Tumor Characterization Using Unsupervised Learning of Mathematical Relations Within Breast Cancer Data

Abstract

Despite the variety of imaging, genetic and histopathological data used to assess tumors, there is still an unmet need for patient-specific tumor growth profile extraction and tumor volume prediction, for use in surgery planning. Models of tumor growth predict tumor size and require tumor biology-dependent parametrization, which hardly generalizes to cope with tumor variability among patients. In addition, the datasets are limited in size, owing to the restricted or single-time measurements. In this work, we address the shortcomings that incomplete biological specifications, the inter-patient variability of tumors, and the limited size of the data bring to mechanistic tumor growth models. We introduce a machine learning model that alleviates these shortcomings and is capable of characterizing a tumor’s growth pattern, phenotypical transitions, and volume. The model learns without supervision, from different types of breast cancer data the underlying mathematical relations describing tumor growth curves more accurate than three state-of-the-art models. Experiments performed on publicly available clinical breast cancer datasets, demonstrate the versatility of the approach among breast cancer types. Moreover, the model can also, without modification, learn the mathematical relations among, for instance, histopathological and morphological parameters of the tumor and, combined with the growth curve, capture the (phenotypical) growth transitions of the tumor from a small amount of data. Finally, given the tumor growth curve and its transitions, our model can learn the relation among tumor proliferation-to-apoptosis ratio, tumor radius, and tumor nutrient diffusion length, used to estimate tumor volume. Such a quantity can be readily incorporated within current clinical practice, for surgery planning.