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基于MRI的深度学习放射学用于区分舌头癌症T阶段

Zhaoyi Lu¹, Bowen Zhu², Hang Ling³

¹Department of Otorhinolaryngology, The First Affiliated Hospital, Nanjing Medical University, Nanjing, 210029, China.

|August 23, 2025

概括

深度学习MRI模型 (DLRresnet18/DLRresnet50) 显示了比传统放射学更好的舌头癌症T阶段预测准确度. 这些模型增强了个性化治疗规划的诊断工具.

科学领域:

癌症学
放射学
人工智能

背景情况:

准确的T阶段预测对于语言癌症治疗计划至关重要.
传统的放射学模型可能受到主观解释的限制.

研究的目的:

开发和评估基于深度学习的MRI模型来预测舌头癌症T阶段.
将深度学习放射学模型 (DLR) 与传统放射学模型 (Rad) 的性能进行比较.

主要方法:

对579名舌头癌症患者的MRI数据进行了回顾性分析 (T2WI,CET1).
使用PyRadiomics提取2375个放射性特征.
开发DLR模型 (ResNet18,ResNet50) 并与Rad模型进行比较.

主要成果:

与Rad模型相比,深度学习模型 (DLRresnet18,DLRresnet50) 在培训,测试和外部验证集中显示出更高的AUC值.
决策曲线分析 (DCA) 证实了DLR模型的更好性能.
净重新分类改进 (NRI) 和综合歧视改进 (IDI) 对DLR模型是积极的.

结论:

DLRresnet18和DLRresnet50模型显著提高了舌头癌症T期预测的准确性.
这些由人工智能驱动的模型减少了主观解释,支持个性化治疗策略.
这项研究为舌头癌症的图像辅助诊断提供了新的工具.

关键词:

深度学习磁共振成像辐射学舌头癌症

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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...