Utilizing gradient approximations to optimize data selection protocols for tumor growth model calibration

TL;DR

This paper introduces a gradient-based Bayesian information-theoretic method to optimize data collection timing for tumor model calibration, reducing data needs and improving clinical decision-making accuracy.

Contribution

It presents a novel score function that removes the need for a weight parameter, enhancing calibration efficiency with fewer scans.

Findings

Fewer scans are needed for accurate model calibration.

The new score function improves calibration efficiency.

Uncertainty analysis aids in realistic clinical decision-making.

Abstract

The use of mathematical models to make predictions about tumor growth and response to treatment has become increasingly more prevalent in the clinical setting. The level of complexity within these models ranges broadly, and the calibration of more complex models correspondingly requires more detailed clinical data. This raises questions about how much data should be collected and when, in order to minimize the total amount of data used and the time until a model can be calibrated accurately. To address these questions, we propose a Bayesian information-theoretic procedure, using a gradient-based score function to determine the optimal data collection times for model calibration. The novel score function introduced in this work eliminates the need for a weight parameter used in a previous study's score function, while still yielding accurate and efficient model calibration using even fewer scans on a sample set of synthetic data, simulating tumors of varying levels of radiosensitivity. We also conduct a robust analysis of the calibration accuracy and certainty, using both error and uncertainty metrics. Unlike the error analysis of the previous study, the inclusion of uncertainty analysis in this work|as a means for deciding when the algorithm can be terminated|provides a more realistic option for clinical decision-making, since it does not rely on data that will be collected later in time.