Risk-stratified classification of pulmonary nodule malignancy via a machine learning model integrating imaging and cell-free DNA: a model development and validation study (DECIPHER-NODL).

ABSTRACT:

Background

Accurate risk stratification of pulmonary nodules is critical for early lung cancer detection. This study aimed to improve malignancy classification and invasiveness prediction using machine learning models integrating low-dose computed tomography (LDCT) radiomics and plasma cell-free DNA (cfDNA) fragmentomics.

Methods

This multicenter study enrolled 1356 participants across discovery (n = 1147) and external validation (n = 209) cohorts. A deep learning-based imaging model processed LDCT scans for automated lung nodule detection and malignancy classification. A parallel cfDNA model analyzed four whole-genome fragmentation features: copy number variation, fragment size ratio, fragment-based methylation, and mutation context and signature. The two models were integr

SUBMITTER: Wang H 

PROVIDER: S-EPMC12661453 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature