Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig, Leipzig University, Humboldtstraße 25, 04105, Leipzig, Germany.
Institute for Medical Informatics, Statistics and Epidemiology (IMISE), Leipzig University, Leipzig, Germany.
J Cancer Res Clin Oncol. 2024 Oct 14;150(10):457. doi: 10.1007/s00432-024-05985-y.
Thrombocytopenia is a common side effect of cytotoxic chemotherapies, which is often dose-limiting. Predicting an individual's risk is of high clinical importance, as otherwise, a small subgroup of patients limits dosages for the overall population for safety reasons.
We aim to predict individual platelet dynamics using non-linear auto-regressive networks with exogenous inputs (NARX). We consider different architectures of the NARX networks, namely feed-forward networks (FNN) and gated recurrent units (GRU). To cope with the relative sparsity of individual patient data, we employ transfer learning (TL) approaches based on a semi-mechanistic model of hematotoxicity. We use a large data set of patients with high-grade non-Hodgkin's lymphoma to learn the respective models on an individual scale and to compare prediction performances with that of the semi-mechanistic model.
Of the examined network models, the NARX with GRU architecture performs best. In comparison to the semi-mechanistic model, the network model can result in a substantial improvement of prediction accuracy for patients with irregular dynamics, given well-spaced measurements. TL improves individual prediction performances.
NARX networks can be utilized to predict an individual's thrombotoxic response to cytotoxic chemotherapy treatment. For reasonable model learning, we recommend at least three well-spaced measurements per cycle: at baseline, during the nadir phase and during the recovery phase. We aim at generalizing our approach to other treatment scenarios and blood lineages in the future.
血小板减少是细胞毒性化疗的常见副作用，常为此类药物剂量的限制因素。预测个体的风险具有重要的临床意义，否则，一小部分患者会因安全原因限制整体人群的剂量。
我们旨在使用具有外生输入的非线性自回归网络（NARX）预测个体血小板动力学。我们考虑了 NARX 网络的不同架构，即前馈网络（FNN）和门控循环单元（GRU）。为了应对个体患者数据的相对稀疏性，我们采用了基于造血毒性半机理模型的迁移学习（TL）方法。我们使用了一个患有高级别非霍奇金淋巴瘤的患者的大型数据集，在个体尺度上学习各自的模型，并将预测性能与半机理模型进行比较。
在所检查的网络模型中，具有 GRU 架构的 NARX 表现最佳。与半机理模型相比，在测量间隔合理的情况下，网络模型可以显著提高不规则动力学患者的预测准确性。TL 提高了个体预测性能。
NARX 网络可用于预测个体对细胞毒性化疗治疗的血栓毒性反应。为了进行合理的模型学习，我们建议每个周期至少进行三次间隔合理的测量：在基线时、在最低点时和在恢复阶段。我们的目标是在未来将我们的方法推广到其他治疗方案和血液谱系。
