RT Journal Article
SR Electronic
T1 Deep learning detects virus presence in cancer histology
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 690206
DO 10.1101/690206
A1 Jakob Nikolas Kather
A1 Jefree Schulte
A1 Heike I. Grabsch
A1 Chiara Loeffler
A1 Hannah Muti
A1 James Dolezal
A1 Andrew Srisuwananukorn
A1 Nishant Agrawal
A1 Sara Kochanny
A1 Saskia von Stillfried
A1 Peter Boor
A1 Takaki Yoshikawa
A1 Dirk Jaeger
A1 Christian Trautwein
A1 Peter Bankhead
A1 Nicole A. Cipriani
A1 Tom Luedde
A1 Alexander T. Pearson
YR 2019
UL http://biorxiv.org/content/early/2019/07/05/690206.abstract
AB Oncogenic viruses like human papilloma virus (HPV) or Epstein Barr virus (EBV) are a major cause of human cancer. Viral oncogenesis has a direct impact on treatment decisions because virus-associated tumors can demand a lower intensity of chemotherapy and radiation or can be more susceptible to immune check-point inhibition. However, molecular tests for HPV and EBV are not ubiquitously available.We hypothesized that the histopathological features of virus-driven and non-virus driven cancers are sufficiently different to be detectable by artificial intelligence (AI) through deep learning-based analysis of images from routine hematoxylin and eosin (HE) stained slides. We show that deep transfer learning can predict presence of HPV in head and neck cancer with a patient-level 3-fold cross validated area-under-the-curve (AUC) of 0.89 [0.82; 0.94]. The same workflow was used for Epstein-Barr virus (EBV) driven gastric cancer achieving a cross-validated AUC of 0.80 [0.70; 0.92] and a similar performance in external validation sets. Reverse-engineering our deep neural networks, we show that the key morphological features can be made understandable to humans.This workflow could enable a fast and low-cost method to identify virus-induced cancer in clinical trials or clinical routine. At the same time, our approach for feature visualization allows pathologists to look into the black box of deep learning, enabling them to check the plausibility of computer-based image classification.