Cycle-consistent adversarial networks improves generalizability of radiomics model in grading meningiomas on external validation.

ABSTRACT: The heterogeneity of MRI is one of the major reasons for decreased performance of a radiomics model on external validation, limiting the model's generalizability and clinical application. We aimed to establish a generalizable radiomics model to predict meningioma grade on external validation through leveraging Cycle-Consistent Adversarial Networks (CycleGAN). In this retrospective study, 257 patients with meningioma were included in the institutional training set. Radiomic features (n = 214) were extracted from T2-weighted (T2) and contrast-enhanced T1 (T1C) images. After radiomics feature selection, extreme gradient boosting classifiers were developed. The models were validated in the external validation set consisting of 61 patients with meningiomas. To reduce the gap in generalization a

SUBMITTER: Park YW 

PROVIDER: S-EPMC9055063 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature