在患有子宫内膜癌的患者中,基于人工智能增强磁共振成像的术前分期
Lise Lecointre1, Julia Alekseenko2, Matteo Pavone3
1Institute of Image-Guided Surgery, IHU Strasbourg, France; University Hospitals of Strasbourg, Department of Gynecologic Surgery, Strasbourg, France; University of Strasbourg, ICube, Laboratory of Engineering, Computer Science and Imaging, Department of Robotics, Imaging, Teledetection and Healthcare Technologies, CNRS, UMR, Strasbourg, France.
概括
这项研究开发了一个自动化的深度学习系统来分析子宫内膜癌的MRI扫描,旨在通过预测瘤入侵来改善手术前阶段. 虽然专家表现更好,但人工智能在协助临床医生进行成像分析方面表现有前途.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 在瘤学瘤学.
背景情况:
- 准确的预后因素评估对于子宫内膜癌治疗和预后至关重要.
- 磁共振成像 (MRI) 在手术前评估中起着关键作用.
- 预测深层肌膜和宫层侵袭对于分期至关重要.
研究的目的:
- 提出一种深度学习 (DL) 管道,用于在MRI扫描中对瘤和子宫进行细分.
- 使用DL. 预测深度肌肉膜侵袭和宫层侵袭使用DL.
- 协助临床医生进行子宫内膜癌的手术前炼.
主要方法:
- 一个2阶段的DL管道被开发用于子宫和瘤细分从形T2权重的MRI.
- 该模型在142名患者数据集上进行了训练,并使用3倍分层方法进行了测试 (每倍36名患者).
- 诊断性能与两名专家放射科医生的评估进行了比较.
主要成果:
- 对于深层肌体侵入,DL模型实现了0.702的平均平衡精度,而专家则为0.769.
- 对于宫肌瘤入侵,该模型的平均平衡精度为0.721,而专家则为0.859.
- 子宫和瘤细分实现了分别为0.847和0.579的Dice分数.
结论:
- 完全自动的DL方法显示了支持子宫内膜癌的术前分期的潜力.
- 该系统证明了子宫和瘤的强大细分,尽管存在数据挑战.
- 这种人工智能解决方案可以通过帮助临床决策对医疗成像产生积极影响.
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