MRI radiomics-based machine learning for classification of deep-seated lipoma and atypical lipomatous tumor of the extremities.

ABSTRACT:

Purpose

To determine diagnostic performance of MRI radiomics-based machine learning for classification of deep-seated lipoma and atypical lipomatous tumor (ALT) of the extremities.

Material and methods

This retrospective study was performed at three tertiary sarcoma centers and included 150 patients with surgically treated and histology-proven lesions. The training-validation cohort consisted of 114 patients from centers 1 and 2 (n = 64 lipoma, n = 50 ALT). The external test cohort consisted of 36 patients from center 3 (n = 24 lipoma, n = 12 ALT). 3D segmentation was manually performed on T1- and T2-weighted MRI. After extraction and selection of radiomic features, three machine learning classifiers were trained and validated using nested fivefold cross-validation. The best

SUBMITTER: Gitto S 

PROVIDER: S-EPMC10338387 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature