Pathology image-based predictive model for individual survival time of early-stage lung adenocarcinoma patients.

ABSTRACT: The tumor microenvironment (TME) is associated with tumor prognosis, immunotherapy response, and prognosis in patients. Here, we hypothesized that the entire TME in pathology image is associated with the survival time prediction. To address this hypothesis, we utilize the entire TME on pathology image of early-stage lung adenocarcinoma (esLUAD), which is the most common histological subtype of lung cancer. Notably, we investigated whether machine learning models can predict individual survival time from pathology images without region-level annotation and solely based on patient-level survival data. In particular, we proposed a pathology image-based predictive model in a cascaded learning system to predict the individual survival time of esLUAD patients in two independent cohorts (National

SUBMITTER: Vo VT 

PROVIDER: S-EPMC12528468 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature