Identification of Precise 3D CT Radiomics for Habitat Computation by Machine Learning in Cancer.

ABSTRACT: Purpose To identify precise three-dimensional radiomics features in CT images that enable computation of stable and biologically meaningful habitats with machine learning for cancer heterogeneity assessment. Materials and Methods This retrospective study included 2436 liver or lung lesions from 605 CT scans (November 2010-December 2021) in 331 patients with cancer (mean age, 64.5 years ± 10.1 [SD]; 185 male patients). Three-dimensional radiomics were computed from original and perturbed (simulated retest) images with different combinations of feature computation kernel radius and bin size. The lower 95% confidence limit (LCL) of the intraclass correlation coefficient (ICC) was used to measure repeatability and reproducibility. Precise features were identified by combining repeatability and

SUBMITTER: Prior O 

PROVIDER: S-EPMC10982821 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature