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Combined use of biomarkers for detection of ovarian cancer in high-risk women

机译:结合使用生物标志物检测高危女性的卵巢癌

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The aim of this study was to examine the negative predictive value for a panel of serum markers in women at high risk for developing ovarian cancer. A total of 201 serum samples were collected and analyzed from 102 women at “high risk” for ovarian cancer: 26 with primary ovarian cancer, 31 with recurrent ovarian cancer, 28 with benign gynecologic diseases, and 14 with other cancers. Samples were tested for cancer antigen (CA) 125 II, CA19-9, CA72-4, CA15-3, and macrophage colony-stimulating factor, OVX1, and the marker values were further used as input to be evaluated by a previously trained artificial neural network (ANN). CA125 alone identified 72% of the primary ovarian cancers at a specificity of 95%. If either CA125 or CA72-4 were elevated, sensitivity rose to 80%. Adding macrophage colony-stimulating factor–improved sensitivity to 84% and when CA15-3 was included, a sensitivity of 88% was achieved. Specificity of the four marker panel was, however, reduced to 82.5%. By contrast, at the same sensitivity of 88%, the ANN exhibited a much higher specificity at 92.5% (p = 0.0105). Our data suggest that the combined use of multiple biomarkers improve sensitivity in women at high risk for ovarian cancer. In contrast to the simple “or” combination rule, the ANN was able to achieve a higher sensitivity without significant loss in specificity.
机译:这项研究的目的是检查一组高血清卵巢癌女性的血清标志物阴性预测值。从102位处于“高危”卵巢癌女性中收集了201份血清样本,并进行了分析:26例原发性卵巢癌,31例复发性卵巢癌,28例良性妇科疾病和14例其他癌症。测试样品的癌症抗原(CA)125 II,CA19-9,CA72-4,CA15-3和巨噬细胞集落刺激因子OVX1,并将标记物的值进一步用作输入值,以便由先前训练有素的人工方法进行评估神经网络(ANN)。仅CA125就以95%的特异性鉴定出72%的原发性卵巢癌。如果CA125或CA72-4升高,灵敏度将上升至80%。添加巨噬细胞集落刺激因子可使敏感性提高到84%,当包含CA15-3时,敏感性达到88%。然而,四个标记物组的特异性降低到82.5%。相比之下,在88%的相同灵敏度下,ANN的特异性更高,为92.5%(p = 0.0105)。我们的数据表明,多种生物标志物的联合使用可提高处于卵巢癌高风险女性的敏感性。与简单的“或”组合规则相反,ANN能够实现更高的灵敏度,而不会显着降低特异性。

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