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Differentiating leiomyosarcoma from leiomyoma: in support of an MR imaging predictive scoring system

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Abstract

Purpose

The purpose of this study was to determine the Magnetic Resonance (MR) imaging features that best differentiate leiomyosarcoma (LMS) from leiomyoma, and to explore a scoring system to preoperatively identify those at highest risk of having LMS.

Methods

Our Institutional Review Board approved this retrospective HIPAA-compliant study with a waiver for written informed consent. Institutional Research Patient Data Registry identified patients with histopathologically-proven LMS (n = 19) or leiomyoma (n = 25) and a pelvic MRI within six months prior to surgery. Qualitative differentiating MRI features were selected based on prior publications and clinical experience. Patient and MRI characteristics for leiomyomas versus LMS were compared using Wilcoxon rank-sum tests or Fisher’s exact tests and using a basic classification tree. Hypothesis testing was two-tailed, with a p value < 0.001 used to determine inclusion of variables into an MR imaging predictive (MRP) score. Diagnostic performance [sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV)] of the MRP in diagnosis of LMS used all possible scores as cutoffs.

Results

Seven out of 15 MRI features were found to have an association with LMS. The final MRP scores ranged from 0 to 7: a score of 0–3 was associated with 100% NPV for LMS, and a MRP score of 6–7 with 100% PPV for LMS.

Conclusion

Seven qualitative MR imaging features, extracted from a standard MR imaging protocol, allow differentiation of LMS from leiomyoma. An exploratory risk stratification MRP score can be used to determine the likelihood of LMS being present.

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The Jill Effect (SG).

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Authors and Affiliations

  1. Department of Radiology, Harvard Medical School, Brigham and Women’s Hospital, 75 Francis Street, Boston, MA, 02115, USA

    Jyothi P. Jagannathan, Camden Bay & Fiona M. Fennessy

  2. Department of Radiology, Turku University Hospital, University of Turku, Kiinamyllynkatu 4-8, 20521, Turku, Finland

    Aida Steiner

  3. Department of Medicine/Biostatistics, Harvard Medical School, Brigham and Women’s Hospital, 75 Francis Street, Boston, MA, 02115, USA

    Camden Bay

  4. Department of Gynecologic Oncology, Harvard Medical School, Massachusetts General Hospital, 55 Fruit St, Boston, MA, 02214, USA

    Eric Eisenhauer

  5. Division of Surgical Oncology, Harvard Medical School, Dana Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, 02215, USA

    Michael G. Muto

  6. Department of Medical Oncology, Harvard Medical School, Dana Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, 02215, USA

    Suzanne George

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  1. Jyothi P. Jagannathan
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Correspondence to Fiona M. Fennessy.

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Jagannathan, J.P., Steiner, A., Bay, C. et al. Differentiating leiomyosarcoma from leiomyoma: in support of an MR imaging predictive scoring system. Abdom Radiol 46, 4927–4935 (2021). https://doi.org/10.1007/s00261-021-03132-6

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  • DOI: https://doi.org/10.1007/s00261-021-03132-6

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