Machine learning model for circulating tumor DNA detection in chronic obstructive pulmonary disease patients with lung cancer.

ABSTRACT:

Background

Patients with chronic obstructive pulmonary disease (COPD) have a high risk of developing lung cancer. Due to the high rates of complications from invasive diagnostic procedures in this population, detecting circulating tumor DNA (ctDNA) as a non-invasive method might be useful. However, clinical characteristics that are predictive of ctDNA mutation detection remain incompletely understood. This study aimed to investigate factors associated with ctDNA detection in COPD patients with lung cancer.

Methods

Herein, 177 patients with COPD and lung cancer were prospectively recruited. Plasma ctDNA was genotyped using targeted deep sequencing. Comprehensive clinical variables were collected, including the emphysema index (EI), using chest computed tomography. Machine lea

SUBMITTER: Shin SH 

PROVIDER: S-EPMC10891398 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature