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基于MRI的机器学习模型可以预测脑膜瘤的恶性生物行为

Maoyuan Li^1,2, Luzhou Liu², Jie Qi²

¹Department of Radiology, Chengdu Qingbaijiang District People's Hospital, Chengdu, 610300, Sichuan, China.

BMC medical imaging

|September 27, 2023

概括

使用MRI数据的机器学习模型可以通过评估WHO等级和Ki-67指数来预测脑膜瘤的恶性生物行为. 这些模型为预测瘤攻击性提供了更好的灵敏度和全面性.

科学领域:

神经外科神经外科
放射学放射学是一门学科.
在瘤学瘤学.

背景情况:

脑膜瘤根据世卫组织和Ki-67指数进行分级,以评估恶性潜在.
精确预测脑膜瘤的生物行为对于治疗规划至关重要.

研究的目的:

开发和验证基于MRI的机器学习模型,用于预测脑膜瘤的WHO等级和Ki-67指数.
用放射性和临床放射性放射性 (CRR) 模型评估世卫组织等级和Ki-67指数的综合预测性能.

主要方法:

这是一项对313名脑膜瘤患者进行的多中心回顾性研究.
在MRI序列 (T1,T2,T1CE) 中从感兴趣的二维和三维区域提取放射性特征.
开发和验证240个使用特征选择和八个分类器的预测模型,包括结合临床,放射和放射性特征的CRR模型.

主要成果:

选择的放射性模型实现了0.857 (WHO等级),0.798 (Ki-67指数) 和0.888 (组合) 的交叉验证AUC.
放射性模型的验证集AUC分别为0.829,0.752和0.904.
CRR模型显示验证AUC为0.821 (WHO等级),0.753 (Ki-67指数) 和0.906 (组合),没有从额外的临床/放射学特征显著改善.

结论:

基于MRI的机器学习模型有效预测脑膜瘤的WHO等级和Ki-67指数.
整合世卫组织等级和Ki-67指数的模型提高了脑膜瘤恶性行为的预测灵敏度和全面性.

关键词:

在 Ki-67 机器人机器人机器学习是机器学习.磁共振成像技术磁共振成像技术阴道瘤是发生在阴道上的人.世界卫生组织评级WHO等级.

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Magnetic Resonance Imaging

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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...