Modeling the Error of Caliper Measurements in Animal Experiments

Abstract

Cancer prevention and treatment is one of the most significant public health challenges of the 21st century. Cancer is a serious health problem, and it is the second leading cause of death following cardiovascular diseases. This requires a reliable virtual patient model, which is usually created based on animal studies that precede human studies. Preclinical drug testing often involves mouse experiments, where tumors are implanted under the skin. Up until now, the most widespread tumor measurement method is caliper measurement, which involves a large measurement error, especially if the tumor is small. We present a noise model that can be used to model the measurement noise in animal experiments where tumor size is measured with calipers. Accurate in silico measurement is essential, as animal studies are costly, time-consuming, and strictly regulated. By incorporating a noise model, in silico experiments can better reflect real-world measurement uncertainties, improving experimental reproducibility and the reliability of virtual patient modeling. In order to model the noise, we use data from preclinical experiments measured using MRI and digital calipers, and we use a nonlinear transformation to whiten the noise. Finally, based on the Anderson-Darling test, we find the distributions that fit the noise best. We show that virtually generated measurements based on the noise model produce similar results to the original measurement noise, thus the noise model can be used to create virtual patients and model realistic experimental setups for in silico experiments.

Keywords

• Cancer
• digital caliper
• MRI
• measurement error
• personalized therapy
• preclinical experiments