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首页> 美国卫生研究院文献>International Journal of Environmental Research and Public Health >Using Deep Learning with Convolutional Neural Network Approach to Identify the Invasion Depth of Endometrial Cancer in Myometrium Using MR Images: A Pilot Study
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Using Deep Learning with Convolutional Neural Network Approach to Identify the Invasion Depth of Endometrial Cancer in Myometrium Using MR Images: A Pilot Study

机译：利用MR图像使用深度学习卷积神经网络方法来识别肌瘤中肌瘤内膜癌的侵袭深度：试验研究

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Myometrial invasion affects the prognosis of endometrial cancer. However, discrepancies exist between pre-operative magnetic resonance imaging staging and post-operative pathological staging. This study aims to validate the accuracy of artificial intelligence (AI) for detecting the depth of myometrial invasion using a deep learning technique on magnetic resonance images. We obtained 4896 contrast-enhanced T1-weighted images (T1w) and T2-weighted images (T2w) from 72 patients who were diagnosed with surgico-pathological stage I endometrial carcinoma. We used the images from 24 patients (33.3%) to train the AI. The images from the remaining 48 patients (66.7%) were used to evaluate the accuracy of the model. The AI then interpreted each of the cases and sorted them into stage IA or IB. Compared with the accuracy rate of radiologists’ diagnoses (77.8%), the accuracy rate of AI interpretation in contrast-enhanced T1w was higher (79.2%), whereas that in T2w was lower (70.8%). The diagnostic accuracy was not significantly different between radiologists and AI for both T1w and T2w. However, AI was more likely to provide incorrect interpretations in patients with coexisting benign leiomyomas or polypoid tumors. Currently, the ability of this AI technology to make an accurate diagnosis has limitations. However, in hospitals with limited resources, AI may be able to assist in reading magnetic resonance images. We believe that AI has the potential to assist radiologists or serve as a reasonable alternative for pre-operative evaluation of the myometrial invasion depth of stage I endometrial cancers.
机译：肌瘤侵袭影响子宫内膜癌的预后。然而，在术前磁共振成像分期和后术后病理分期之间存在差异。本研究旨在验证人工智能（AI）的准确性，用于使用磁共振图像对磁共振图像的深度学习技术来检测Myometerial侵袭的深度。从72名患者获得4896个对比增强的T1加权图像（T1W）和T2加权图像（T2W），该患者被诊断出患有外科病理阶段I子宫内膜癌。我们使用24名患者（33.3％）的图像培训AI。剩余的48名患者（66.7％）的图像用于评估模型的准确性。然后AI解释每个病例并将它们分类为IA或IB。与放射科学家诊断的精度率（77.8％）相比，对比增强T1W的AI解释的精度率较高（79.2％），而T2W较低（70.8％）。 T1W和T2W的放射科学家和AI之间的诊断精度没有显着差异。然而，AI更有可能为共存良性的平滑肌组织或息肉瘤患者提供错误的解释。目前，这种AI技术能够进行准确诊断的能力有局限性。然而，在资源有限的医院中，AI可能能够帮助阅读磁共振图像。我们认为AI有可能协助放射科医师或作为合理的替代替代替代阶段IS子宫内膜癌症的肌瘤侵袭深度的术前评价。

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