Bone metastasis prediction in non-small-cell lung cancer: primary CT-based radiomics signature and clinical feature.

ABSTRACT:

Background

Radiomics provided opportunities to quantify the tumor phenotype non-invasively. This study extracted contrast-enhanced computed tomography (CECT) radiomic signatures and evaluated clinical features of bone metastasis in non-small-cell lung cancer (NSCLC). With the combination of the revealed radiomics and clinical features, the predictive modeling on bone metastasis in NSCLC was established.

Methods

A total of 318 patients with NSCLC at the Tianjin Medical University Cancer Institute & Hospital was enrolled between January 2009 and December 2019, which included a feature-learning cohort (n = 223) and a validation cohort (n = 95). We trained a radiomics model in 318 CECT images from feature-learning cohort to extract the radiomics features of bone metastasis in NS

SUBMITTER: Liu Z 

PROVIDER: S-EPMC11299297 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature