Radiomics and machine learning–based methods offer exciting opportunities for improving
diagnostic performance and efficiency in musculoskeletal radiology for various tasks …

The WHO classification of soft tissue tumours categorizes the lesions based on histological
differentiation. In the recent fifth edition (2020), many new entities have been introduced …

This article proposes a summary of the current status of the research regarding the use of
radiomics and artificial intelligence to improve the radiological assessment of patients with …

Objectives This systematic review and meta-analysis evaluated the diagnostic accuracy of
CT and MRI for differentiating atypical lipomatous tumors and malignant liposarcomas from …

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 …

Fat-containing tumors are very commonly found in daily practice with benign lipoma
accounting for the majority of superficial tumors. Overlap in imaging findings between …

Simple Summary Differentiating atypical lipomatous tumors from lipomas on MR images is a
challenging task due to similar imaging characteristics. Given these challenges, it would be …

After Evans and colleagues identified the lipomatous tumor with a well-differentiated
liposarcoma in a subcutaneous location or within a muscle layer, namely, atypical …

Aims This pilot study aims to determine if tumour heterogeneity assessed using magnetic
resonance imaging (MRI) radiomics-based texture analysis (TA) can differentiate between …

Objective: This study aimed to evaluate the efficacy of using MRI findings to differentiate
superficial spindle cell lipomas (SCLs) from atypical lipomatous tumor/well-differentiated …