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Original research
Validating the diagnostic accuracy of an MRI-based scoring system for differentiating benign uterine leiomyomas from leiomyosarcomas
  1. Maryam Al Khuri1,2,
  2. Ishaq Al Salmi3,
  3. Hawra Al Ajmi1,
  4. Aymen Al Hadidi4,
  5. Abdullah Alabousi2,5,
  6. Ehsan Haider2,5,
  7. Pooja Vasudev6,7,
  8. Ahmed Al Salmi8,
  9. Sachin Jose9 and
  10. Nasser Alrahbi10
  1. 1Radiology Department, Sohar Hospital, Sohar, Al Batinah North, Oman
  2. 2Department of Medical Imaging, McMaster University, Hamilton, Canada
  3. 3Radiology Department, The Royal Hospital, Seeb, Muscat, Oman
  4. 4Radiology Department, Khoula Hospital, Mina Al Fahal, Muscat, Oman
  5. 5Diagnostic Imaging, St Joseph's Healthcare Hamilton, Hamilton, Canada
  6. 6Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Canada
  7. 7St Joseph's Healthcare Hamilton, Hamilton, Canada
  8. 8Radiology Department, Rustaq Hospital, Rustaq, Al Batinah South, Oman
  9. 9Research and Studies Department, Oman Medical Speciality Board, Al-Athaiba, Muscat, Oman
  10. 10Histopathology Department, The Royal Hospital, Seeb, Muscat, Oman
    1. Correspondence to Dr Maryam Al Khuri, Radiology Department, Sohar Hospital, Sohar, Al Batinah North, Oman; maaalkhoori{at}gmail.com

    Abstract

    Objective Uterine leiomyomas are the most common benign uterine tumors. They are difficult to distinguish from their malignant counterparts—smooth muscle tumors of unknown malignant potential (STUMP) and leiomyosarcoma. The purpose of this study is to propose and validate the diagnostic accuracy of the MRI-based Oman-Canada Scoring System of Myometrial Masses (OCSSMM) to differentiate uterine leiomyomas from STUMP/leiomyosarcomas.

    Methods This is a retrospective study performed at two tertiary care centers. All patients with a pathology-proven uterine mass who underwent pre-operative pelvic MRI between January 2010 and January 2020 were included. Using a 1.5T MRI machine, sequences included were axial/coronal/sagittal T2 and T1 weighted imaging, axial diffusion weighted and apparent diffusion coefficient map, and axial or sagittal dynamic contrast-enhanced sequences. A scoring system was designed based on previously published worrisome MRI features for uterine leiomyosarcoma. Each feature was allocated a score from 0 to 2 according to the strength of association with malignancy. Subsequently, the MR images were blindly and independently reviewed by a fellowship-trained radiologist and a clinical fellow/senior resident. Each uterine mass was scored according to their imaging features. The scores were divided into five categories according to the sum of scores. Category III and above was considered positive for leiomyosarcoma/STUMP. Sensitivity, specificity, and positive and negative predictive values were calculated.

    Results A total of 244 women were included (age range 20–74 years, mean 40). Of these, 218 patients had benign leiomyoma, 13 had STUMP, and 13 had leiomyosarcoma. The sensitivity and specificity of the scoring system were 92.3% and 64.7%, respectively. The negative predictive value was 98.6%. No leiomyosarcoma was missed using this scoring system. The presence of non-cystic T2 hyperintensity or diffusion restriction in a uterine mass were the most sensitive signs of a leiomyosarcoma/STUMP.

    Conclusion The proposed multi-parametric MRI scoring system may be useful in differentiating benign uterine leiomyomas from leiomyosarcomas/STUMP.

    • Gynecology
    • Uterine Cancer
    • Uterus
    • Uterine Neoplasms

    Data availability statement

    Data are available upon reasonable request. The data that support the findings of this study are available from the corresponding author, MAK, upon reasonable request.

    https://doi.org/10.1136/ijgc-2023-005220

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      Data availability statement

      Data are available upon reasonable request. The data that support the findings of this study are available from the corresponding author, MAK, upon reasonable request.

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      Footnotes

      • Contributors Conceptualization: IAS, MAK, AA, EH. Data curation: MAK, IAS, HAA, AAH, AA, EH, PV, AAS, NA. Formal analysis: MAK, SJ. Methodology: MAK. Project administration: MAK, IAS. Supervision: IAS. Writing-original draft: MAK, HAA. Writing-review and editing: IAS, AA, EH, MAK. Guarantor: MAK.

      • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

      • Competing interests None declared.

      • Provenance and peer review Not commissioned; externally peer reviewed.

      • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.