Table Info

Table 2.

Performance comparison of radiologists and radiomics

Aim	Reference	Information database	Radiomic performances
WD-LPS vs. lipoma	Vos et al. [37]	MDM2 detection	Sensitivity: radiologist > radiomics Specificity: radiologist < radiomics Accuracy: radiologist = radiomics
LPS vs. lipoma	Malinauskaite et al. [26]	Histologic examination	Sensitivity: radiologist < radiomics Specificity: radiologist < radiomics Accuracy: radiologist < radiomics
ALT vs. lipoma	Tang et al. [40]	Histologic examination	Sensitivity: radiologist > radiomics Specificity: radiologist < radiomics Accuracy: radiologist < radiomics

Aim

Reference

Information database

Radiomic performances

WD-LPS vs. lipoma

Vos et al. [37]

MDM2 detection

Sensitivity: radiologist > radiomics

Specificity: radiologist < radiomics

Accuracy: radiologist = radiomics

LPS vs. lipoma

Malinauskaite et al. [26]

Histologic examination

Sensitivity: radiologist < radiomics

Specificity: radiologist < radiomics

Accuracy: radiologist < radiomics

ALT vs. lipoma

Tang et al. [40]

Histologic examination

Sensitivity: radiologist > radiomics

Specificity: radiologist < radiomics

Accuracy: radiologist < radiomics

Declarations

Author contributions

RN: Conceptualization, Data curation, Writing—original draft. MG and FF: Conceptualization, Resources. GV: Conceptualization, Methodology, Software, Formal analysis, Data curation, Writing—original draft, Writing—review & editing. MCB: Methodology, Data curation, Writing—original draft. AS: Software, Formal analysis, Writing—review & editing. MZ and LB: Validation, Supervision. VG and IS: Resources. MB: Data curation, Writing—original draft. All authors read and approved the submitted version.

Conflicts of interest

The authors declare that they have no conflicts of interest.

Ethical approval

Not applicable.

Consent to participate

Not applicable.

Consent to publication

Not applicable.

Availability of data and materials

Not applicable.

Funding

This work has been partially supported by MUR - REASONING: foRmal mEthods for computAtional analySis for diagnOsis and progNosis in imagING - PRIN. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright

References

IARC publication [Internet]. International agency for research on cancer: Lyon; c2023 [cited 2023 Feb 1]. Available from: https://publications.iarc.fr/

Shannon BA, Ahlawat S, Morris CD, Levin AS, Fayad LM. Do contrast-enhanced and advanced MRI sequences improve diagnostic accuracy for indeterminate lipomatous tumors? Radiol Med. 2022;127:90–9. [DOI] [PubMed]

Linee guida sarcomi dei tessuti mollie gist [Internet]. [cited 2023 Feb 1]. Available from: https://www.aiom.it/wp-content/uploads/2019/10/2019_LG_AIOM_Sarcomi-1.pdf

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

Rauh J, Klein A, Baur-Melnyk A, Knösel T, Lindner L, Roeder F, et al. The role of surgical margins in atypical lipomatous tumours of the extremities. BMC Musculoskelet Disord. 2018;19:152. [DOI] [PubMed] [PMC]

Moulin B, Messiou C, Crombe A, Kind M, Hohenberger P, Rutkowski P, et al. Diagnosis strategy of adipocytic soft-tissue tumors in adults: a consensus from European experts. Eur J Surg Oncol. 2022;48:518–25. [DOI] [PubMed]

Ballhause TM, Korthaus A, Jahnke M, Frosch KH, Yamamura J, Dust T, et al. Lipomatous tumors: a comparison of MRI-reported diagnosis with histological diagnosis. Diagnostics (Basel). 2022;12:1281. [DOI] [PubMed] [PMC]

Mashima E, Sawada Y, Nakamura M. Recent advancement in atypical lipomatous tumor research. Int J Mol Sci. 2021;22:994. [DOI] [PubMed] [PMC]

Creytens D. Molecular classification of soft tissue and bone tumors. Diagnostics (Basel). 2021;11:2326. [DOI] [PubMed] [PMC]

10.

Gronchi A, Miah AB, Dei Tos AP, Abecassis N, Bajpai J, Bauer S, et al. Soft tissue and visceral sarcomas: ESMO-EURACAN-GENTURIS Clinical Practice Guidelines for diagnosis, treatment and follow-up^☆. Ann Oncol. 2021;32:1348–65. [DOI] [PubMed]

11.

Saifuddin A, Andrei V, Rajakulasingam R, Oliveira I, Seddon B. Magnetic resonance imaging of trunk and extremity myxoid liposarcoma: diagnosis, staging, and response to treatment. Skeletal Radiol. 2021;50:1963–80. [DOI] [PubMed]

12.

Coran A, Ortolan P, Attar S, Alberioli E, Perissinotto E, Tosi AL, et al. Magnetic resonance imaging assessment of lipomatous soft-tissue tumors. In Vivo. 2017;31:387–95. [DOI] [PubMed] [PMC]

13.

Murphey MD, Carroll JF, Flemming DJ, Pope TL, Gannon FH, Kransdorf MJ. From the archives of the AFIP: benign musculoskeletal lipomatous lesions. Radiographics. 2004;24:1433–66. [DOI] [PubMed]

14.

Bjerregaard P, Hagen K, Daugaard S, Kofoed H. Intramuscular lipoma of the lower limb. Long-term follow-up after local resection. J Bone Joint Surg Br. 1989;71:812–5. [DOI] [PubMed]

15.

Thway K. Well-differentiated liposarcoma and dedifferentiated liposarcoma: an updated review. Semin Diagn Pathol. 2019;36:112–21. [DOI] [PubMed]

16.

Lee ATJ, Thway K, Huang PH, Jones RL. Clinical and molecular spectrum of liposarcoma. J Clin Oncol. 2018;36:151–9. [DOI] [PubMed] [PMC]

17.

Sommerville SMM, Patton JT, Luscombe JC, Mangham DC, Grimer RJ. Clinical outcomes of deep atypical lipomas (well-differentiated lipoma-like liposarcomas) of the extremities. ANZ J Surg. 2005;75:803–6. [DOI] [PubMed]

18.

Mavrogenis AF, Lesensky J, Romagnoli C, Alberghini M, Letson GD, Ruggieri P. Atypical lipomatous tumors/well-differentiated liposarcomas: clinical outcome of 67 patients. Orthopedics. 2011;34:e893–8. [DOI] [PubMed]

19.

Rozental TD, Khoury LD, Donthineni-Rao R, Lackman RD. Atypical lipomatous masses of the extremities: outcome of surgical treatment. Clin Orthop Relat Res. 2002;398:203–11. [DOI] [PubMed]

20.

Goldblum JR, Folpe AL, Weiss WS. Enzinger and Weiss’s soft tissue tumors. Philadelphia: Elsevier; 2014. pp. 1817–28.

21.

Kooby DA, Antonescu CR, Brennan MF, Singer S. Atypical lipomatous tumor/well-differentiated liposarcoma of the extremity and trunk wall: importance of histological subtype with treatment recommendations. Ann Surg Oncol. 2004;11:78–84. [DOI] [PubMed]

22.

Binh MBN, Sastre-Garau X, Guillou L, de Pinieux G, Terrier P, Lagacé R, et al. MDM2 and CDK4 immunostainings are useful adjuncts in diagnosing well-differentiated and dedifferentiated liposarcoma subtypes: a comparative analysis of 559 soft tissue neoplasms with genetic data. Am J Surg Pathol. 2005;29:1340–7. [DOI] [PubMed]

23.

Creytens D. What’s new in adipocytic neoplasia? Virchows Arch. 2020;476:29–39. [DOI] [PubMed]

24.

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. 2022;40:951–60. [DOI] [PubMed]

25.

Frapolli R, Bello E, Ponzo M, Craparotta I, Mannarino L, Ballabio S, et al. Combination of PPARγ agonist pioglitazone and trabectedin induce adipocyte differentiation to overcome trabectedin resistance in myxoid liposarcomas. Clin Cancer Res. 2019;25:7565–75. Erratum in: Clin Cancer Res. 2020;26:1199. [DOI] [PubMed]

26.

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;2020:7163453. [DOI] [PubMed] [PMC]

27.

Hong SH, Kim KA, Woo OH, Park CM, Kim CH, Kim MJ, et al. Dedifferentiated liposarcoma of retroperitoneum: spectrum of imaging findings in 15 patients. Clin Imaging. 2010;34:203–10. [DOI] [PubMed]

28.

Murphey MD, Arcara LK, Fanburg-Smith J. From the archives of the AFIP: imaging of musculoskeletal liposarcoma with radiologic-pathologic correlation. Radiographics. 2005;25:1371–95. [DOI] [PubMed]

29.

El Ouni F, Jemni H, Trabelsi A, Ben Maitig M, Arifa N, Ben Rhouma K, et al. Liposarcoma of the extremities: MR imaging features and their correlation with pathologic data. Orthop Traumatol Surg Res. 2010;96:876–83. [DOI] [PubMed]

30.

Löwenthal D, Zeile M, Niederhagen M, Fehlberg S, Schnapauff D, Pink D, et al. Differentiation of myxoid liposarcoma by magnetic resonance imaging: a histopathologic correlation. Acta Radiol. 2014;55:952–60. [DOI] [PubMed]

31.

Billing V, Mertens F, Domanski HA, Rydholm A. Deep-seated ordinary and atypical lipomas: histopathology, cytogenetics, clinical features, and outcome in 215 tumours of the extremity and trunk wall. J Bone Joint Surg Br. 2008;90-B:929–33. [DOI] [PubMed]

32.

Dalal KM, Antonescu CR, Singer S. Diagnosis and management of lipomatous tumors. J Surg Oncol. 2008;97:298–313. [DOI] [PubMed]

33.

Munk PL, Lee MJ, Janzen DL, Connell DG, Logan PM, Poon PY, et al. Lipoma and liposarcoma: evaluation using CT and MR imaging. AJR Am J Roentgenol. 1997;169:589–94. [DOI] [PubMed]

34.

Pitson G, Robinson P, Wilke D, Kandel RA, White L, Griffin AM, et al. Radiation response: an additional unique signature of myxoid liposarcoma. Int J Radiat Oncol Biol Phys. 2004;60:522–6. [DOI] [PubMed]

35.

Kilpatrick SE, Doyon J, Choong PFM, Sim FH, Nascimento AG. The clinicopathologic spectrum of myxoid and round cell liposarcoma: a study of 95 cases. Cancer. 1996;77:1450–8. [DOI] [PubMed]

36.

Bettinelli A, Branchini M, De Monte F, Scaggion A, Paiusco M. Technical note: an IBEX adaption toward image biomarker standardization. Med Phys. 2020;47:1167–73. [DOI] [PubMed]

37.

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. [DOI] [PubMed] [PMC]

38.

Starmans MPA, Timbergen MJM, Vos M, Padmos GA, Grünhagen DJ, Verhoef C, et al. The WORC database: MRI and CT scans, segmentations, and clinical labels for 930 patients from six radiomics studies. medRxiv [Preprint]. 2021 [cited 2023 Feb 1]. Available from: https://www.medrxiv.org/content/10.1101/2021.08.19.21262238v1

39.

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. [DOI] [PubMed] [PMC]

40.

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;56:1746–54. [DOI] [PubMed]

41.

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. [DOI] [PubMed]

42.

Chatwin C, Young R, Ganeshan B. TexRad-feedback plc - cancer management imaging software. Brighton (United Kingdom): University of Sussex, Industrial Informatics & Signal Processing Research Group; 2015.

43.

Zwanenburg A, Vallières M, Abdalah MA, Aerts HJWL, Andrearczyk V, Apte A, et al. The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology. 2020;295:328–38. [DOI] [PubMed] [PMC]

44.

Yang Y, Zhou Y, Zhou C, Ma X. Novel computer aided diagnostic models on multimodality medical images to differentiate well differentiated liposarcomas from lipomas approached by deep learning methods. Orphanet J Rare Dis. 2022;17:158. [DOI] [PubMed] [PMC]

45.

Yushkevich PA, Gerig G. ITK-SNAP: an intractive medical image segmentation tool to meet the need for expert-guided segmentation of complex medical images. IEEE Pulse. 2017;8:54–7. [DOI] [PubMed]