Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Institute for Data Science in Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
NPJ Syst Biol Appl. 2024 Aug 14;10(1):88. doi: 10.1038/s41540-024-00415-8.
We present a study where predictive mechanistic modeling is combined with deep learning methods to predict individual patient survival probabilities under immune checkpoint inhibitor (ICI) immunotherapy. This hybrid approach enables prediction based on both measures that are calculable from mechanistic models of key mechanisms underlying ICI therapy that may not be directly measurable in the clinic and easily measurable quantities or patient characteristics that are not always readily incorporated into predictive mechanistic models. A deep learning time-to-event predictive model trained on a hybrid mechanistic + clinical data set from 93 patients achieved higher per-patient predictive accuracy based on event-time concordance, Brier score, and negative binomial log-likelihood-based criteria than when trained on only mechanistic model-derived values or only clinical data. Feature importance analysis revealed that both clinical and model-derived parameters play prominent roles in increasing prediction accuracy, further supporting the advantage of our hybrid approach.
我们提出了一项研究，其中将预测性机械模型与深度学习方法相结合，以预测接受免疫检查点抑制剂（ICI）免疫治疗的个体患者的生存概率。这种混合方法可以基于以下两种方法进行预测：一种是基于 ICI 治疗的关键机制的机械模型中可计算的措施，但这些措施可能无法在临床中直接测量；另一种是易于测量的数量或患者特征，这些特征并不总是容易纳入预测性机械模型中。在 93 名患者的混合机械 + 临床数据集上训练的深度学习时间事件预测模型，基于事件时间一致性、Brier 评分和负二项对数似然标准，比仅在机械模型衍生值或仅在临床数据上训练的模型具有更高的每个患者预测准确性。特征重要性分析表明，临床和模型衍生参数都在提高预测准确性方面发挥了重要作用，进一步支持了我们的混合方法的优势。
