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MRI, clinical, and radiomic models for differentiation of uterine leiomyosarcoma and leiomyoma

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Abstract

Purpose

To assess the predictive ability of conventional MRI features and MRI texture features in differentiating uterine leiomyoma (LM) from uterine leiomyosarcoma (LMS).

Methods

This single-center, IRB-approved, HIPAA-compliant retrospective study included 108 patients (69 LM, 39 LMS) who had pathology, preoperative MRI, and clinical data available at our tertiary academic institution. Two radiologists independently evaluated 14 features on preoperative MRI. Texture features based on 3D segmentation were extracted from T2W-weighted MRI (T2WI) using commercially available texture software (TexRAD™, Feedback Medical Ltd., Great Britain). MRI conventional features, and clinical and MRI texture features were compared between LM and LMS groups. Dataset was randomly divided into training (86 cases) and testing (22 cases) cohorts (8:2 ratio); training cohort was further subdivided into training and validation sets using ten-fold cross-validation. Optimal radiomics model was selected out of 90 different machine learning pipelines and five models containing different combinations of MRI, clinical, and radiomics variables.

Results

12/14 MRI conventional features and 2/2 clinical features were significantly different between LM and LMS groups. MRI conventional features had moderate to excellent inter-reader agreement for all but two features. Models combining MRI conventional and clinical features (AUC 0.956) and MRI conventional, clinical, and radiomics features (AUC 0.989) had better performance compared to models containing MRI conventional features alone (AUC 0.846 and 0.890) or radiomics features alone (0.929).

Conclusion

While multiple MRI and clinical features differed between LM and LMS groups, the model combining MRI, clinical, and radiomic features had the best predictive ability but was only marginally better than a model utilizing conventional MRI and clinical data alone.

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Funding

No funds, grants, or other support was received for this work.

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Author notes

  1. Lauren A. Roller and Qi Wan are co-first authors.

Authors and Affiliations

  1. Department of Radiology, Brigham and Women’s Hospital, Boston, MA, 02115, USA

    Lauren A. Roller, Kristine S. Burk, Yang Guo & Atul B. Shinagare

  2. Department of Imaging, Dana Farber Cancer Institute, Boston, MA, 02115, USA

    Lauren A. Roller, Qi Wan, Lei Qin, Kristine S. Burk, Yang Guo & Atul B. Shinagare

  3. Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China

    Qi Wan

  4. Department of Medical Imaging, University Medical Imaging Toronto, University Health Network, University of Toronto, Toronto, ON, M5T1W7, Canada

    Xiaoyang Liu

  5. Department of Pathology, University of Michigan, Ann Arbor, MI, 48109, USA

    David Chapel

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Correspondence to Lauren A. Roller.

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Atul B. Shinagare: Serves as a consultant for Virtualscopics and Imaging Endpoints. The other authors have no competing interests or funding.

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Roller, L.A., Wan, Q., Liu, X. et al. MRI, clinical, and radiomic models for differentiation of uterine leiomyosarcoma and leiomyoma. Abdom Radiol (2024). https://doi.org/10.1007/s00261-024-04198-8

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