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The use of mathematical models to make predictions about tumor growth andresponse to treatment has become increasingly more prevalent in the clinicalsetting. The level of complexity within these models ranges broadly, and thecalibration of more complex models correspondingly requires more detailedclinical data. This raises questions about how much data should be collectedand when, in order to minimize the total amount of data used and the time untila model can be calibrated accurately. To address these questions, we propose aBayesian information-theoretic procedure, using a gradient-based score functionto determine the optimal data collection times for model calibration. The novelscore function introduced in this work eliminates the need for a weightparameter used in a previous study's score function, while still yieldingaccurate and efficient model calibration using even fewer scans on a sample setof synthetic data, simulating tumors of varying levels of radiosensitivity. Wealso conduct a robust analysis of the calibration accuracy and certainty, usingboth error and uncertainty metrics. Unlike the error analysis of the previousstudy, the inclusion of uncertainty analysis in this work|as a means fordeciding when the algorithm can be terminated|provides a more realistic optionfor clinical decision-making, since it does not rely on data that will becollected later in time.

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