Department of Gynecology, The First Affiliated Hospital of Jinan University, Guangzhou, 510632, China.
Department of Obstetrics and Gynecology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
Curr Med Sci. 2020 Feb;40(1):184-191. doi: 10.1007/s11596-020-2163-7. Epub 2020 Mar 13.
To determine whether ultrasound features can improve the diagnostic performance of tumor markers in distinguishing ovarian tumors, we enrolled 719 patients diagnosed as having ovarian tumors at Nanfang Hospital from September 2014 to November 2016. Age, menopausal status, histopathology, the International Federation of Gynecology and Obstetrics (FIGO) stages, tumor biomarker levels, and detailed ultrasound reports of patients were collected. The area under the curve (AUC), sensitivity, and specificity of the bellow-mentioned predictors were analyzed using the receiver operating characteristic curve. Of the 719 patients, 531 had benign lesions, 119 had epithelial ovarian cancers (EOC), 44 had borderline ovarian tumors (BOT), and 25 had non-EOC. AUCs and the sensitivity of cancer antigen 125 (CA125), human epididymis-specific protein 4 (HE4), Risk of Ovarian Malignancy Algorithm (ROMA), Risk of Malignancy Index (RMI1), HE4 model, and Rajavithi-Ovarian Cancer Predictive Score (R-OPS) in the overall population were 0.792, 0.854, 0.856, 0.872, 0.893, 0.852, and 70.2%, 56.9%, 69.1%, 60.6%, 77.1%, 71.3%, respectively. For distinguishing EOC from benign tumors, the AUCs and sensitivity of the above mentioned predictors were 0.888, 0.946, 0.947, 0.949, 0.967, 0.966, and 84.0%, 79.8%, 87.4%, 84.9%, 90.8%, 89.1%, respectively. Their specificity in predicting benign diseases was 72.9%, 94.4%, 87.6%, 95.9%, 86.3%, 90.8%, respectively. Therefore, we consider biomarkers in combination with ultrasound features may improve the diagnostic performance in distinguishing malignant from benign ovarian tumors.
为了确定超声特征是否可以提高肿瘤标志物在鉴别卵巢肿瘤中的诊断性能，我们纳入了 2014 年 9 月至 2016 年 11 月在南方医院诊断为卵巢肿瘤的 719 名患者。收集了患者的年龄、绝经状态、组织病理学、国际妇产科联盟（FIGO）分期、肿瘤标志物水平和详细的超声报告。使用受试者工作特征曲线分析了以下预测因子的曲线下面积（AUC）、敏感性和特异性。在 719 名患者中，531 例为良性病变，119 例为上皮性卵巢癌（EOC），44 例为交界性卵巢肿瘤（BOT），25 例为非 EOC。在总体人群中，CA125、人附睾蛋白 4（HE4）、卵巢恶性肿瘤风险算法（ROMA）、恶性指数 1（RMI1）、HE4 模型和 Rajavithi-卵巢癌预测评分（R-OPS）的 AUC 和敏感性分别为 0.792、0.854、0.856、0.872、0.893、0.852 和 70.2%、56.9%、69.1%、60.6%、77.1%、71.3%。对于区分 EOC 与良性肿瘤，上述预测因子的 AUC 和敏感性分别为 0.888、0.946、0.947、0.949、0.967、0.966 和 84.0%、79.8%、87.4%、84.9%、90.8%、89.1%。它们预测良性疾病的特异性分别为 72.9%、94.4%、87.6%、95.9%、86.3%、90.8%。因此，我们认为生物标志物与超声特征相结合可能会提高鉴别良恶性卵巢肿瘤的诊断性能。
