Dataset Information

Development of a deep learning method for improving diagnostic accuracy for uterine sarcoma cases.

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.

SUBMITTER: Toyohara Y

PROVIDER: S-EPMC9669038 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Development of a deep learning method for improving diagnostic accuracy for uterine sarcoma cases.

Toyohara Yusuke Y Sone Kenbun K Noda Katsuhiko K Yoshida Kaname K Kurokawa Ryo R Tanishima Tomoya T Kato Shimpei S Inui Shohei S Nakai Yudai Y Ishida Masanori M Gonoi Wataru W Tanimoto Saki S Takahashi Yu Y Inoue Futaba F Kukita Asako A Kawata Yoshiko Y Taguchi Ayumi A Furusawa Akiko A Miyamoto Yuichiro Y Tsukazaki Takehiro T Tanikawa Michihiro M Iriyama Takayuki T Mori-Uchino Mayuyo M Tsuruga Tetsushi T Oda Katsutoshi K Yasugi Toshiharu T Takechi Kimihiro K Abe Osamu O Osuga Yutaka Y

Scientific reports 20221116 1

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 t ...[more]

PMID: 36385486

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Dataset Information

Development of a deep learning method for improving diagnostic accuracy for uterine sarcoma cases.

Publications

Development of a deep learning method for improving diagnostic accuracy for uterine sarcoma cases.

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