Effectiveness of Multi-Layer Perceptron-Based Binary Classification Neural Network in Detecting Breast Cancer Through Nine Human Serum Protein Markers

<b>Background/Objectives</b>: A newly developed nine-protein serum signature has been utilized to enhance the accuracy of an existing three-protein signature used as a blood-based diagnostic tool. This study used the new nine-protein serum signature to evaluate the clinical sensitivity and specificity of a medical device designed to test the clinical performance of an artificial intelligence algorithm. <b>Methods</b>: A blood-based test using multiple reaction monitoring via mass spectrometry was performed to quantify nine proteins (APOC1, CHL1, FN1, VWF, PPBP, CLU, PRDX6, PRG4, and MMP9) in serum samples from 243 healthy controls and 222 patients with breast cancer. <b>Results</b>: Based on cutoff values determined by an artificial intelligence-based deep learning model, the sensitivity and specificity of the nine-protein signature in diagnosing breast cancer among all participants was 83.3% and 88.1%, respectively, whereas those of the three-protein signature were 71.6% and 85.3%, respectively. The assay yielded a positive predictive value of 86.5% for breast cancer and 13.6% for healthy controls, with corresponding negative predictive values of 14.7% and 85.3%, respectively. The accuracies of nine- and three-protein signatures were 85.8% (area under the receiver operating characteristic curve: 0.8526) and 77.0%, respectively. <b>Conclusions</b>: The nine-protein signature may help detect breast cancer more accurately and effectively than the three-protein signature.