Utilizing Immuno-Oncology registry data for enhanced non-small cell lung cancer treatment predictions.

ABSTRACT:

Objectives

We aim to leverage more comprehensive phenotypic and genotypic clinical data to enhance the treatment response predictions.

Materials and methods

The study cohort includes 213 NSCLC patients who underwent ICI therapy. Patients were categorized based on treatment outcomes: those with complete or partial responses were considered responders, while those exhibiting stable or progressive disease were deemed non-responders. Comprehensive phenotypic and genomic features were selected for prediction. We developed 9 machine learning models. The model demonstrating the highest area under the receiver operating characteristic curve (AUROC) performance was further analyzed using Shapley additive explanation values to interpret the predictive factors.

Results

There wer

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC12239864 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature