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深度学习用于基于2.5D磁共振成像的状瘤差异诊断

Wenfeng Mai¹, Xiaole Fan², Lingtao Zhang¹

¹Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China.

Annals of medicine

|June 18, 2025

概括

精确诊断腺瘤 (PGT) 是至关重要的. 使用2.5DMRI的深度学习 (DL) 模型改善了良性与恶性PGT的区分,优于传统方法.

科学领域:

放射学放射学是一门学科.
在瘤学瘤学.
医疗成像医学成像

背景情况:

腺瘤 (PGT) 的准确手术前诊断对于手术规划至关重要,因为恶性瘤需要更广泛的切除.
精细针吸收活检是目前的诊断标准,对检测恶性瘤的敏感性有限.
深度学习 (DL) 模型虽然在医学成像中普遍存在,但在腺瘤分析中尚未得到充分研究.

研究的目的:

开发和评估使用2.5D成像方法的DL模型,以改善良性和恶性PGT之间的差异化.
将DL模型的诊断性能与传统预测模型进行比较.

主要方法:

一个回顾性研究使用MRI和临床数据,对122名瘤患者进行了回顾性研究.
传统的模型开发涉及单变量分析和多变量逻辑回归与四重交叉验证.
DL模型培训采用2D和2.5D成像与基于变压器的架构进行转移学习.
性能评估使用了接收器运行特征曲线 (AUC) 下的面积和混矩阵指标.

主要成果:

传统模型将边界和周侵袭确定为预测因素,达到0.79的AUC和低灵敏度 (0.54).
使用2.5D T2脂肪抑制MRI的DL模型,表现出卓越的性能,AUC为0.86和灵敏度为0.78.
2.5D成像方法显著提高了DL模型区分PGT的能力.

结论:

整合2.5D成像与基于变压器的DL模型提供了一个强大的工具,以区分状腺瘤.
这种先进的DL方法显着有望改善PGT管理中的手术前诊断准确性.

关键词:

2.5D成像技术的使用.磁共振成像技术磁共振成像技术深度学习是一种深度学习.状腺瘤状腺瘤

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