CT radiomics-based machine learning classification of atypical cartilaginous tumours and appendicular chondrosarcomas.

ABSTRACT:

Background

Clinical management ranges from surveillance or curettage to wide resection for atypical to higher-grade cartilaginous tumours, respectively. Our aim was to investigate the performance of computed tomography (CT) radiomics-based machine learning for classification of atypical cartilaginous tumours and higher-grade chondrosarcomas of long bones.

Methods

One-hundred-twenty patients with histology-proven lesions were retrospectively included. The training cohort consisted of 84 CT scans from centre 1 (n=55 G1 or atypical cartilaginous tumours; n=29 G2-G4 chondrosarcomas). The external test cohort consisted of the CT component of 36 positron emission tomography-CT scans from centre 2 (n=16 G1 or atypical cartilaginous tumours; n=20 G2-G4 chondrosarcomas). Bidimensiona

SUBMITTER: Gitto S 

PROVIDER: S-EPMC8170113 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature