Modeling Spontaneous XXXX following Surgery: An In Vivo-In Silico Approach.

@entity5 @entity1 @entity19 @entity3
UNASSIGNED: Rapid improvements in the detection and tracking of early-stage @entity5 progression aim to guide decisions regarding cancer treatments as well as predict metastatic recurrence in @entity1 following surgery. Mathematical models may have the potential to further assist in estimating metastatic risk, particularly when paired with in vivo @entity5 data that faithfully represent all stages of disease progression. Herein, we describe mathematical analysis that uses data from @entity19 models of spontaneous @entity3 developing after surgical removal of orthotopically implanted @entity5 . Both presurgical ( @entity5 ) growth and postsurgical (metastatic) growth were quantified using bioluminescence and were then used to generate a mathematical formalism based on general laws of the disease (i.e., dissemination and growth). The model was able to fit and predict pre/postsurgical data at the level of the individual as well as the population. Our approach also enabled retrospective analysis of clinical data describing the probability of metastatic relapse as a function of @entity5 size. In these data-based models, interindividual variability was quantified by a key parameter of intrinsic metastatic potential. Critically, our analysis identified a highly nonlinear relationship between @entity5 size and postsurgical survival, suggesting possible threshold limits for the utility of @entity5 size as a predictor of metastatic recurrence. These findings represent a novel use of clinically relevant models to assess the impact of surgery on metastatic potential and may guide optimal timing of treatments in neoadjuvant (presurgical) and adjuvant (postsurgical) settings to maximize @entity1 benefit. @entity5 Res; 76(3); 1-13.   2015 AACR.
