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

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....

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Detalles Bibliográficos
Autores principales: Toyohara, Yusuke, Sone, Kenbun, Noda, Katsuhiko, Yoshida, Kaname, Kurokawa, Ryo, Tanishima, Tomoya, Kato, Shimpei, Inui, Shohei, Nakai, Yudai, Ishida, Masanori, Gonoi, Wataru, Tanimoto, Saki, Takahashi, Yu, Inoue, Futaba, Kukita, Asako, Kawata, Yoshiko, Taguchi, Ayumi, Furusawa, Akiko, Miyamoto, Yuichiro, Tsukazaki, Takehiro, Tanikawa, Michihiro, Iriyama, Takayuki, Mori-Uchino, Mayuyo, Tsuruga, Tetsushi, Oda, Katsutoshi, Yasugi, Toshiharu, Takechi, Kimihiro, Abe, Osamu, Osuga, Yutaka
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9669038/
https://www.ncbi.nlm.nih.gov/pubmed/36385486
http://dx.doi.org/10.1038/s41598-022-23064-5

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9669038/
https://www.ncbi.nlm.nih.gov/pubmed/36385486
http://dx.doi.org/10.1038/s41598-022-23064-5

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