Deep-Learning Serial CT Prediction of Survival in Immunotherapy-Treated Non-Small Cell Lung Cancer.

ABSTRACT:

Importance

Reliable early response biomarkers for overall survival (OS) are lacking for patients receiving immune checkpoint inhibitor (ICI) therapy for advanced non-small cell lung cancer (NSCLC). Existing imaging-based measures, such as Response Evaluation Criteria in Solid Tumors (RECIST) and tumor volume change (TVC), have limited predictive value for long-term outcomes, and advanced imaging-based biomarkers may enhance decision-making in clinical practice and clinical trials.

Objective

To develop and validate a fully automated deep-learning imaging-based biomarker using pretherapy and 12-week follow-up computed tomography (CT) scans.

Design, setting, and participants

This prognostic study used retrospectively collected HER data from routine clinical practice (RCP

SUBMITTER: Sako C 

PROVIDER: S-EPMC12853213 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature