Establishing a predictive model for tumor mutation burden status based on ¹⁸F-FDG PET/CT and clinical features of non-small cell lung cancer patients.

ABSTRACT:

Background

Tumor mutation burden (TMB) has emerged as a promising biomarker for immune checkpoint inhibitors (ICI) response, but its detection through whole exome sequencing (WES) is costly and invasive. This study aims to establish a predictive model for TMB using baseline metabolic parameters (MPs) of ¹⁸F-fluorodeoxyglucose (FDG) uptake on positron emission tomography/computed tomography (PET/CT) and clinical features in non-small cell lung cancer (NSCLC) patients, potentially offering a non-invasive and cost-effective method to predict TMB status.

Methods

A total of 223 NSCLC patients with baseline ¹⁸F-FDG PET/CT scans and TMB detection results were retrospectively enrolled from January 2019 to September 2023, and were divided into two groups: TMB-hi

SUBMITTER: Chen Z 

PROVIDER: S-EPMC11484715 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature