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

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 s...

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Bibliographic Details
Main Authors: Yusuke Toyohara, Kenbun Sone, Katsuhiko Noda, Kaname Yoshida, Ryo Kurokawa, Tomoya Tanishima, Shimpei Kato, Shohei Inui, Yudai Nakai, Masanori Ishida, Wataru Gonoi, Saki Tanimoto, Yu Takahashi, Futaba Inoue, Asako Kukita, Yoshiko Kawata, Ayumi Taguchi, Akiko Furusawa, Yuichiro Miyamoto, Takehiro Tsukazaki, Michihiro Tanikawa, Takayuki Iriyama, Mayuyo Mori-Uchino, Tetsushi Tsuruga, Katsutoshi Oda, Toshiharu Yasugi, Kimihiro Takechi, Osamu Abe, Yutaka Osuga
Format: Article
Language:English
Published: Nature Portfolio 2022-11-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-022-23064-5

Internet

https://doi.org/10.1038/s41598-022-23064-5

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