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Radiomics-based approaches outperform visual analysis for differentiating lipoma from atypical lipomatous tumors: a review

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Abstract

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.

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References

  1. WHO Classification of Tumours Editorial Board. WHO Classification of tumours: soft tissue and bone tumours. International Agency for Research on Cancer. 2020.

  2. Johnson CN, Ha AS, Chen E, Davidson D. Lipomatous soft-tissue tumors: J Am Acad Orthop Surg. 2018;26:779–88.

    PubMed  Google Scholar 

  3. Weaver J, Downs-Kelly E, Goldblum JR, Turner S, Kulkarni S, Tubbs RR et al. Fluorescence in situ hybridization for MDM2 gene amplification as a diagnostic tool in lipomatous neoplasms. Mod Pathol Off J U S Can Acad Pathol Inc. 2008;21:943–9.

  4. Nagano S, Yokouchi M, Setoguchi T, Ishidou Y, Sasaki H, Shimada H, et al. Differentiation of lipoma and atypical lipomatous tumor by a scoring system: implication of increased vascularity on pathogenesis of liposarcoma. BMC Musculoskelet Disord. 2015;16:36.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Asano Y, Miwa S, Yamamoto N, Hayashi K, Takeuchi A, Igarashi K, et al. A scoring system combining clinical, radiological, and histopathological examinations for differential diagnosis between lipoma and atypical lipomatous tumor/well-differentiated liposarcoma. Sci Rep Nature Publishing Group. 2022;12:237.

    Google Scholar 

  6. Brisson M, Kashima T, Delaney D, Tirabosco R, Clarke A, Cro S, et al. MRI characteristics of lipoma and atypical lipomatous tumor/well-differentiated liposarcoma: retrospective comparison with histology and MDM2 gene amplification. Skeletal Radiol. 2013;42:635–47.

    Article  PubMed  Google Scholar 

  7. O’Donnell PW, Griffin AM, Eward WC, Sternheim A, White LM, Wunder JS, et al. Can experienced observers differentiate between lipoma and well-differentiated liposarcoma using only MRI? Sarcoma. 2013;2013:982784.

    PubMed  PubMed Central  Google Scholar 

  8. Malinauskaite I, Hofmeister J, Burgermeister S, Neroladaki A, Hamard M, Montet X, et al. Radiomics and machine learning differentiate soft-tissue lipoma and liposarcoma better than musculoskeletal radiologists. Sarcoma. 2020;2020:1–9.

    Article  Google Scholar 

  9. Tomaszewski MR, Gillies RJ. The biological meaning of radiomic features. Radiology. 2021;298:505–16.

    Article  PubMed  Google Scholar 

  10. Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology. 2016;278:563–77.

    Article  PubMed  Google Scholar 

  11. Larue RTHM, Defraene G, De Ruysscher D, Lambin P, van Elmpt W. Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures. Br J Radiol. 2017;90:20160665.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Kinahan PE, Perlman ES, Sunderland JJ, Subramaniam R, Wollenweber SD, Turkington TG, et al. The QIBA profile for FDG PET/CT as an imaging biomarker measuring response to cancer therapy. Radiology. 2020;294:647–57.

    Article  PubMed  Google Scholar 

  13. Predictive modeling, machine learning, and statistical issues [Internet]. Radiomics Radiogenomics. Chapman and Hall/CRC; 2019 [cited 2022 Jul 3]. p. 151–68. Available from: https://www.taylorfrancis.com/chapters/edit/10.1201/9781351208277-9/predictive-modeling-machine-learning-statistical-issues-panagiotis-korfiatis-timothy-kline-zeynettin-akkus-kenneth-philbrick-bradley-erickson 

  14. Shur JD, Doran SJ, Kumar S, ap Dafydd D, Downey K, O’Connor JPB, et al. Radiomics in oncology: A practical guide. RadioGraphics. 2021;41:1717–32 (Radiological Society of North America)

  15. Parmar C, Velazquez ER, Leijenaar R, Jermoumi M, Carvalho S, Mak RH, et al. Robust radiomics feature quantification using semiautomatic volumetric segmentation. PLOS One. 2014;9:e102107.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Bera K, Braman N, Gupta A, Velcheti V, Madabhushi A. Predicting cancer outcomes with radiomics and artificial intelligence in radiology. Nat Rev Clin Oncol. 2022;19:132–46.

    Article  CAS  PubMed  Google Scholar 

  17. Hosny A, Aerts HJ, Mak RH. Handcrafted versus deep learning radiomics for prediction of cancer therapy response. Lancet Digit Health Elsevier. 2019;1:e106–7.

    Article  Google Scholar 

  18. Gebejes A, Huertas R. Texture characterization based on grey-level co-occurrence matrix. Proc Conf Inform Manag Sci. 2013;3:375–378.

  19. van Timmeren JE, Cester D, Tanadini-Lang S, Alkadhi H, Baessler B. Radiomics in medical imaging—“how-to” guide and critical reflection. Insights Imaging. 2020;11:91.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Park JE, Park SY, Kim HJ, Kim HS. Reproducibility and generalizability in radiomics modeling: possible strategies in radiologic and statistical perspectives. Korean J Radiol. 2019;20:1124–37.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Zwanenburg A, Leger S, Agolli L, Pilz K, Troost EGC, Richter C, et al. Assessing robustness of radiomic features by image perturbation. Sci Rep. 2019;9:614.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Leporq B, Bouhamama A, Pilleul F, Lame F, Bihane C, Sdika M, et al. MRI-based radiomics to predict lipomatous soft tissue tumors malignancy: a pilot study. Cancer Imaging. 2020;20:78.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Cay N, Mendi BAR, Batur H, Erdogan F. Discrimination of lipoma from atypical lipomatous tumor/well-differentiated liposarcoma using magnetic resonance imaging radiomics combined with machine learning. Jpn J Radiol [Internet]. 2022 [cited 2022 Jun 7]; Available from: https://link.springer.com/10.1007/s11604-022-01278-x

  24. Thornhill RE, Golfam M, Sheikh A, Cron GO, White EA, Werier J, et al. Differentiation of lipoma from liposarcoma on MRI using texture and shape analysis. Acad Radiol. 2014;21:1185–94.

    Article  PubMed  Google Scholar 

  25. Vos M, Starmans MPA, Timbergen MJM, van der Voort SR, Padmos GA, Kessels W, et al. Radiomics approach to distinguish between well differentiated liposarcomas and lipomas on MRI. Br J Surg. 2019;106:1800–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Tang Y, Cui J, Zhu J, Fan G. Differentiation between lipomas and atypical lipomatous tumors of the extremities using radiomics. J Magn Reson Imaging. 2022;56:1746–1754.

  27. Pressney I, Khoo M, Endozo R, Ganeshan B, O’Donnell P. Pilot study to differentiate lipoma from atypical lipomatous tumour/well-differentiated liposarcoma using MR radiomics-based texture analysis. Skeletal Radiol. 2020;49:1719–29.

    Article  PubMed  Google Scholar 

  28. Kalpathy-Cramer J, Mamomov A, Zhao B, Lu L, Cherezov D, Napel S, et al. Radiomics of lung nodules: a multi-institutional study of robustness and agreement of quantitative imaging features. Tomography. 2016;2:430–7.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Bleker J, Kwee TC, Rouw D, Roest C, Borstlap J, de Jong IJ, et al. A deep learning masked segmentation alternative to manual segmentation in biparametric MRI prostate cancer radiomics. Eur Radiol. 2022;32:6526–35.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Chen MY, Woodruff MA, Dasgupta P, Rukin NJ. Variability in accuracy of prostate cancer segmentation among radiologists, urologists, and scientists. Cancer Med. 2020;9:7172–82.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Gitto S, Cuocolo R, Albano D, Morelli F, Pescatori LC, Messina C, et al. CT and MRI radiomics of bone and soft-tissue sarcomas: a systematic review of reproducibility and validation strategies. Insights Imaging. 2021;12:68.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Wang B, Lei Y, Tian S, Wang T, Liu Y, Patel P, et al. Deeply supervised 3D fully convolutional networks with group dilated convolution for automatic MRI prostate segmentation. Med Phys. 2019;46:1707–18.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Ushinsky A, Bardis M, Glavis-Bloom J, Uchio E, Chantaduly C, Nguyentat M, et al. A 3D–2D hybrid U-Net convolutional neural network approach to prostate organ segmentation of multiparametric MRI. AJR Am J Roentgenol. 2021;216:111–6.

    Article  PubMed  Google Scholar 

  34. Fradet G, Ayde R, Bottois H, El Harchaoui M, Khaled W, Drapé J-L, et al. Prediction of lipomatous soft tissue malignancy on MRI: comparison between machine learning applied to radiomics and deep learning. Eur Radiol Exp. 2022;6:41.

    Article  PubMed  PubMed Central  Google Scholar 

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Authors and Affiliations

  1. Department of Radiology and Diagnostic Imaging, University of Alberta, 2B2.41 WMC, 8440-112 Street NW, Edmonton, Alberta, T6G 2B7, Canada

    Jordan Haidey, Gavin Low & Mitchell P. Wilson

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  1. Jordan Haidey
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Haidey, J., Low, G. & Wilson, M.P. Radiomics-based approaches outperform visual analysis for differentiating lipoma from atypical lipomatous tumors: a review. Skeletal Radiol 52, 1089–1100 (2023). https://doi.org/10.1007/s00256-022-04232-0

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  • DOI: https://doi.org/10.1007/s00256-022-04232-0

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