随着肺癌治疗中针对 PD-1/PD-L1 的免疫检查点抑制剂 (ICIs) 的应用不断增加，临床需要可靠地预测个体患者的治疗结果。为了弥补预测差距，我们检查了四种不同的数学模型，形式为：常微分方程，包括新颖的延迟响应模型。我们通过对模型导出的结果概率进行等权重，严格评估其对患者进展性疾病 (PD) 状态的个体和综合预测能力。拟合完整的治疗过程，新型延迟反应模型 (R2=0.938) 优于最简单的延迟反应模型模型（R2=0.865）。仅通过原发肿瘤最长直径测量值进行校准，该模型组合就能够可靠地预测患者 PD 结果，总体准确度为 77%（敏感性 = 70%，特异性 = 81%）。它自动识别了 51% 的患者子集，可以实现总体准确度为 81%（敏感性 = 81%，特异性 = 81%）的预测。所有模型的性能均显着优于完全数据驱动的基于机器学习的方法。这些建模方法提供了动态基线框架，通过利用临床上可用的测量数据识别不同的治疗结果轨迹，支持临床医生的治疗决策。所提出的方法与其他方法的联合应用预测工具和生物标志物以及进一步的疾病信息（例如转移阶段）可以进一步增强治疗结果的预测。我们相信简单的模型公式可以使开发的模型广泛应用于其他癌症类型。可以轻松地为其他治疗方式制定类似的模型。版权所有 © 2024 作者。由爱思唯尔公司出版。保留所有权利。

The increased application of immune checkpoint inhibitors (ICIs) targeting PD-1/PD-L1 in lung cancer treatment generates clinical need to reliably predict individual patients' treatment outcomes.To bridge the prediction gap, we examine four different mathematical models in the form of ordinary differential equations, including a novel delayed response model. We rigorously evaluate their individual and combined predictive capabilities with regard to the patients' progressive disease (PD) status through equal weighting of model-derived outcome probabilities.Fitting the complete treatment course, the novel delayed response model (R2=0.938) outperformed the simplest model (R2=0.865). The model combination was able to reliably predict patient PD outcome with an overall accuracy of 77% (sensitivity = 70%, specificity = 81%), solely through calibration with primary tumor longest diameter measurements. It autonomously identified a subset of 51% of patients where predictions with an overall accuracy of 81% (sensitivity = 81%, specificity = 81%) can be achieved. All models significantly outperformed a fully data-driven machine learning-based approach.These modeling approaches provide a dynamic baseline framework to support clinicians in treatment decisions by identifying different treatment outcome trajectories with already clinically available measurement data.Conjoint application of the presented approach with other predictive tools and biomarkers, as well as further disease information (e.g. metastatic stage), could further enhance treatment outcome prediction. We believe the simple model formulations allow widespread adoption of the developed models to other cancer types. Similar models can easily be formulated for other treatment modalities.Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.