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使用F-FDG PET-CT参数预测鼻癌总生存率的机器学习模型的比较评估

Duanyu Lin¹, Wenxi Wu¹, Zongwei Huang¹

¹Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital（Fujian Branch of Fudan University Shanghai Cancer Center）, 420 Fuma Rd, Jin'an District, Fuzhou, Fujian, China.

Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico

|September 20, 2024

概括

这项研究开发了一个机器学习模型,使用18F-FDG PET-CT扫描和临床数据来预测鼻癌存活率. 拉索模型在预后有效性方面显示出卓越的准确性.

科学领域:

在瘤学瘤学.
放射学放射学是一门学科.
医疗成像医学成像

背景情况:

鼻癌 (NPC) 是一个重大的健康挑战.
准确的预后模型对于有效的患者管理至关重要.
F-FDG PET-CT为瘤特征提供了有价值的成像见解.

研究的目的:

在NPC中评估F-FDG PET-CT参数的预后值.
确定最佳的机器学习 (ML) 模型来预测NPC预后.
整合PET-CT参数与临床数据进行增强的预测.

主要方法:

分析了2016-2020年间诊断的678名NPC患者.
开发和比较四个ML模型:RF,XGBoost,LASSO和多因素COX回归.
使用卡普兰-梅尔曲线,ROC曲线 (AUC),一致性指数 (C指数) 和决策曲线分析 (DCA) 的评估.

主要成果:

年龄,T阶段和代谢性瘤体积 (MTVT) 是整体生存期的独立预后因素.
纳入SUV-peak(T),MTVT,HIN,年龄,病理和T阶段的LASSO模型,实现了5年AUC为0.849.
亚组分析确定了高异质性指数 (HIT,MTVN,HIN) 作为重要的风险因素.

结论:

为NPC开发了一种新的ML预后模型,集成F-FDG PET-CT和临床数据.
这种模型显著改善了鼻癌患者的预后.
拉索模型展示了NPC预后的卓越性能和准确性.

关键词:

机器学习机器学习鼻癌是指鼻癌.阳位子辐射断层扫描/计算机断层扫描参数预测预后预测预测亚组分析亚组分析

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