PT  - JOURNAL ARTICLE
AU  - Cristian Axenie
AU  - Daria Kurz
TI  - Tumor Characterization using Unsupervised Learning of Mathematical Relations within Breast Cancer Data
AID  - 10.1101/2020.06.08.140723
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.06.08.140723
4099  - http://biorxiv.org/content/early/2020/06/09/2020.06.08.140723.short
4100  - http://biorxiv.org/content/early/2020/06/09/2020.06.08.140723.full
AB  - 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 and introduce a machine learning model capable of characterizing a tumor, namely its 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 on three publicly available clinical breast cancer datasets, being versatile 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 to estimate tumor volume, which can be readily incorporated within current clinical practice, for surgery planning. We demonstrate the broad unsupervised learning and prediction capabilities of our model through a series of experiments on publicly available clinical datasets.Competing Interest StatementThe authors have declared no competing interest.