Radiomics-based decision support tool assists radiologists in small lung nodule classification and improves lung cancer early diagnosis.

ABSTRACT:

Background

Methods to improve stratification of small (≤15 mm) lung nodules are needed. We aimed to develop a radiomics model to assist lung cancer diagnosis.

Methods

Patients were retrospectively identified using health records from January 2007 to December 2018. The external test set was obtained from the national LIBRA study and a prospective Lung Cancer Screening programme. Radiomics features were extracted from multi-region CT segmentations using TexLab2.0. LASSO regression generated the 5-feature small nodule radiomics-predictive-vector (SN-RPV). K-means clustering was used to split patients into risk groups according to SN-RPV. Model performance was compared to 6 thoracic radiologists. SN-RPV and radiologist risk groups were combined to generate "Safety-Net" and "Earl

SUBMITTER: Hunter B 

PROVIDER: S-EPMC10703918 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature