Prediction of Individual Breast Cancer Evolution to Surgical Size

Abstract

Modelling surgical size is not inherently meant to replicate the tumors exact form and proportions, but instead to elucidate the degree of the tissue volume that may be surgically removed in terms of improving patient survival and minimize the risk that subsequent operations will be needed to eliminate all malignant cells entirely. Given the broad range of models of tumor growth, there is no specific rule of thumb about how to select the most suitable model for a particular breast cancer type and whether that would influence its subsequent application in surgery planning. Typically, these models require tumor biologydependent parametrization, which hardly generalizes to cope with tumor heterogeneity. In addition, the datasets are limited in size, owing to the restricted or expensive measurement methods. We address the shortcomings that incomplete biological specifications, the variety of tumor types, and the limited size of the data bring to existing mechanistic tumor growth models and introduce a Machine Learning model for the PRediction of INdividual breast Cancer Evolution to Surgical Size (PRINCESS). This is a data-driven model based on neural networks capable of unsupervised learning of cancer growth curves. PRINCESS learns the temporal evolution of the tumor along with the underlying distribution of the measurement space. We demonstrate the superior accuracy of PRINCESS, against four typically used tumor growth models, in learning tumor growth curves from a set of four clinical breast cancer datasets. Our experiments show that, without any modification, PRINCESS can accurately predict tumor sizes while being versatile between breast cancer types.