Radiomics is the extraction of predefined mathematic features from medical images for
predicting variables of clinical interest. Recent research has demonstrated that radiomics …

This narrative review focuses on clinical applications of artificial intelligence (AI) in
musculoskeletal imaging. A range of musculoskeletal disorders are discussed using a …

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 …

Objective To systematically review radiomic feature reproducibility and model validation
strategies in recent studies dealing with CT and MRI radiomics of bone and soft-tissue …

Background Atypical cartilaginous tumour (ACT) and high-grade chondrosarcoma (CS) of
long bones are respectively managed with active surveillance or curettage and wide …

Objectives To develop and validate a multiparametric MRI–based radiomics model with
optimal oversampling and machine learning techniques for predicting human papillomavirus …

Hersh Sagreiya, MD, is an assistant professor of radiology at the University of Pennsylvania.
He performs clinical work in abdominal imaging and research in machine learning and …

Objective No consensus exists for tumor volume response criteria in patients with Ewing
sarcoma. This study aimed to identify an optimal cutoff for predicting a good histological …

Purpose: To investigate the predictive performance of radiomic features extracted from
breast MRI for upgrade of ductal carcinoma in situ (DCIS) to invasive carcinoma. Methods …

Sarcomas are heterogeneous rare tumors predominantly affecting the musculoskeletal
(MSK) system. Due to significant variations in their natural history and variable response to …