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基于MRI的深度学习放射学,用于区分良性和恶性副喉空间瘤

Helei Yan^1,2,3,4, Lei Liu⁵, Mingzhe Xie⁶

¹From the Department of Otolaryngology-Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, People's Republic of China.

The Laryngoscope

|February 11, 2025

概括

一个新的深度学习放射学模型准确地区分恶性和良性副喉空间瘤. 这种工具有助于术前诊断和临床决策对喉空间 (PPS) 瘤.

科学领域:

在瘤学瘤学.
放射学放射学是一门学科.
人工智能的人工智能

背景情况:

副喉空间 (PPS) 瘤需要准确的手术前诊断来指导治疗.
从临床上讲,区分恶性和良性PPS瘤是具有挑战性的.

研究的目的:

开发和验证一个诊断工具,用于副喉空间瘤.
整合深度学习和放射学功能,以提高诊断准确度.

主要方法:

对217名患有PPS瘤的患者进行了回顾性研究.
使用部MRI开发深度学习 (DL),放射学 (Rad) 和深度学习放射学 (DLR) 模型.
使用AUC,特异性和灵敏度评估模型性能;通过DCA评估的临床效用.

主要成果:

DLR模型实现了最高的AUC (0.899训练,0.821测试集).
与独立的DL和Rad模型相比,DLR模型表现出优越的性能.
决策曲线分析证实了DLR模型的临床实用性.

结论:

DLR模型显示出高预测能力,用于诊断恶性和良性PPS瘤.
这种综合模型可以显著帮助手术前诊断PPS瘤.
DLR模型是临床决策的宝贵工具.

关键词:

深度学习是一种深度学习.深度学习辐射学磁共振成像技术的使用副喉空间瘤副喉空间瘤无线电学 (radiomics) 是一种无线电学.

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