Deep learning for the determination of myometrial invasion depth and automatic lesion identification in endometrial cancer MR imaging: a preliminary study in a single institution

Objective To determine the diagnostic performance of a deep learning (DL) model in evaluating myometrial invasion (MI) depth on T2-weighted imaging (T2WI)–based endometrial cancer (EC) MR imaging (ECM). Methods We retrospectively enrolled 530 patients with pathologically proven EC at our institution...

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Veröffentlicht in:	European radiology 2020-09, Vol.30 (9), p.4985-4994
Hauptverfasser:	Chen, Xiaojun, Wang, Yida, Shen, Minhua, Yang, Bingyi, Zhou, Qing, Yi, Yinqiao, Liu, Weifeng, Zhang, Guofu, Yang, Guang, Zhang, He
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Algorithms Archiving Cancer Classification Communications systems Deep Learning Diagnostic Radiology Diagnostic systems Endometrial cancer Endometrial Neoplasms - diagnosis Endometrium Female Humans Image classification Imaging Imaging Informatics and Artificial Intelligence Internal Medicine Interventional Radiology Lesions Machine learning Magnetic resonance imaging Magnetic Resonance Imaging - methods Medical imaging Medicine Medicine & Public Health Middle Aged Model accuracy Myometrium Myometrium - pathology Neoplasm Invasiveness Neuroradiology Radiology Retrospective Studies Sensitivity Ultrasound
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