Circulating Tumor Enumeration using Deep Learning

Abstract

Cancer is the third most killer disease just after infectious and cardiovascular diseases. Existing cancer treatment methods vary among patients based on the type and stage of tumor development. Treatment modalities such as chemotherapy, surgery and radiation are successful when the disease is detected early and regularly monitored. Enumeration and detection of Circulating Tumor Cells (CTC’s) is a key monitoring method which involves identification of cancer related substances known as tumor markers which are excreted by primary tumors into patient’s blood. The presence, absence or number of CTC’s in blood can be used as treatment metric indicator. As such, the metric can be used to evaluate patient’s disease progression and determine effectiveness of a treatment option a patient is subjected to. In this paper, we present a deep learning model based on Convolutional Neural Network which learns and enumerates CTC’s from stained image samples. With no human intervention, the model learns the best set of representations to enumerate CTC’s.