Assessing clinician performance using a multi-modality clinical decision-support system for lung cancer prognostication.

ABSTRACT: Surgery is the primary treatment for early-stage lung cancer. Adjuvant therapy is offered to patients who are at a high risk of recurrence, however, determining the patients that would benefit from additional therapy is often difficult. In this study, we aimed to develop a clinical decision support system (CDSS) for post-surgery lung cancer prognostication integrating a multi-modality deep learning model (DLM). Pre-operative medical images and clinical, surgical, and pathological information were fed into an externally validated DLM. A CDSS was then developed to display the patient information and DLM results for potential clinical use. Four oncologists evaluated each patient's recurrence probability, their confidence level, and their post-surgery recommendations both with and without the

SUBMITTER: Christie JR 

PROVIDER: S-EPMC12800322 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature