Department of Gynecology, the First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510060, Guangdong, PR China.
State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Department of Oncology, Sun Yat-Sen University Cancer Center, Guangzhou 510060, Guangdong, PR China.
Gynecol Oncol. 2021 Mar;160(3):704-712. doi: 10.1016/j.ygyno.2020.12.006. Epub 2020 Dec 24.
To develop a novel diagnostic nomogram model to predict malignancy in patients with ovarian masses.
In total, 1277 patients with ovarian masses were retrospectively analyzed. Receiver operating characteristic (ROC) analysis was performed to identify valuable predictive factors. Univariate and multivariate logistic regression analyses were used to identify risk factors for ovarian cancer. Subsequently, a predictive nomogram model was developed. The performance of the nomogram model was assessed by its calibration and discrimination in a validation cohort. Decision curve analysis (DCA) was applied to assess the clinical net benefit of the model.
Overall, 496 patients (38.8%) had ovarian cancer. Eighteen parameters were significantly different between the malignant and benign groups. Five parameters were identified as being most optimal for predicting malignancy, including age, carbohydrate antigen 125, fibrinogen-to-albumin ratio, monocyte-to-lymphocyte ratio, and ultrasound result. These parameters were incorporated to establish a nomogram model, and this model exhibited an area under the ROC curve (AUC) of 0.937 (95% confidence interval [CI], 0.920-0.954). The model was also well calibrated in the validation cohort and showed an AUC of 0.925 (95%CI, 0.896-0.953) at the cut-off point of 0.298. DCA confirmed that the nomogram model achieved the best clinical utility with almost the entire range of threshold probabilities. The model has demonstrated superior efficacy in predicting malignancy compared to currently available models, including the risk of ovarian malignancy algorithm, copenhagen index, and the risk of malignancy index. More importantly, the nomogram established here showed potential value in identification of early-stage ovarian cancer.
The cost-effective and easily accessible nomogram model exhibited favorable accuracy for preoperative prediction of malignancy in patients with ovarian masses, even at early stages.
开发一种新的诊断列线图模型，以预测卵巢肿块患者的恶性肿瘤。
回顾性分析了 1277 例卵巢肿块患者。进行受试者工作特征（ROC）分析以确定有价值的预测因素。使用单因素和多因素逻辑回归分析确定卵巢癌的危险因素。随后，开发了预测列线图模型。在验证队列中，通过校准和判别评估列线图模型的性能。决策曲线分析（DCA）用于评估模型的临床净获益。
共有 496 例（38.8%）患者患有卵巢癌。恶性和良性组之间有 18 个参数存在显著差异。确定了 5 个参数对预测恶性肿瘤最有意义，包括年龄、糖链抗原 125、纤维蛋白原与白蛋白比值、单核细胞与淋巴细胞比值和超声结果。这些参数被纳入建立列线图模型，该模型的 ROC 曲线下面积（AUC）为 0.937（95%置信区间 [CI]，0.920-0.954）。该模型在验证队列中也具有良好的校准能力，在截止值为 0.298 时 AUC 为 0.925（95%CI，0.896-0.953）。DCA 证实，该列线图模型在几乎整个阈值概率范围内具有最佳的临床实用性。该模型在预测卵巢肿块患者的恶性肿瘤方面表现出比目前可用的模型更好的疗效，包括卵巢恶性肿瘤风险算法、哥本哈根指数和恶性肿瘤风险指数。更重要的是，这里建立的列线图在识别早期卵巢癌方面显示出潜在的价值。
这种具有成本效益且易于获取的列线图模型在预测卵巢肿块患者的恶性肿瘤方面具有良好的准确性，甚至在早期阶段也是如此。
