Deep Learning–Based Tissue Analysis May Benefit Colorectal Cancer Patients

Deep learning techniques may pave the way for a more accurate outcome prediction in colorectal cancer patients as compared to evaluations currently performed by an experienced human observer. Researchers at the University of Helsinki are reporting in Scientific Reports that they have created a deep learning algorithm that appears to help clinicians better predict patient outcomes based on colorectal cancer tissue samples. "In our study we hypothesized whether a deep learning–based algorithm can be trained to extract prognostic features from cancer tissue images without any expert-defined supervision. It appeared exciting that almost no domain expertise is needed to build accurate classifiers," said study investigator Johan Lundin, MD, PhD, who is the Research Director of FIMM-Institute for Molecular Medicine Finland, at the University of Helsinki. The researchers combined convolutional and recurrent architectures to train a deep network to predict colorectal cancer outcome based simply on images of tumor tissue samples.