Dual-layer spectral detector computed tomography multiparameter machine learning model for prediction of invasive lung adenocarcinoma.

ABSTRACT:

Background

Lung adenocarcinoma (LUAD) is the leading cause of cancer-related deaths. High-resolution computed tomography (HRCT) has improved the detection of ground glass nodules (GGNs), which are early indicators of lung cancer. Accurate assessment of GGN invasiveness is crucial for determining the appropriate surgical approach. Dual-layer spectral detector computed tomography (DLCT) offers advanced imaging capabilities, including electron density and iodine density, which enhance the evaluation of GGN invasiveness. This study aims to develop a machine learning (ML) model that integrates DLCT parameters and clinical features to predict the invasiveness of GGNs in LUAD, aiding in surgical decision-making and prognosis improvement.

Methods

The retrospective study encompassed

SUBMITTER: Wan J 

PROVIDER: S-EPMC11921186 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature