Discrimination of lipoma from atypical lipomatous tumor/well-differentiated liposarcoma using magnetic resonance imaging radiomics combined with machine learning
Japanese Journal of Radiology, cilt.40, sa.9, ss.951-960, 2022 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 40 Sayı: 9
- Basım Tarihi: 2022
- Doi Numarası: 10.1007/s11604-022-01278-x
- Dergi Adı: Japanese Journal of Radiology
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Agricultural & Environmental Science Database, Biotechnology Research Abstracts, CINAHL, EMBASE, MEDLINE
- Sayfa Sayıları: ss.951-960
- Anahtar Kelimeler: Atypical lipomatous tumor, Lipoma, Machine learning, MRI, Radiomics, Well-differentiated liposarcoma
- Ankara Yıldırım Beyazıt Üniversitesi Adresli: Evet
Özet
© 2022, The Author(s) under exclusive licence to Japan Radiological Society.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.