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Radiomics-based approaches outperform visual analysis for differentiating lipoma from atypical lipomatous tumors: a
Jordan Haidey1, Gavin Low2, Mitchell P Wilson2
1Department of Radiology and Diagnostic Imaging, University of Alberta, 2B2.41 WMC, 8440-112 Street NW, Edmonton, Alberta, T6G 2B7, Canada. jhaidey@ualberta.ca.
Background:
Differentiating atypical lipomatous tumors (ALTs) and well-differentiated liposarcomas (WDLs) from benign lipomatous lesions is important for guiding clinical management, though conventional visual analysis of these lesions is challenging due to overlap of imaging features. Radiomics-based approaches may serve as a promising alternative and/or supplementary diagnostic approach to conventional imaging.
Purpose:
The purpose of this study is to review the practice of radiomics-based imaging and systematically evaluate the literature available for studies evaluating radiomics applied to differentiating ALTs/WDLs from benign lipomas.
Review:
A background review of the radiomic workflow is provided, outlining the steps of image acquisition, segmentation, feature extraction, and model development. Subsequently, a systematic review of MEDLINE, EMBASE, Scopus, the Cochrane Library, and the grey literature was performed from inception to June 2022 to identify size studies using radiomics for differentiating ALTs/WDLs from benign lipomas. Radiomic models were shown to outperform conventional analysis in all but one model with a sensitivity ranging from 68 to 100% and a specificity ranging from 84 to 100%. However, current approaches rely on user input and no studies used a fully automated method for segmentation, contributing to interobserver variability and decreasing time efficiency.
Conclusion:
Radiomic models may show improved performance for differentiating ALTs/WDLs from benign lipomas compared to conventional analysis. However, considerable variability between radiomic approaches exists and future studies evaluating a standardized radiomic model with a multi-institutional study design and preferably fully automated segmentation software are needed before clinical application can be more broadly considered.
