Deep learning detects virus presence in cancer histology

Abstract

1Oncogenic viruses like human papilloma virus (HPV) or Epstein Barr virus (EBV) are a major cause of human 2 cancer. Viral oncogenesis has a direct impact on treatment decisions because virus-associated tumors can 3 demand a lower intensity of chemotherapy and radiation or can be more susceptible to immune check-4 point inhibition. However, molecular tests for HPV and EBV are not ubiquitously available. 5We hypothesized that the histopathological features of virus-driven and non-virus driven cancers are suf-6 ficiently different to be detectable by artificial intelligence (AI) through deep learning-based analysis of 7 images from routine hematoxylin and eosin (HE) stained slides. We show that deep transfer learning can 8 predict presence of HPV in head and neck cancer with a patient-level 3-fold cross validated area-under-9 the-curve (AUC) of 0.89 [0.82; 0.94]. The same workflow was used for Epstein-Barr virus (EBV) driven 10 gastric cancer achieving a cross-validated AUC of 0.80 [0.70; 0.92] and a similar performance in external 11 validation sets. Reverse-engineering our deep neural networks, we show that the key morphological fea-12 tures can be made understandable to humans. 13 This workflow could enable a fast and low-cost method to identify virus-induced cancer in clinical trials or 14 clinical routine. At the same time, our approach for feature visualization allows pathologists to look into 15 the black box of deep learning, enabling them to check the plausibility of computer-based image classifi-16 cation. 17 18

Pan-cancer image-based detection of clinically actionable genetic alterations

“…9 In the case of MSI, the genotype-phenotype correlation is consistent enough to robustly infer the genotype just by observing morphological features in a histological image, as we have previously shown. 10 Other previous studies have identified genotypephenotype links for selected genetic features in lung cancer 11,12 , prostate cancer 13 , head and neck 14 and liver 15 cancer, among others. Building on these previous studies, we systematically investigated the presence of genotype-phenotype links for a wide range of clinically relevant molecular features across all major solid tumor types.…”

Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis

“…However, once learned, the feature representation may also be used to find similar images 11 and to quantify associations with traits beyond tissue types 12,13 . This approach, known as transfer learning, has been used to establish associations with genomic alterations [14][15][16][17][18][19] , transcriptomic changes 20,21 and survival [22][23][24] .…”

The impact of site-specific digital histology signatures on deep learning model accuracy and bias

“…Deep learning on digital histology has exploded as a potential tool to identify standard histologic features such as grade 3,4 , mitosis 5,6 , and invasion 7,8 . Recently, deep-learning approaches have been applied to identify less apparent features of interest, including clinical biomarkers such as breast cancer receptor status 4,9 , microsatellite instability 10,11 , or the presence of pathogenic virus in cancer 12 . These approaches have been further extended to infer more complex features of tumor biology directly from histology, including gene expression [13][14][15] and pathogenic mutations 16,17 .…”