Deep learning model to predict Epstein–Barr virus associated gastric cancer in histology

Abstract

The detection of Epstein–Barr virus (EBV) in gastric cancer patients is crucial for clinical decision making, as it is related with specific treatment responses and prognoses. Despite its importance, the limited medical resources preclude universal EBV testing. Herein, we propose a deep learning-based EBV prediction method from H&E-stained whole-slide images (WSI). Our model was developed using 319 H&E stained WSI (26 EBV positive; TCGA dataset) from the Cancer Genome Atlas, and 108 WSI (8 EBV positive; ISH dataset) from an independent institution. Our deep learning model, EBVNet consists of two sequential components: a tumor classifier and an EBV classifier. We visualized the learned representation by the classifiers using UMAP. We externally validated the model using 60 additional WSI (7 being EBV positive; HGH dataset). We compared the model’s performance with those of four pathologists. EBVNet achieved an AUPRC of 0.65, whereas the four pathologists yielded a mean AUPRC of 0.41. Moreover, EBVNet achieved an negative predictive value, sensitivity, specificity, precision, and F1-score of 0.98, 0.86, 0.92, 0.60, and 0.71, respectively. Our proposed model is expected to contribute to prescreen patients for confirmatory testing, potentially to save test-related cost and labor.

Worldwide Prevalence of Epstein–Barr Virus in Patients with Burkitt Lymphoma: A Systematic Review and Meta-Analysis

“…Our literature search yielded 135 studies [ 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 ,…”

“…Machine learning and deep learning are two types of AI used in the medical field to evaluate medical data and acquire an understanding of the pathogenesis of diseases. Recently, an AI application used for EBV has been developed, such as a deep-learning-based EBV prediction method from H&E-stained whole-slide images (WSI) in gastric cancer [ 46 ], and deep-learning-based classifiers to detect microsatellite instability and EBV status directly from hematoxylin-and-eosin-stained histological slides [ 47 ]. In BL, artificial neural networks and various types of machine learning were used to analyze the gene expression and protein levels by immunohistochemistry of several hematological neoplasia and pan-cancer series in order to predict patients’ survival and the disease subtype classification with a high accuracy [ 48 ].…”

Artificial Intelligence in the Pathology of Gastric Cancer

“…In a study based on the Korean population, Jeong et al [ 34 ] developed a DL-based EBV prediction method from H&E-stained WSIs, using 319 slides from the TCGA dataset and 108 slides from an independent institution. An additional 60 WSIs from other institutions were used for external validation.…”