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Differentiation Between Lipomas and Atypical Lipomatous Tumors of the Extremities Using Radiomics
Yaozhou Tang1, Jingjing Cui2, Jingyi Zhu1
1Department of Radiology, The First Affiliated Hospital, China Medical University, Shenyang, Liaoning, China.
Background:
The differentiation of soft tissue lipomas from atypical lipoma tumors (ALTs) of the extremities is important because of the distinction of the cytogenetic profiles and the treatment decisions.
Purpose:
To investigate a radiomics method to differentiate between lipomas and ALTs of the extremities.
Study Type:
Retrospective.
Population:
Imaging data of 122 patients including 90 cases of lipomas and 32 cases of ALTs.
Field Strength/Sequence:
Axial T1-weighted imaging and fat suppressed T2-weighted imaging at 3.0T MRI.
Assessment:
Analysis of variance and the least absolute shrinkage and selection operator methods were used for feature selection and the random forest method was used to build three radiomics models based on T1WI, FS T2WI, and their combination (T1&T2WI). Three independent radiologists classified the tumors based on the subjective assessments.
Statistical Tests:
The area under the curve (AUC) of the receiver operating characteristic curve, accuracy, F1-score, specificity, and sensitivity were employed. The differences of the classifiers and discriminating ability of the radiologists and the radiomics model were compared by Delong test. A P value <0.05 was considered significant. Kappa test was used to determine the inter-reader agreements between the radiologists.
Result:
The AUCs were 0.952 (95% confidence interval [CI]: 0.785-0.998), 0.944 (95% CI: 0.774-0.997), and 0.968 (95% CI: 0.809-1) for T1WI, FS T2WI, and T1&T2WI models in testing sets respectively. Delong test showed there were no significant difference between the different radiomics models (P > 0.05). The AUCs of the radiologists were 0.893 (95% CI: 0.824-0.942), 0.831 (95% CI: 0.752-0.893), and 0.893 (95% CI: 0.824-0.94), respectively. There were significant difference between radiomics model and radiologists' model in the training and entire cohorts (P < 0.05) while there were no significant difference in the testing sets (P > 0.05).
Data Conclusion:
Radiomics has the potential to distinguish between lipomas and ALTs of the extremities and their discrimination ability is no weaker than the senor radiologists.
Level Of Evidence:
3 TECHNICAL EFFICACY STAGE: 2.
