A radiomics-based decision support tool improves lung cancer diagnosis in combination with the Herder score in large lung nodules.

ABSTRACT:

Background

Large lung nodules (≥15 mm) have the highest risk of malignancy, and may exhibit important differences in phenotypic or clinical characteristics to their smaller counterparts. Existing risk models do not stratify large nodules well. We aimed to develop and validate an integrated segmentation and classification pipeline, incorporating deep-learning and traditional radiomics, to classify large lung nodules according to cancer risk.

Methods

502 patients from five U.K. centres were recruited to the large-nodule arm of the retrospective LIBRA study between July 2020 and April 2022. 838 CT scans were used for model development, split into training and test sets (70% and 30% respectively). An nnUNet model was trained to automate lung nodule segmentation. A radiomics sign

SUBMITTER: Hunter B 

PROVIDER: S-EPMC9664396 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature