Abstract
Hepatocellular carcinoma (HCC) is one of the fastest growing causes of cancer-related death. Guidelines recommend obtaining a screening ultrasound with or without alpha-fetoprotein (AFP) every 6 months in at-risk adults. AFP as a screening biomarker is plagued by low sensitivity/specificity, prompting interest in discovering alternatives. Mass spectrometry-based techniques are promising in their ability to identify potential biomarkers. This study aimed to use machine learning utilizing spectral data and AFP to create a model for early detection. Serum samples were collected from three separate cohorts, and data were compiled to make Development, Internal Validation, and Independent Validation sets. AFP levels were measured, and Deep MALDI® analysis was used to generate mass spectra. Spectral data were input into the VeriStrat® classification algorithm. Machine learning techniques then classified each sample as "Cancer" or "No Cancer". Sensitivity and specificity of the test were >80% to detect HCC. High specificity of the test was independent of cause and severity of underlying disease. When compared to AFP, there was improved cancer detection for all tumor sizes, especially small lesions. Overall, a machine learning algorithm incorporating mass spectral data and AFP values from serum samples offers a novel approach to diagnose HCC. Given the small sample size of the Independent Validation set, a further independent, prospective study is warranted.