood Institute 1R01HL15127701A1,R01HL15807101A1+8 种基金National Institute of Biomedical Imaging and Bioengineering 1R43EB028736-01VA Merit Review Award IBX004121A from the United States Department of Veterans Affairs Biomedical Laboratory Research and Development Service the Office of the Assistant Secretary of Defense for Health Affairs,through the Breast Cancer Research Program(W81XWH-19-1-0668)the Prostate Cancer Research Program(W81XWH-20-1-0851)the Lung Cancer Research Program(W81XWH-18-1-0440,W81XWH-20-1-0595)the Peer Reviewed Cancer Research Program(W81XWH-18-1-0404,W81XWH-21-1-0345,W81XWH-211-0160)the Kidney Precision Medicine Project(KPMP)Glue Grant and sponsored research agreements from Bristol Myers-Squibb,Boehringer-Ingelheim,Eli-Lilly and Astrazenecasupported in part by the National Natural Science Foundation of China general program(No.61571314)the Sichuan University-Yibin City Strategic Cooperation Special Fund(No.2020CDYB-27)Support Program of Sichuan Science and Technology Department(No.2023YFS0327-LH).
文摘Accurate prognosis prediction is essential for guiding cancer treatment and improving patient outcomes.While recent studies have demonstrated the potential of histopathological images in survival analysis,existing models are typically developed in a cancerspecific manner,lack extensive external validation,and often rely on molecular data that are not routinely available in clinical practice.To address these limitations,we present PROGPATH,a unified model capable of integrating histopathological image features with routinely collected clinical variables to achieve pancancer prognosis prediction.PROGPATH employs a weakly supervised deep learning architecture built upon the foundation model for image encoding.Morphological features are aggregated through an attention-guided multiple instance learning module and fused with clinical information via a cross-attention transformer.A router-based classification strategy further refines the prediction performance.PROGPATH was trained on 7999 whole-slide images(WSIs)from 6,670 patients across 15 cancer types,and extensively validated on 17 external cohorts with a total of 7374 WSIs from 4441 patients,covering 12 cancer types from 8 consortia and institutions across three continents.PROGPATH achieved consistently superior performance compared with state-of-the-art multimodal prognosis prediction models.It demonstrated strong generalizability across cancer types and robustness in stratified subgroups,including early-and advancedstage patients,treatment cohorts(radiotherapy and pharmaceutical therapy),and biomarker-defined subsets.We further provide model interpretability by identifying pathological patterns critical to PROGPATH’s risk predictions,such as the degree of cell differentiation and extent of necrosis.Together,these results highlight the potential of PROGPATH to support pancancer outcome prediction and inform personalized cancer management strategies.
