Distinguishing benign and malignant myxoid soft tissue tumors: Performance of radiomics vs. radiologists.

ABSTRACT:

Introduction

Benign and malignant myxoid soft tissue tumors have shared clinical, imaging, and histologic features that can make diagnosis challenging. The purpose of this study is comparison of the diagnostic performance of a radiomic based machine learning (ML) model to musculoskeletal radiologists.

Methods

Manual segmentation of 90 myxoid soft tissue tumors (45 myxomas and 45 myxofibrosarcomas) was performed on axial T1, and T2FS or STIR magnetic resonance imaging sequences. Eighty-seven radiomic features from each modality were extracted. Five ML models were trained to classify tumors as benign or malignant in 40 tumors and then tested with an additional 50 tumors using cross validation. The accuracy of the best ML model based on area under the receiver operating charact

SUBMITTER: Lawrenz JM 

PROVIDER: S-EPMC11771854 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature