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可解释的CT放射学模型用于预测地面玻璃结节患者的侵入性

M P Hong¹, R Zhang², S J Fan³

¹Department of Radiology, Jiaxing TCM Hospital Affiliated to Zhejiang Chinese Medical University, Jiaxing, China.

Clinical radiology

|October 13, 2023

概括

一个可解释的CT放射学模型有效地预测了地面玻璃结节的侵入性. 这种由人工智能驱动的方法为临床使用提供了个性化,基于图像的决策支持.

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:

地玻璃结节 (GGNs) 需要准确的侵入性评估,以实现最佳的患者管理.
在手术前区分侵袭性和非侵袭性GGN仍然是一个临床挑战.

研究的目的:

评估一个可解释的计算机断层扫描 (CT) 放射性模型来预测GGN侵入性.
将放射性模型的性能与传统放射学模型进行比较.

主要方法:

在三个中心进行的回顾性研究,包括培训和外部验证队伍.
从CT图像中提取的放射学特征,通过特征查减少.
使用LASSO和XGBoost开发放射性模型,并通过后勤回归开发放射性模型.
使用夏普利添加式扩展 (SHAP) 探索可解释性.

主要成果:

放射性XGBoost模型在预测GGN侵入性方面表现出卓越的性能.
对于XGBoost模型的曲线下的面积 (AUC) 值在整个队列中从0.838到0.885不等.
SHAP分析提供了对模型决策和预测因素的定量和视觉见解.

结论:

一个可解释的CT放射学模型显示了手术前GGN侵入性评估的潜力.
该模型提供个性化,基于图像的临床决策支持.
这种方法可以提高对GGN特征的理解,并有助于治疗规划.

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