Abstract: Being responsible for over 50,000 death per year within the U.S. alone, colorectal cancer (CRC) is the second leading cause of cancer related deaths in industry nations with increasing prevalence. Within the scope of personalized medicine, precise estimates on future progress are crucial. We thus propose a novel deep learning based system using deep convolutional sparse autoencoders for estimating future lesion growth for CRC liver lesions based on single slice CT tumor images for early therapy assessment. Furthermore, we show that our system can be used for one-year survival prediction in CRC patients. While state of the art treatment assessment (RECIST) is premised on retrospective lesion analysis, our proposed system delivers an estimate on future response, thus prospectively allowing to adapt therapy before further progress. We compare our system to single-lesion assessment through RECIST diameter and Radiomics. With our approach we archieve a phi-coefficient of 40.0% compared to 27.3% / 29.4% and an AUC of .784 vs .744/.737 for growth prediction, as well as a phi-coefficient of 44.9% vs 32.1% / 18.0% and an AUC of .710 vs. .688/.568 for survival prediction.
Keywords: deep convolutional neural networks, colorectal cancer, computed tomography, survival prediction, tumor growth prediction
Author Affiliation: Siemens Healthcare GmbH, University Hospital Grosshadern Ludwig-Maximilians-University München, University of Technology Ilmenau Neuroinformatics and Cognitive Robotics Lab