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基于MRI的息地放射学,用于预测清细胞脏细胞癌的WHO/ISUP核级

Naijing Shi^1,2, Cong Zhang^1,2, Xinyi Li^1,2

¹Department of Radiology, The First Central Hospital of Baoding, Baoding, Hebei, China.

Frontiers in oncology

|February 16, 2026

概括

这项研究开发了一个可解释的AI模型,集成MRI特征来预测清细胞细胞癌 (ccRCC) 等级. 该模型显示了对非侵入性分级和个性化治疗规划的承诺.

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:

清晰细胞细胞癌 (ccRCC) 的准确分类对于治疗至关重要.
目前的分级依赖于侵入性方法,需要非侵入性的替代方案.

研究的目的:

开发和评估一个可解释的融合模型,集成内,周和息地MRI特征,用于预测ccRCCWHO/ISUP核级.
评估这个模型对于非侵入性手术前分级的可行性.

主要方法:

对154名ccRCC患者的回顾性分析.
从对比度增强的T1加权MRI中提取内,周 (1-5mm边缘) 和息地放射性特征.
开发机器学习模型和一个集成的nomogram.
使用ROC曲线 (AUC),校准曲线和决策曲线分析 (DCA) 的性能评估.
使用沙普利增量解释 (SHAP) 的模型解释性.

主要成果:

综合性息地区域模型实现了高性能 (AUC 0.894培训,0.877测试).
一个2毫米周边缘模型也显示出强的性能 (AUC 0.884训练,0.839测试).
结合息地,内,2毫米周特征和临床预测因子的最终名图,实现了出色的预测性表现 (AUC 0.934训练,0.912测试).
SHAP分析提供了模型决策的可视化.

结论:

开发的整合多式核磁共振功能的诺米图提供了对ccRCCWHO/ISUP核级的优秀的非侵入性手术前预测.
这种方法显示出作为个性化治疗规划和预后评估的辅助工具的潜力.
临床应用需要进一步的外部验证.

关键词:

世卫组织/ISUP 核级核级清细胞细胞癌是什么?息地息地息地息地周周口 (peritumoral) 的情况是这样的.无线电学 (radiomics) 是一种无线电学.

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