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脑转移的MRI放射学从非小细胞肺癌的亚病理分类:一个机器学习,多中心研究

Fuxing Deng^1,2,3, Zhiyuan Liu^1,2,3, Wei Fang⁴

¹Department of Oncology, Xiangya Hospital, Central South University, Changsha, 410008, China.

Physical and engineering sciences in medicine

|July 17, 2023

概括

机器学习准确地区分非小细胞肺癌大脑病变使用放射学从对比增强的T1MRI扫描. 这种方法有助于在活检不可行时进行诊断,改善了患者的护理.

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:

大脑转移在非小细胞肺癌 (NSCLC) 中很常见.
脑病变的组织学类型对治疗至关重要,但通常需要侵入性活检.
在NSCLC患者中,需要使用非侵入性方法来准确表征病变.

研究的目的:

开发和验证一种机器学习模型,用于区分NSCLC患者脑病变的组织学亚型.
为了比较各种机器学习算法的性能,用于此分类任务.
识别关键的成像特征,有助于精确的病变分化.

主要方法:

利用了来自两个患者队伍 (兴亚医院和岳阳中央医院) 的回顾性数据.
从对比度增强的T1MRI扫描中提取了放射性特征.
训练和评估了8个机器学习算法,包括XGBoost和一个3D卷积神经网络.
为了特征解释性,使用了夏普利添加式解释 (SHAP).

主要成果:

使用单个放射性特征的XGBoost模型实现了最高的性能 (AUC:0.85内部,0.80外部验证).
肺腺癌与状癌的分类性能在模型中从0.60到0.87的AUC不等.
SHAP分析确定了影响分类结果的重要特征.

结论:

结合对比度增强的T1MRI,XGBoost和SHAP的放射学模型为NSCLC中大脑病变的分类提供了一个有希望的,可解释的方法.
这种非侵入性方法可以帮助诊断当活检是不安全或不可行的.
这项研究强调了人工智能驱动的放射学在NSCLC脑转移的精密瘤学中的潜力.

关键词:

大脑转移的转移.解释能力解释能力机器学习机器学习磁共振成像技术磁共振成像技术无线电学 (Radiomics) 是一种辐射学.辐射疗法辐射疗法

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