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多参数MRI与放射学相结合,用于诊断和分级子宫内膜纤维化

Huanhuan Wang¹, Li Zhu¹, Hui Zhu²

¹Department of Radiology, Nanjing Drum Tower Hospital, Clinical College of Nanjing University of Chinese Medicine, No. 321 Zhongshan Road, Nanjing, 210008, China.

Abdominal radiology (New York)

|January 22, 2025

概括

多参数MRI (MP-MRI) 结合放射学和机器学习 (ML) 准确地分级子宫内膜纤维化 (EF). 这种非侵入性方法显示出减少在诊断EF时对胰腺镜检查的需求的潜力.

科学领域:

放射学放射学是一门学科.
医疗成像医学成像
在瘤学瘤学.

背景情况:

子宫内膜纤维化 (EF) 的诊断和分级可能具有挑战性.
透镜虽然有效,但具有侵入性.

研究的目的:

评估多参数MRI (MP-MRI) 与放射学结合用于诊断和分级子宫内膜纤维化 (EF) 的疗效.

主要方法:

预计将招募74名严重EF患者,41名轻度至中度EF患者和40名健康妇女.
从T2和DWIMRI序列中提取的放射性特征.
使用特征选择和分类器开发和评估五种机器学习 (ML) 预测模型.

主要成果:

性能最好的模型将无监督特征选择 (UFS) 与逻辑回归 (LR) 结合起来,在测试组中达到0.92的平均AUC.
模型1 (T2 + ADC + 临床数据) 显示出优异的性能,AUC为0.92,F1得分为0.80,精度为0.81.
与T2相关的特征,特别是T2_wavelet-LLL_gldm_DependenceVariance,对于区分纤维化水平至关重要.

结论:

使用ML的MP-MRI放射学分析为EF分级提供了出色的性能.
这种非侵入性技术有望减少对胰腺镜的依赖.

关键词:

子宫内膜 (Endometrium) 是一个内膜.纤维化纤维化纤维化机器学习是机器学习.磁共振成像技术磁共振成像技术无线电学 (Radiomics) 是一种辐射学.

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