Imaging phenotyping using ¹⁸F-FDG PET/CT radiomics to predict micropapillary and solid pattern in lung adenocarcinoma.

ABSTRACT:

Objectives

To develop and validate a machine learning model using ¹⁸F-FDG PET/CT radiomics signature and clinical features to predict the presence of micropapillary and solid (MP/S) components in lung adenocarcinoma.

Methods

Eight hundred and forty-six patients who underwent preoperative PET/CT with pathologically confirmed adenocarcinoma were enrolled. After segmentation, 1688 radiomics features were extracted from PET/CT and selected to construct predictive models. Then, we developed a nomogram based on PET/CT radiomics integrated with clinical features. Receiver operating curves, calibration curves, and decision curve analysis (DCA) were performed for diagnostics assessment and test of the developed models for distinguishing patients with MP/S components from t

SUBMITTER: Zhou L 

PROVIDER: S-EPMC10772036 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature