He Gang, Holcroft Christina A, Beauchamp Marie-Claude, Yasmeen Amber, Ferenczy Alex, Kendall-Dupont Jennifer, Mes-Masson Anne-Marie, Provencher Diane, Gotlieb Walter H
Department of Diagnostic Medicine, Jewish General Hospital, McGill University, Montreal QC.
Centre for Clinical Epidemiology, Jewish General Hospital, McGill University, Montreal QC.
J Obstet Gynaecol Can. 2012 Jun;34(6):567-574. doi: 10.1016/S1701-2163(16)35273-2.
To investigate biomarkers and clinical parameters to distinguish ovarian cancers from benign ovarian tumours.
Serum biomarkers (CA 125, human epididymis protein 4 [HE 4], interleukin-18 [IL-18], leptin, macrophage migration inhibitory factor [MIF], fibroblast growth factor 2 [FGF-2], insulin-like growth factor, osteopontin, prolactin) and the risk of malignancy indexes I and II (RMI-I and RMI-II) scores were obtained prior to surgery in 52 patients with ovarian tumours (37 malignant and 15 benign). ROC curves were built for each individual marker, for logistic regression models using all markers, and for models combining both biomarkers and RMI scores.
The model with nine biomarkers performed well (specificity 93%, sensitivity 84%) and was more reliable than the RMI-I or RMI-II alone. A regression model combining RMI-II and six of the biomarkers (CA 125, HE 4, IL-18, leptin, MIF, and FGF-2) allowed differentiation between the cancer and non-cancer cases in this pilot study.
The regression models using biomarkers combined with clinical scoring systems warrant further investigation to improve triage of patients with ovarian tumours to enhance utilization of resources and optimize patient care.
研究用于区分卵巢癌与卵巢良性肿瘤的生物标志物和临床参数。
在52例卵巢肿瘤患者（37例恶性肿瘤患者和15例良性肿瘤患者）手术前获取血清生物标志物（CA 125、人附睾蛋白4 [HE 4]、白细胞介素-18 [IL-18]、瘦素、巨噬细胞移动抑制因子[MIF]、成纤维细胞生长因子2 [FGF-2]、胰岛素样生长因子、骨桥蛋白、催乳素）以及恶性风险指数I和II（RMI-I和RMI-II）评分。针对每个单独的标志物、使用所有标志物的逻辑回归模型以及结合生物标志物和RMI评分的模型构建ROC曲线。
包含9种生物标志物的模型表现良好（特异性93%，敏感性84%），且比单独的RMI-I或RMI-II更可靠。在这项初步研究中，一个结合RMI-II和6种生物标志物（CA 125、HE 4、IL-18、瘦素、MIF和FGF-2）的回归模型能够区分癌症和非癌症病例。
使用生物标志物结合临床评分系统的回归模型值得进一步研究，以改善卵巢肿瘤患者的分诊，提高资源利用效率并优化患者护理。
