ArticleScientific reports2022
Development of a deep learning method for improving diagnostic accuracy for uterine sarcoma cases.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- MRI for diagnosing uterine sarcoma: a systematic review and meta-analysis.Japanese journal of radiology · 2026Pooled it
- Development and External Testing of an Interpretable MRI-Based Machine Learning Model for Differentiating Endometrial Stromal Sarcoma from Cellular Leiomyoma.Journal of imaging informatics in medicine · 2026Article
- Differentiation of uterine fibroids and sarcomas by MRI and serum LDH levels: a multicenter study of the KAMOGAWA study.Journal of gynecologic oncology · 2025Article
- Artificial Intelligence and Uterine Fibroids: A Useful Combination for Diagnosis and Treatment.Journal of clinical medicine · 2025Review
- Review
- The automatic diagnosis artificial intelligence system for preoperative magnetic resonance imaging of uterine sarcoma.Journal of gynecologic oncology · 2024Article
- Article
- Clinical features of uterine sarcomas presenting mainly with uterine masses: a retrospective study.BMC women's health · 2023Article
- Article
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Authors and funding
29 authors.
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Abstract
Uterine sarcomas have very poor prognoses and are sometimes difficult to distinguish from uterine leiomyomas on preoperative examinations. Herein, we investigated whether deep neural network (DNN) models can improve the accuracy of preoperative MRI-based diagnosis in patients with uterine sarcomas. Fifteen sequences of MRI for patients (uterine sarcoma group: n = 63; uterine leiomyoma: n = 200) were used to train the models. Six radiologists (three specialists, three practitioners) interpreted the same images for validation. The most important individual sequences for diagnosis were axial T2-weighted imaging (T2WI), sagittal T2WI, and diffusion-weighted imaging. These sequences also represented the most accurate combination (accuracy: 91.3%), achieving diagnostic ability comparable to that of specialists (accuracy: 88.3%) and superior to that of practitioners (accuracy: 80.1%). Moreover, radiologists' diagnostic accuracy improved when provided with DNN results (specialists: 89.6%; practitioners: 92.3%). Our DNN models are valuable to improve diagnostic accuracy, especially in filling the gap of clinical skills between interpreters. This method can be a universal model for the use of deep learning in the diagnostic imaging of rare tumors.
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