Background/Objectives: 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.Methods: 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.Results: 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.Conclusions: The nine-protein signature may help detect breast cancer more accurately and effectively than the three-protein signature.
背景/目的：本研究采用新开发的九蛋白血清标志物组合，旨在提升现有三蛋白标志物作为血液诊断工具的准确性。通过该九蛋白标志物组合，评估了用于测试人工智能算法临床性能的医疗器械的临床敏感性与特异性。方法：采用基于质谱的多反应监测技术对243名健康对照者和222名乳腺癌患者的血清样本进行检测，定量分析九种蛋白质（APOC1、CHL1、FN1、VWF、PPBP、CLU、PRDX6、PRG4和MMP9）。结果：基于人工智能深度学习模型确定的临界值，九蛋白标志物组合在所有参与者中诊断乳腺癌的敏感性和特异性分别为83.3%和88.1%，而三蛋白标志物组合的相应值分别为71.6%和85.3%。该检测方法对乳腺癌的阳性预测值为86.5%，对健康对照的阳性预测值为13.6%，相应的阴性预测值分别为14.7%和85.3%。九蛋白与三蛋白标志物组合的准确率分别为85.8%（受试者工作特征曲线下面积：0.8526）和77.0%。结论：与三蛋白标志物组合相比，九蛋白标志物组合可能有助于更准确、更有效地检测乳腺癌。
肿瘤(癌症)患者之家