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基于可变自编码器的深度学习和放射学,用于预测局部晚期食道状细胞癌患者对新辅助化学免疫治疗的病理完整反应

Qing Gu¹, Shenlun Chen^2,3, Andre Dekker³

¹Department of Radiation Oncology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China.

The British journal of radiology

|September 25, 2025

概括

一种新的深度学习放射学 (DLR) 模型有效地预测了在接受新辅助化学免疫疗法 (nCIT) 的局部晚期食道状细胞癌 (LA-ESCC) 患者的病理完整反应 (pCR). 这种DLR模型显示了在治疗反应预测方面比传统的深度学习方法更高的性能.

科学领域:

在瘤学瘤学.
放射学放射学是一门学科.
人工智能的人工智能

背景情况:

新辅助化疗免疫疗法 (nCIT) 是局部晚期食道状细胞癌 (LA-ESCC) 的关键治疗方法.
准确预测病理完整反应 (pCR) 对于优化LA-ESCC的治疗策略至关重要.

研究的目的:

在接受nCIT的LA-ESCC患者中开发和验证PCR的预测模型.
将深度学习放射学 (DLR) 模型的性能与深度学习 (DL) 模型进行PCR预测的比较.

主要方法:

利用基于变化自编码器 (VAE) 的深度学习和放射学来构建DL和DLR模型.
在253名LA-ESCC患者的队列上使用5倍交叉验证进行训练和验证的模型.
在一个由40名LA-ESCC患者组成的独立测试队列上评估模型性能.

主要成果:

与DL模型相比,DLR模型在验证 (0.949) 和测试队列 (0.926) 中实现了较高的曲线下面面积 (AUC) 值.
在DLR模型中,精度和回忆之间的性能差距较小.
与DL模型 (0.726验证,0.727测试) 相比,DLR模型获得了更高的F1分数 (0.766验证,0.836测试).

结论:

一个新的DLR模型,集成基于VAE的深度学习和放射学,有效地预测LA-ESCC接受nCIT的患者的pCR.
与独立的DL模型相比,开发的DLR模型表现出卓越的预测性能.
这种方法为LA-ESCC的个性化治疗选择提供了一个有希望的工具.

关键词:

深度学习是一种深度学习.食管状细胞癌的癌症新辅助化学免疫疗法新辅助疗法无线电学 (Radiomics) 是一种辐射学.变量自动编码器变量自动编码器

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