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ProMMF_Kron:一种多式深度学习模型,用于预测胃腺癌中的免疫治疗反应

Chenchen Wang¹, Weikun Liu¹, Dongmei Ai¹

¹School of Mathematics and Physics, University of Science and Technology Beijing, Beijing, China.

Frontiers in immunology

|February 26, 2026

概括

一个新的深度学习模型,ProMMF_Kron,准确地预测患者对胃癌免疫检查点抑制剂 (ICI) 治疗的反应. 这种工具可以区分微卫星不稳定 (MSI) 和微卫星稳定 (MSS) 亚型,提高精确免疫疗法.

科学领域:

在瘤学瘤学.
免疫治疗是一种免疫疗法.
生物信息学是一种生物信息学.
人工智能在医学中的应用

背景情况:

免疫检查点抑制剂 (ICI) 疗法通过增强T细胞反应来改善癌症治疗结果.
预测患者对ICI治疗的反应是具有挑战性的,因为与免疫相关的不良事件.
微卫星不稳定性 (MSI) 是免疫治疗反应的生物标志物,但胃腺癌 (STAD) 呈现出显著的分子异质性 (22%的MSI与大多数的MSS).

研究的目的:

开发一种可靠的预测工具,以区分胃癌患者的MSI和MSS亚型.
通过准确预测对免疫检查点抑制剂的反应,增强精确免疫疗法.

主要方法:

开发了ProMMF_Kron,一种使用282名胃癌患者多中心数据集的多式模式深度学习模型.
采用了两阶段的特征融合策略:从分子形状 (差异基因分析) 和病理图像 (深 convolutional 神经网络) 中提取特征.
集成的Kronecker产品操作和反向投影模块,以实现高效的基因表达和图像特征交互.

主要成果:

在独立的测试数据集上,ProMMF_Kron在区分MSI和MSS亚型时获得了0.96的AUC (95%CI:0.89-1.00).
该模型的性能优于传统的单模预测模型 (3.2%的AUC改善) 和其他多模融合方法 (4.3%的AUC改善).
显示出出色的稳定性和概括能力,在结直肠癌 (CRC) 数据集上保持高准确性.

结论:

ProMMF_Kron是一个强大的决策支持工具,用于精密免疫治疗胃癌.
生物信息学分析揭示了关键分子生物标志物和免疫调节途径之间的关联.
该模型显示了大量的临床翻译价值和个性化癌症治疗的应用前景.

关键词:

强度强化治疗治疗 ICI治疗.深度学习是一种深度学习.微卫星的不稳定性多式联运数据是多式联运数据.胃腺癌是胃腺癌.

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