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可解释的基于CT的多相放射性分析,用于在手术前区分良性和恶性固体瘤:一个多中心研究

Yaohai Wu¹, Fei Cao¹, Hanqi Lei¹

¹Department of Urology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China.

Abdominal radiology (New York)

|May 11, 2024

概括

使用对比度增强CT (CECT) 的机器学习模型可以有效地区分良性瘤和恶性瘤. 排泄阶段 (EP) 模型在区分瘤类型方面表现最好.

科学领域:

放射学放射学是一门学科.
人工智能的人工智能
在瘤学瘤学.

背景情况:

区分良性瘤和恶性瘤对于适当的患者管理至关重要.
增强对比度的CT (CECT) 是瘤特征的关键成像方式.

研究的目的:

开发和比较机器学习模型,使用三相CECT区分良性和恶性瘤.
评估基于单个和组合CECT阶段的模型的性能.

主要方法:

放射性特征从CECT.的皮质甲状腺 (CP),脏 (NP) 和分泌 (EP) 阶段提取出来.
随机森林 (RF) 模型使用单相和全相 (TP) 特性进行训练.
模型被内部和外部验证,SHapley添加式解释 (SHAP) 用于解释.

主要成果:

射频模型在训练和验证组中都实现了高AUC.
排泄阶段 (EP) 模型在训练组中显示出最高的AUC (0.930),在验证组中表现强 (0.921).
"原始_形状_平度"特征被确定为EP模型预测中最重要的特征.

结论:

基于三相CECT的机器学习模型对于分辨脏瘤是有效的.
基于EP特征的射频模型在良性与恶性瘤分类方面表现出卓越的性能.

关键词:

计算机断层扫描 (CT) 是一种计算机断层扫描.无线电学 (Radiomics) 是一种辐射学.随机的森林随机的森林脏瘤:脏瘤是指脏的瘤.莎普利的添加式解释

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