Radiomic approach to support multidisciplinary tumor board decision-making in locally advanced non-small cell lung cancer.

ABSTRACT:

Background and objective

Selecting the optimal treatment for locally advanced non-small cell lung cancer (LA-NSCLC) is complex and typically requires multidisciplinary tumor board (MTB) evaluation. This study investigated whether machine learning (ML) models trained on MTB decisions could support treatment selection by integrating clinicopathological characteristics with radiomic features from both the primary tumor and mediastinal lymph nodes (LN).

Materials and methods

We retrospectively analyzed patients with LA-NSCLC whose treatments had been decided by an expert MTB. Patients were categorized into three pathways: (A) upfront surgery, (B) neoadjuvant systemic treatment followed by surgery, (C) concurrent chemoradiotherapy. Baseline CT scans were segmented to extract radi

SUBMITTER: Pasello G 

PROVIDER: S-EPMC12757298 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature