Abstract 5921: Machine learning based detection of pancreatic tumors using the Dxcover® cancer platform

Abstract Pancreatic cancer is the 7th most deadly cancer worldwide with over 460,000 victims per year. In the current diagnostic pathway, carbohydrate antigen (CA) 19-9 serum test is the first method used for detection of pancreatic cancer; although, with poor positive predictive values reported, it cannot provide certain information about the presence of a pancreatic tumor or other surrounding tumors in symptomatic patients. Attenuated total reflection - Fourier transform infrared spectroscopy (ATR-FTIR) has demonstrated exceptional potential in human blood serum analysis for cancer diagnostics and its implementation in the clinical environment could represent a significant step forward in the early detection of pancreatic cancer. This proof-of-concept study was focused on the discrimination between both cancer versus healthy control samples, and cancer versus symptomatic control samples from patients with comorbidities and/or confounding diseases. The study aimed to investigate the use of the Dxcover® cancer platform as a novel approach for pancreatic cancer detection. Various machine learning algorithms were applied to discriminate between cancer (n=100) and healthy control samples (n=100), achieving results amounting to a sensitivity of 91.0 ± 5.6%, specificity of 87.6 ± 5.8%, and accuracy of 89.3 ± 3.8% with partial least squares-discriminant analysis (PLS-DA). Moreover, an area under the curve (AUC) equal to 0.9536 was obtained through receiving operating characteristic (ROC) analysis. The same algorithms were also applied to discriminate between cancer (n=35) and symptomatic control samples (n=35) achieving a balanced sensitivity and specificity over 75% with an AUC of 0.8436. Both discriminations underwent bootstrapping validation and were proven statistically significant. Herein, we present these results and demonstrate that ATR-FTIR spectroscopic analysis of serum is a cost-effective, minimally invasive, highly sensitive and specific test for detection of pancreatic cancer. Citation Format: Alexandra Sala, James M. Cameron, Cerys A. Jenkins, Hugh Barr, Loren Christie, Justin J. Conn, Thomas (Jeff) R. Evans, Dean A. Harris, David S. Palmer, Christopher Rinaldi, Ashton G. Theakstone, Matthew J. Baker. Machine learning based detection of pancreatic tumors using the Dxcover® cancer platform [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5921.