息地放射学预测了口腔癌中的HPV状态
Oya Altinok1,2, Ghulam Rasool1,3, Asim Waqas1,3
1Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida, USA.
Cancer medicine
|December 21, 2025
概括
使用CT图像的新型放射学分类器准确地预测了口腔癌 (OPC) 中的HPV状态. 这种基于息地的方法捕捉了瘤异质性,显示出强大的性能和对HPV阳性和HPV阴性瘤的潜在临床相关性.
科学领域:
- 放射学和医学成像技术
- 在瘤学瘤学.
- 计算病理学计算病理学
背景情况:
- 人类乳头瘤病毒 (HPV) 状态是口腔癌 (OPC) 的关键预后因素.
- 准确的HPV状态确定对于指导治疗决策和预测患者结果至关重要.
- 目前用于确定HPV状态的方法可能是侵入性的或耗时的.
研究的目的:
- 开发和验证基于息地的放射学分类器,使用CT图像来预测OPC中的HPV状态.
- 为了比较息地放射学分类器与内和联合放射学模型的性能.
- 通过生存分析来解释有助于HPV状态预测的放射性特征,并评估其临床相关性.
主要方法:
- 从192名OPC患者的治疗前CT扫描分析.
- 通过双层无监督聚类和放射性特征提取来生成瘤息地.
- 使用 LASSO 回归和 ROSE 算法开发和比较息地,内和联合放射学分类器.
- 使用AUCROC的性能评估和使用SHAP的特征解释;使用Kaplan-Meier的生存分析.
主要成果:
- 息地放射学分类器实现了高性能 (AUC为0.970训练,0.937测试).
- 与紧的形状和均的纹理相关的放射性特征与HPV阳性瘤有关.
- 与HPV地面真相相比,息地放射学分类器证明了无法区分的生存预测.
结论:
- 基于息地的放射学分类器可以从CT图像中可靠地预测OPC中的HPV状态.
- 这种基于成像的算法捕获了反映生物差异的瘤异质性的空间模式.
- 该分类器显示出强大的性能,可解释性和潜在的临床实用性,用于在OPC中非侵入性地确定HPV状态.
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