Prognostic information at diagnosis has important implications for cancer
treatment and monitoring. Although cancer staging, histopathological
assessment, molecular features, and clinical variables can provide useful
prognostic insights, improving risk stratification remains an active research
area. We developed a deep learning system (DLS) to predict disease specific
survival across 10 cancer types from The Cancer Genome Atlas (TCGA). We used a
weakly-supervised approach without pixel-level annotations, and tested three
different survival loss functions. The DLS was developed using 9,086 slides
from 3,664 cases and evaluated using 3,009 slides from 1,216 cases. In
multivariable Cox regression analysis of the combined cohort including all 10
cancers, the DLS was significantly associated with disease specific survival
(hazard ratio of 1.58, 95% CI 1.28-1.70, p<0.0001) after adjusting for cancer
type, stage, age, and sex. In a per-cancer adjusted subanalysis, the DLS
remained a significant predictor of survival in 5 of 10 cancer types. Compared
to a baseline model including stage, age, and sex, the c-index of the model
demonstrated an absolute 3.7% improvement (95% CI 1.0-6.5) in the combined
cohort. Additionally, our models stratified patients within individual cancer
stages, particularly stage II (p=0.025) and stage III (p<0.001). By developing
and evaluating prognostic models across multiple cancer types, this work
represents one of the most comprehensive studies exploring the direct
prediction of clinical outcomes using deep learning and histopathology images.
Our analysis demonstrates the potential for this approach to provide prognostic
information in multiple cancer types, and even within specific pathologic
stages. However, given the relatively small number of clinical events, we
observed wide confidence intervals, suggesting that future work will benefit
from larger datasets.

通过开发深度学习系统，在多种癌症类型中利用组织病理图像预测疾病特异性生存，为癌症治疗和监测提供重要的预后信息。