Radiomics analysis to predict pulmonary nodule malignancy using machine learning approaches.

ABSTRACT:

Background

Low-dose CT screening can reduce lung cancer-related mortality. However, most screen-detected pulmonary abnormalities do not develop into cancer and it often remains challenging to identify malignant nodules, particularly among indeterminate nodules. We aimed to develop and assess prediction models based on radiological features to discriminate between benign and malignant pulmonary lesions detected on a baseline screen.

Methods

Using four international lung cancer screening studies, we extracted 2060 radiomic features for each of 16 797 nodules (513 malignant) among 6865 participants. After filtering out low-quality radiomic features, 642 radiomic and 9 epidemiological features remained for model development. We used cross-validation and grid search to assess thr

SUBMITTER: Warkentin MT 

PROVIDER: S-EPMC10947877 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature