Development and validation of a computed tomography-based radiomics-clinical model to preoperatively predict high-grade patterns within lung invasive adenocarcinoma.

ABSTRACT:

Background

Lung cancer remains a leading cause of cancer mortality globally, with non-small cell lung cancer (nSCLC), particularly invasive adenocarcinoma (IAC), being predominant. Within IAC, high-grade patterns (HGPs) are strongly linked to aggressive behavior and poor prognosis. Unfortunately, preoperative identification of HGPs is challenging: biopsy (histopathology gold standard) suffers from sampling limitations, while conventional computed tomography (CT) lacks sensitivity. Radiomics offers promise by extracting quantitative features from CT images. This study aimed to develop and validate a CT-based radiomics-clinical integrated model for preoperative prediction of HGPs in IAC patients, thereby guiding optimal surgical decision-making.

Methods

A total of 278 patients

SUBMITTER: Zhang X 

PROVIDER: S-EPMC12433077 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature