Discrimination of lipoma from atypical lipomatous tumor/well-differentiated liposarcoma using magnetic resonance imaging radiomics combined with machine learning

• Published in: Japanese journal of radiology Vol. 40; no. 9; pp. 951 - 960
• Main Authors: Cay, Nurdan, Mendi, Bokebatur Ahmet Rasit, Batur, Halitcan, Erdogan, Fazli
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.09.2022; Springer Nature B.V
• Subjects: Auditory discrimination; Biopsy; Diagnostic systems; Discrimination; Imaging; Learning algorithms; Lipoma; Machine learning; Magnetic resonance imaging; MDM2 protein; Medical imaging; Medicine; Medicine & Public Health; Nuclear Medicine; Original Article; Parameters; Radiology; Radiomics; Radiotherapy; Reproducibility; Support vector machines; Tumors; Atypical lipomatous tumor; Lipoma; Well-differentiated liposarcoma; MRI; Machine learning; Radiomics
• ISSN: 1867-1071; 1867-108X; 1867-108X
• DOI: 10.1007/s11604-022-01278-x

Purpose To evaluate the diagnostic capability of radiomics in distinguishing lipoma and Atypic Lipomatous Tumors/Well-Differentiated Liposarcomas (ALT/WDL) with Magnetic Resonance Imaging (MRI). Materials and methods Patients with a histopathologic diagnosis of lipoma ( n  = 45) and ALT/WDL ( n  = 20), who had undergone pre-surgery or pre-biopsy MRI, were enrolled. The MDM2 amplification was accepted as gold-standard test. The T1-weighted turbo spin echo images were used for radiomics analysis. Utility of a predefined standardized imaging protocol and a single type of 1.5 T scanner were sought as inclusion criteria. Radiomics parameters that show a certain level of reproducibility were included in the study and supplied to Support Vector Machine (SVM) as a machine learning method. Results No significant difference was found in terms of gender, location and age between the lipoma and ALT/WDL groups. Sixty-five parameters were accepted as reproducible. Fifty-seven parameters were able to distinguish the two groups significantly (AUC range 0.564–0.902). Diagnostic performance of the SVM was one of the highest among literature findings: sensitivity = 96.8% (95% CI 94.03–98.39%), specificity = 93.72% (95% CI 86.36–97.73%) and AUC = 0.987 (95% CI 0.972–0.999). Conclusion Although radiomics has been proven to be useful in previous literature regarding discrimination of lipomas and ALT/WDLs, we found that its accuracy could further be improved with utility of standardized hardware, imaging protocols and incorporation of machine learning methods.