Bio-IT Research Center, Hallym University, Chuncheon, South Korea.
Department of Convergence Software, Hallym University, Chuncheon, South Korea.
Biomed Eng Online. 2018 Nov 6;17(Suppl 2):152. doi: 10.1186/s12938-018-0581-6.
Screening test using CA-125 is the most common test for detecting ovarian cancer. However, the level of CA-125 is diverse by variable condition other than ovarian cancer. It has led to misdiagnosis of ovarian cancer.
In this paper, we explore the 16 serum biomarker for finding alternative biomarker combination to reduce misdiagnosis. For experiment, we use the serum samples that contain 101 cancer and 92 healthy samples. We perform two major tasks: Marker selection and Classification. For optimal marker selection, we use genetic algorithm, random forest, T-test and logistic regression. For classification, we compare linear discriminative analysis, K-nearest neighbor and logistic regression.
The final results show that the logistic regression gives high performance for both tasks, and HE4-ELISA, PDGF-AA, Prolactin, TTR is the best biomarker combination for detecting ovarian cancer.
We find the combination which contains TTR and Prolactin gives high performance for cancer detection. Early detection of ovarian cancer can reduce high mortality rates. Finding a combination of multiple biomarkers for diagnostic tests with high sensitivity and specificity is very important.
CA-125 筛查试验是检测卵巢癌最常用的方法。然而，CA-125 的水平在卵巢癌以外的其他多种情况下存在差异，这导致了卵巢癌的误诊。
本文探讨了 16 种血清生物标志物，以寻找替代生物标志物组合，减少误诊。在实验中，我们使用了包含 101 例癌症和 92 例健康样本的血清样本。我们进行了两项主要任务：标志物选择和分类。为了进行最优标志物选择，我们使用了遗传算法、随机森林、t 检验和逻辑回归。对于分类，我们比较了线性判别分析、K-最近邻和逻辑回归。
最终结果表明，逻辑回归在这两个任务中都表现出了很高的性能，HE4-ELISA、PDGF-AA、催乳素、TTR 是检测卵巢癌的最佳生物标志物组合。
我们发现包含 TTR 和催乳素的组合在癌症检测中具有较高的性能。早期发现卵巢癌可以降低高死亡率。寻找具有高灵敏度和特异性的多种生物标志物组合的诊断测试非常重要。
