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基于深度学习的腺瘤分类:整合动态对比增强MRI以提高诊断准确性

Kazim Ayberk Sinci¹, Ilker Ozgur Koska^2,3, Yusuf Kenan Cetinoglu²

¹Department of Radiology, Kanuni Sultan Suleyman Education and Research Hospital, Istanbul, 34303, Türkiye. ayberksinci94@gmail.com.

BMC medical imaging

|July 4, 2025

概括

深度学习模型在增强动态对比增强 (DCE) -MRI数据时显著改善腺瘤分类. 这种功能性成像方法提高了诊断准确度,可以区分正常组织,瘤和瘤亚型.

科学领域:

放射学放射学是指放射学
人工智能的人工智能
在瘤学瘤学.

背景情况:

口腔腺瘤需要准确的分类才能有效治疗.
多参数MRI,包括DCE序列,提供丰富的诊断信息.
深度学习模型显示了自动图像分析的前景.

研究的目的:

评估深度学习模型在分类状腺瘤中的性能.
评估动态对比增强 (DCE) MRI 数据对分类准确性的影响.
为了比较不同的深度学习架构用于瘤检测和亚型化.

主要方法:

一项对164名参与者 (124名瘤患者,40名对照人) 的回顾性研究,使用多参数MRI.
两种深度学习模型 (MobileNetV2,EfficientNetB0) 进行了微调,用于瘤分类任务.
动态对比增强 (DCE) 衍生指标与增强分类模型集成.

主要成果:

在正常与瘤分类中,EfficientNetB0实现了85%的准确性.
整合DCE数据显著改善了良性与恶性瘤分类,在MobileNetV2.2.的帮助下,该分类达到96%.
DCE数据提升了组合方法的89%准确性,用于亚型分类 (小瘤与多形腺瘤).

结论:

动态对比增强 (DCE) -MRI数据大大提高了腺癌分类的深度学习模型的准确性.
来自DCE-MRI的功能成像生物标志物对于改善非侵入性诊断工作流程非常有价值.
深度学习与DCE-MRI相结合,为腺瘤的特征提供了一个强大的工具.

关键词:

深度学习是一种深度学习.状腺瘤的发生.输液磁共振成像技术的成像形腺瘤是一种多形腺瘤.温的瘤是一个细小的瘤.

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Magnetic Resonance Imaging

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...