Li Lu, Ma Xuhui
Department of Oncology, Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou 450008, China.
Saudi J Biol Sci. 2017 Mar;24(3):644-648. doi: 10.1016/j.sjbs.2017.01.037. Epub 2017 Jan 26.
We aimed to evaluate the specificity of 12 tumor markers related to colon carcinoma and identify the most sensitive index. Logistic regression and Bhattacharyya distance were used to evaluate the index. Then, different index combinations were used to establish a support vector machine (SVM) diagnosis model of malignant colon carcinoma. The accuracy of the model was checked. High accuracy was assumed to indicate the high specificity of the index. Through Logistic regression, three indexes, CEA, HSP60 and CA199, were screened out. Using Bhattacharyya distance, four indexes with the largest Bhattacharyya distance were screened out, including CEA, NSE, AFP, and CA724. The specificity of the combination of the above six indexes was higher than that of other combinations, so did the accuracy of the established SVM identification model. Using Logistic regression and Bhattacharyya distance for detection and establishing an SVM model based on different serum marker combinations can increase diagnostic accuracy, providing a theoretical basis for application of mathematical models in cancer diagnosis.
我们旨在评估12种与结肠癌相关的肿瘤标志物的特异性，并确定最敏感的指标。采用逻辑回归和巴氏距离来评估该指标。然后，使用不同的指标组合建立恶性结肠癌的支持向量机（SVM）诊断模型。检查模型的准确性。假设高准确性表明该指标具有高特异性。通过逻辑回归，筛选出三个指标，即癌胚抗原（CEA）、热休克蛋白60（HSP60）和糖类抗原199（CA199）。使用巴氏距离，筛选出巴氏距离最大的四个指标，包括CEA、神经元特异性烯醇化酶（NSE）、甲胎蛋白（AFP）和糖类抗原724（CA724）。上述六个指标组合的特异性高于其他组合，所建立的支持向量机识别模型的准确性也是如此。使用逻辑回归和巴氏距离进行检测，并基于不同的血清标志物组合建立支持向量机模型，可以提高诊断准确性，为数学模型在癌症诊断中的应用提供理论依据。
