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机器学习整合MRI 内和内放射学预测PNST术后并发症的特征

Jia Hao Liu¹, Fang Ying Tang¹, Ji Feng Wang¹

¹Wound Repair Department, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China (J.H.L, F.Y.T., J.F.W., Y.H.C., R.W.H., J.W.); Hand Surgery & Peripheral Nerve Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China (J.H.L, F.Y.T., J.F.W., Y.H.C., R.W.H., J.W.).

Academic radiology

|February 24, 2026

概括

整合内和周放射学有效地预测了外围神经瘤并发症. 融合模型显示出卓越的性能,为风险评估提供了强大的临床途径.

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:

术后并发症发生在15-76%的患有外围神经膜瘤 (PNST) 的患者中.
目前,对于PNSTs缺乏客观的手术前风险分层工具.
磁共振成像 (MRI) 放射学研究主要集中在内区域,使周微环境的预测价值不清楚.

研究的目的:

调查整合内和周放射学在PNST患者术后并发症的预测价值.
开发和评估PNST并发症的术前风险评估的放射学模型.

主要方法:

对280名PNST患者进行了回顾性研究,接受了手术前的MRI.
内和周区域 (2毫米扩张) 的细分,用于放射性特征提取.
开发和比较四种模型:内,周,融合区域 (Imagefusion) 和连接特征 (intraPeri2mm),使用AUC和DCA进行评估.

主要成果:

放射学模型的表现明显优于仅用于临床的模型 (p<0.001).
与单个区域模型相比,融合模型 (intraPeri2mm AUC 0.899,图像融合 AUC 0.895) 显示出更高的性能.
图像融合+临床途径显示出强大的临床净益,特别是在标准化数据下.

结论:

内和周放射学的整合提供了有效的手术前预测PNST术后并发症.
对于标准化的临床数据设置,建议使用Imagefusion + 临床途径.
一个仅限于intraPeri2mm的策略可能适用于具有有限临床数据的环境.

关键词:

机器学习是机器学习.磁共振成像技术磁共振成像技术周围神经膜瘤周围神经膜瘤术后并发症术后并发症无线电学 (Radiomics) 是一种无线电学.

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