Multimodal deep learning radiomics model for predicting postoperative progression in solid stage I non-small cell lung cancer.

ABSTRACT:

Purpose

To explore the application value of a multimodal deep learning radiomics (MDLR) model in predicting the risk status of postoperative progression in solid stage I non-small cell lung cancer (NSCLC).

Materials and methods

A total of 459 patients with histologically confirmed solid stage I NSCLC who underwent surgical resection in our institution from January 2014 to September 2019 were reviewed retrospectively. At another medical center, 104 patients were reviewed as an external validation cohort according to the same criteria. A univariate analysis was conducted on the clinicopathological characteristics and subjective CT findings of the progression and non-progression groups. The clinicopathological characteristics and subjective CT findings that exhibited significan

SUBMITTER: Kuang Q 

PROVIDER: S-EPMC11487701 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature