A CT-based deep learning model for preoperative prediction of spread through air spaces in clinical stage I lung adenocarcinoma.

ABSTRACT:

Objective

To develop and validate a deep learning signature for noninvasive prediction of spread through air spaces (STAS) in clinical stage I lung adenocarcinoma and compare its predictive performance with conventional clinical-semantic model.

Methods

A total of 513 patients with pathologically-confirmed stage I lung adenocarcinoma were retrospectively enrolled and were divided into training cohort (n = 386) and independent validation cohort (n = 127) according to different center. Clinicopathological data were collected and CT semantic features were evaluated. Multivariate logistic regression analyses were conducted to construct a clinical-semantic model predictive of STAS. The Swin Transformer architecture was adopted to develop a deep learning signature predictive of STA

SUBMITTER: Ma X 

PROVIDER: S-EPMC11751050 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature