AI 中文总结
该研究构建含多种不确定性的数学统计模型，校准至前列腺癌数据后，发现自适应疗法可延长特定患者的进展时间，但需结合肿瘤负荷风险评估其适用性，同时指出模型误设会影响预测可靠性。
AI 中文摘要
自适应疗法已成为一种极具前景的治疗策略，它利用肿瘤内竞争来延缓疾病进展。然而，其实施通常依赖于对肿瘤负荷的间接测量，且必须考虑潜在的显著患者异质性。本研究中，我们构建了一个数学统计模型，用以捕捉患者间变异性、参数不确定性及不完善的生物标志物测量，并使用贝叶斯推断框架将该模型校准至临床前列腺癌数据。我们利用所得虚拟队列证明，在简单且已确立的基于Lotka-Volterra的模型中，自适应疗法可显著延长模型预测的最终会进展的患者亚组的进展时间。为考虑与更大肿瘤体积相关的其他风险因素，我们引入了一种基于转移风险的新指标，该指标表明，当同时考虑持续肿瘤负荷时，自适应疗法可能存在劣势。鉴于肿瘤学中不确定性的普遍性，我们随后描述了若干未来建模方向，这些方向还需捕捉潜在肿瘤或生物标志物动态随时间演变的不确定性。最后，我们证明模型误设与不可识别性会导致不可靠的预测，尤其是在不确定性未得到充分捕捉的情况下。
英文摘要
Adaptive therapy has emerged as a promising treatment strategy that exploits within-tumour competition to delay disease progression. Implementation, however, typically relies on indirect measurements of tumour burden and must account for potentially substantial patient heterogeneity. In this work, we capture patient-to-patient variability, parameter uncertainty, and imperfect biomarker measurements with a mathematical and statistical model that we calibrate to clinical prostate cancer data using a Bayesian inference framework. We use the resulting virtual cohort to demonstrate that, within the simple but now well-established Lotka-Volterra-based model, adaptive therapy robustly improves time-to-progression for the subset of patients that are predicted to eventually progress by the model. To account for other risk factors associated with larger tumour volumes, we introduce a new metric based on the risk of metastasis that demonstrates how adaptive therapy may be disadvantageous when sustained tumour burden is also considered. Given the ubiquity of uncertainty in oncology, we then describe several future modelling directions that also capture uncertainty in the temporal evolution of the underlying tumour or biomarker dynamics. Finally, we demonstrate how model misspecification and non-identifiability can lead to unreliable predictions, especially if uncertainty is inadequately captured.
Comments29 pages, 9 figures
