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深度学习模型与计算机断层扫描功能相结合,可在手术前预测胃肠道瘤风险分层

Yi Li¹, Yan-Bei Liu², Xu-Bin Li¹

¹Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, State Key Laboratory of Druggability Evaluation and Systematic Translational Medicine, Tianjin Key Laboratory of Digestive Cancer, Tianjin 300060, China.

World journal of gastrointestinal oncology

|December 16, 2024

概括

一个深度学习模型与CT特征相结合,准确地预测了胃肠层瘤 (GIST) 风险. 这种综合方法显示出高于独立的深度学习模型对GIST风险分层的优异性能.

科学领域:

放射学放射学是一门学科.
在瘤学瘤学.
人工智能的人工智能

背景情况:

胃肠道 stromal 瘤 (GIST) 是一种常见的介质细胞瘤.
在5年内,GIST复发会影响多达50%的患者.
准确的手术前风险分层对于GIST管理至关重要.

研究的目的:

评估深度学习模型 (DLM) 与计算机断层扫描 (CT) 功能集成,用于GIST风险分层.
与独立的DLM相比,评估组合模型的预测性能.

主要方法:

从GIST患者的551次手术前对比度增强CT扫描进行了回顾性分析.
确定恶性瘤风险的显著CT成像预测因素.
使用卷积神经网络开发和验证DLM和联合DLM.

主要成果:

瘤大小,和扩大的食血管是显著的风险预测因素.
与独立的DLM相比,组合的DLM实现了更高的准确性 (92%的低风险,87%的中等风险,90%的高风险) 和AUROC (0.97,0.83,0.96).
组合模型在预测GIST风险分层方面表现出了统计学上显著的改进.

结论:

使用常规CT数据的DLM组合为手术前GIST风险分层提供了一个强大的方法.
开发的组合模型在预测GIST风险方面优于独立的DLM.
这种人工智能驱动的方法增强了GIST患者的手术前风险评估.

关键词:

深度学习是一种深度学习.胃肠道 stromal 瘤在胃肠道预后预后预测预测风险分层是指风险的分层.断层扫描,X射线计算成像

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Computed Tomography

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...