Towards Gaussian processes modelling to study the late effects of radiotherapy in children and young adults with brain tumours

TL;DR

This paper explores the use of Gaussian Process modeling to analyze irregular and sparse longitudinal data in childhood cancer survivors, aiming to better understand late effects of radiotherapy.

Contribution

It demonstrates the application of Gaussian Processes to make population-based and individual predictions from limited, irregularly sampled clinical data.

Findings

Identified a consistent trend in IGF-1 levels post-radiotherapy.

Achieved an average RMSE of around 32 ng/ml in individual predictions.

Showed GP modeling can handle sparse, irregular longitudinal data effectively.

Abstract

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.