Deep-learning model AIBISI predicts bacterial infection across cancer types based on pathological images

Abstract

Microorganisms play an important role in many physiological functions. Many studies have found that bacteria also regulate cancer susceptibility and tumor progression by affecting some metabolic or immune system signaling pathways. However, current bacterial detection methods are inaccurate or inefficient. Thus, we constructed a deep neural network (AIBISI) based on hematoxylin and eosin (H&E)-stained pathology slides to predict and visualize bacterial infection. Our model performance achieved as high as 0.81 of AUC (area under the ROC curve) within cancer type. We also built a pan-cancer model to predict bacterial infection across cancer types. To facilitate clinical usage, AIBISI visualized image areas affected by possible infection. Importantly, we successfully validated our model (AUC = 0.755) in pathological images from an independent patient cohort of stomach cancer (n = 32). To our best knowledge, this is the first artificial intelligence (AI)-based model to investigate bacterial infection in pathology images and has the potential to enable fast clinical decision related to pathogens in tumors.