Survivors of childhood cancer need lifelong monitoring for side effects from radiotherapy. However, longitudinal data from routine monitoring is often infrequently and irregularly sampled, and subject to inaccuracies. Due to this, measurements are often studied in isolation, or simple relationships (e.g., linear) are used to impute missing timepoints. In this study, we investigated the potential role of Gaussian Processes (GP) modelling to make population-based and individual predictions, using insulin-like growth factor 1 (IGF-1) measurements as a test case. With training data of 23 patients with a median (range) of 4 (1-16) timepoints we identified a trend within the range of literature reported values. In addition, with 8 test cases, individual predictions were made with an average root mean squared error of 31.9 (10.1 - 62.3) ng/ml and 27.4 (0.02 - 66.1) ng/ml for two approaches. GP modelling may overcome limitations of routine longitudinal data and facilitate analysis of late effects of radiotherapy.
翻译:儿童癌症幸存者需终身监测放疗引发的副作用。然而,常规监测获得的纵向数据往往采样稀疏且不规则,并存在误差。因此,现有研究常孤立分析测量值,或采用简单关系(如线性关系)插补缺失时间点。本研究以胰岛素样生长因子1(IGF-1)测量值为测试案例,探讨高斯过程建模在群体与个体预测中的潜在应用。通过23名患者(中位时间点数为4,范围1-16)的训练数据,我们发现了与文献报道值范围一致的变化趋势。此外,在8例测试案例中,两种方法实现的个体预测平均均方根误差分别为31.9(10.1-62.3)ng/ml和27.4(0.02-66.1)ng/ml。高斯过程建模有望克服常规纵向数据的局限性,促进放疗远期效应的分析。