现有的生存预测模型仅依赖于基线或肿瘤动力学数据，缺乏机器学习集成。我们引入了一种新颖的动力学机器学习 (kML) 模型，该模型集成了基线标记物、肿瘤动力学和四种治疗中简单血液标记物（白蛋白、C 反应蛋白、乳酸脱氢酶和中性粒细胞）。 kML 是在三项 II 期试验（533 名患者）中针对非小细胞肺癌的免疫检查点抑制 (ICI) 开发的，并在一项 III 期试验（ICI 和化疗，377 名和 354 名患者）的两个组中进行了验证。它超越了当前最先进的个体预测，测试集 C 指数为 0.790，12 个月生存准确度为 78.7%，风险比为 25.2（95% CI：10.4-61.3，P < 0.0001）以确定长期幸存者。至关重要的是，kML 仅使用 25 周的研究数据就预测了​​ III 期试验的成功（预测 HR = 0.814 (0.64-0.994) 与最终研究 HR = 0.778 (0.65-0.931)）。治疗中血液标记物建模与预测机器学习相结合，构成了支持个性化医疗和药物开发的宝贵方法。该代码可在 https://gitlab.inria.fr/benzekry/nlml_onco 上公开获取。© 2024 作者。临床药理学

Existing survival prediction models rely only on baseline or tumor kinetics data and lack machine learning integration. We introduce a novel kinetics-machine learning (kML) model that integrates baseline markers, tumor kinetics, and four on-treatment simple blood markers (albumin, C-reactive protein, lactate dehydrogenase, and neutrophils). Developed for immune-checkpoint inhibition (ICI) in non-small cell lung cancer on three phase II trials (533 patients), kML was validated on the two arms of a phase III trial (ICI and chemotherapy, 377 and 354 patients). It outperformed the current state-of-the-art for individual predictions with a test set C-index of 0.790, 12-months survival accuracy of 78.7% and hazard ratio of 25.2 (95% CI: 10.4-61.3, P < 0.0001) to identify long-term survivors. Critically, kML predicted the success of the phase III trial using only 25 weeks of on-study data (predicted HR = 0.814 (0.64-0.994) vs. final study HR = 0.778 (0.65-0.931)). Modeling on-treatment blood markers combined with predictive machine learning constitutes a valuable approach to support personalized medicine and drug development. The code is publicly available at https://gitlab.inria.fr/benzekry/nlml_onco.© 2024 The Author(s). Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.