Deep learning detects virus presence in cancer histology
Kather, J. N.; Schulte, J.; Grabsch, H. I.; Loeffler, C.; Muti, H. S.; Dolezal, J.; Srisuwananukorn, A.; Agrawal, N.; Kochanny, S.; von Stillfried, S.; Boor, P.; Yoshikawa, T.; Jaeger, D.; Trautwein, C.; Bankhead, P.; Cipriani, N. A.; Luedde, T.; Pearson, A. T.
Show abstract
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.\n\nWe 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.\n\nThis 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.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Generalizing AI-driven Assessment of Immunohistochemistry across Immunostains and Cancer Types: A Universal Immunohistochemistry Analyzer 95%
- Explainable, federated deep learning model predicts disease progression risk of cutaneous squamous cell carcinoma 95%
- A Deep Learning Model for Molecular Label Transfer that Enables Cancer Cell Identification from Histopathology Images 95%
Similar papers in this journal
- Artificial intelligence-based histopathology image analysis identifies a novel subset of endometrial cancers with distinct genomic features and unfavourable outcome 96%
- The Impact of Digital Histopathology Batch Effect on Deep Learning Model Accuracy and Bias 95%
- Machine learning-based tissue of origin classification for cancer of unknown primary diagnostics using genome-wide mutation features 93%
Similar papers in this journal
- Integrative deep learning analysis improves colon adenocarcinoma patient stratification at risk for mortality 95%
- Weakly-Supervised Tumor Purity Prediction FromFrozen H&E Stained Slides 94%
- Transformer-based deep learning model for the diagnosis of suspected lung cancer in primary care based on electronic health record data 92%
Similar papers in this journal
- Aggregation of Cohorts for Histopathological Diagnosis with Deep Morphological Analysis 94%
- Scalable long-read Nanopore HPV16 Amplicon-based Whole-Genome Sequencing 93%
- Long-term maintenance of patient-specific characteristics in tumoroids from six cancer indications in a common base culture media system 92%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.