Artificial neural network-augmented dosiomic integration for predicting distant recurrence in NSCLC patients treated with SBRT.

ABSTRACT:

Objective

Stereotactic body radiotherapy (SBRT) is a standard curative treatment for inoperable early-stage non-small cell lung cancer (NSCLC) patients. However, the high rate of distant recurrence following radiotherapy remains a significant clinical challenge. This study focuses on developing a machine learning model for distant recurrence prediction using diverse dosiomic and patient-specific clinical features. The proposed model aims to assist clinicians in informed decision-making, individualized treatment decisions to improve post-SBRT outcomes.

Method

This study utilized a multi-institutional dataset comprising 575 NSCLC patients who underwent SBRT. A total of 21 features, comprising 14 dosimetric and 7 clinical variables, were incorporated for developing the predicti

SUBMITTER: Halder K 

PROVIDER: S-EPMC12507626 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature