Machine Learning to Differentiate T2-Weighted Hyperintense Uterine Leiomyomas from Uterine Sarcomas by Utilizing Multiparametric Magnetic Resonance Quantitative Imaging Features.

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  • Additional Information
    • Source:
      Publisher: Association Of University Radiologists Country of Publication: United States NLM ID: 9440159 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1878-4046 (Electronic) Linking ISSN: 10766332 NLM ISO Abbreviation: Acad Radiol Subsets: MEDLINE
    • Publication Information:
      Publication: Reston Va : Association Of University Radiologists
      Original Publication: Reston, VA : Association of University Radiologists, c1994-
    • Subject Terms:
      Leiomyoma*/diagnosis ; Leiomyoma*/pathology ; Machine Learning* ; Sarcoma*/diagnosis ; Sarcoma*/pathology ; Uterine Neoplasms*/diagnosis ; Uterine Neoplasms*/pathology; Diffusion Magnetic Resonance Imaging/*methods ; Multiparametric Magnetic Resonance Imaging/*methods ; Uterus/*diagnostic imaging; Adult ; Aged ; Algorithms ; Diagnosis, Differential ; Female ; Humans ; Middle Aged ; Retrospective Studies
    • Abstract:
      Rationale and Objective: Uterine leiomyomas with high signal intensity on T2-weighted imaging (T2WI) can be difficult to distinguish from sarcomas. This study assessed the feasibility of using machine learning to differentiate uterine sarcomas from leiomyomas with high signal intensity on T2WI on multiparametric magnetic resonance imaging.
      Materials and Methods: This retrospective study included 80 patients (50 with benign leiomyoma and 30 with uterine sarcoma) who underwent pelvic 3 T magnetic resonance imaging examination for the evaluation of uterine myometrial smooth muscle masses with high signal intensity on T2WI. We used six machine learning techniques to develop prediction models based on 12 texture parameters on T1WI and T2WI, apparent diffusion coefficient maps, and contrast-enhanced T1WI, as well as tumor size and age. We calculated the areas under the curve (AUCs) using receiver-operating characteristic analysis for each model by 10-fold cross-validation and compared these to those for two board-certified radiologists.
      Results: The eXtreme Gradient Boosting model gave the highest AUC (0.93), followed by the random forest, support vector machine, multilayer perceptron, k-nearest neighbors, and logistic regression models. Age was the most important factor for differentiation (leiomyoma 44.9 ± 11.1 years; sarcoma 58.9 ± 14.7 years; p < 0.001). The AUC for the eXtreme Gradient Boosting was significantly higher than those for both radiologists (0.93 vs 0.80 and 0.68, p = 0.03 and p < 0.001, respectively).
      Conclusion: Machine learning outperformed experienced radiologists in the differentiation of uterine sarcomas from leiomyomas with high signal intensity on T2WI.
      (Copyright © 2019. Published by Elsevier Inc.)
    • Contributed Indexing:
      Keywords: Leiomyoma; Machine learning; Magnetic resonance imaging; Sarcoma; Uterine neoplasm
    • Publication Date:
      Date Created: 20190122 Date Completed: 20200424 Latest Revision: 20200424
    • Publication Date:
      20240829
    • Accession Number:
      10.1016/j.acra.2018.11.014
    • Accession Number:
      30661978