Radiomics-based machine learning (ML) classifier for detection of type 2 diabetes on standard-of-care abdomen CTs: a proof-of-concept study.

ABSTRACT:

Purpose

To determine if pancreas radiomics-based AI model can detect the CT imaging signature of type 2 diabetes (T2D).

Methods

Total 107 radiomic features were extracted from volumetrically segmented normal pancreas in 422 T2D patients and 456 age-matched controls. Dataset was randomly split into training (300 T2D, 300 control CTs) and test subsets (122 T2D, 156 control CTs). An XGBoost model trained on 10 features selected through top-K-based selection method and optimized through threefold cross-validation on training subset was evaluated on test subset.

Results

Model correctly classified 73 (60%) T2D patients and 96 (62%) controls yielding F1-score, sensitivity, specificity, precision, and AUC of 0.57, 0.62, 0.61, 0.55, and 0.65, respectively. Model's performance

SUBMITTER: Wright DE 

PROVIDER: S-EPMC12315806 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature