Towards Gaussian processes modelling to study the late effects of radiotherapy in children and young adults with brain tumours
This addresses the need for better monitoring of side effects in childhood cancer survivors, but is incremental as it applies an existing method to a new medical dataset.
The study tackled the problem of predicting late effects of radiotherapy in childhood cancer survivors using infrequent and irregular longitudinal data, by applying Gaussian Processes modeling to IGF-1 measurements, achieving individual prediction errors of 27.4-31.9 ng/ml on test cases.
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.