AI blood signature in common blood tests for detection of gastric cancer in a cohort of 190,000 individuals.

Abstract

1500 Background: Gastric cancer is the fourth leading cause of cancer death worldwide. Lack of symptoms in the early stage of disease leads to delayed presentation and low overall survival rate. Early detection is key, but there is not yet an established test for non-invasive gastric cancer screening. Routine blood test panels, including complete blood count, liver function, renal function and clotting profiles, could be potentially useful in reflecting bodily processes related to cancer. Current literature and territory-wide statistical data analyses from Hong Kong have shown these subtle changes and differences in measured levels of subcomponents between individuals with and without gastric cancer, that are not recognized clinically, form recognizable patterns distinct to gastric cancer. In this study, we hypothesized that gastric cancer signatures could be identified in routine blood data. Deep learning AI algorithms are used to identify the gastric cancer signature. Results from testing and validation of the identified routine blood signature using a Big Clinical Data cohort are reported. Methods: This is a territory-wide healthcare database study using data from Hong Kong Hospital Authority data collaboration laboratory. All patients prescribed with medications for dyspepsia and with all blood tests components (CBC, LFT, RFT, clotting function) available in the period between 2004-2015 inclusive were included. In the period, data from 2004 to 09 and 2011 to 14 were used as training cohort and the rest were used as testing cohort. A blood gastric cancer signature was generated using deep learning AI algorithms, and applied to predict gastric cancer in Big Data population. Results: 193,117 patients were included in the captioned period, in whom 4,790 patients were diagnosed to have gastric cancer. 151,449 (3,815 pts with gastric cancer and 147,634 pts disease free) patients were included In training cohort. In the testing cohort in 2010 and 2015 (975 pts with gastric cancer and 40,693 pts disease-free), the tumor signature yielded a sensitivity of 0.79, specificity of 0.99, positive predictive value of 0.95 and a negative predictive value of 0.99. Conclusions: The hypothesis of the presence of a distinctive gastric cancer signature from parameters in routine blood test data is tested and validated in a large population cohort with dyspepsia. The signature has high accuracy and high sensitivity with low false positive and false negative rates. Multicenter prospective testing is being conducted in cohort indicated for OGD in Hong Kong.[Table: see text]