A novel diagnostic nomogram based on serological and ultrasound findings for preoperative prediction of malignancy in patients with ovarian masses

• Published in: Gynecologic oncology Vol. 160; no. 3; pp. 704 - 712
• Main Authors: Guo, Yunyun, Jiang, Tengjia, Ouyang, Linglong, Li, Xiaohui, He, Weipeng, Zhang, Zuwei, Shen, Hongwei, You, Zeshan, Yang, Guofen, Lai, Huiling
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.03.2021
• Subjects: Diagnosis; Hematology, Oncology, and Palliative Medicine; Nomogram; Obstetrics and Gynecology; Ovarian mass; Risk of malignancy; Ovarian mass; Risk of malignancy; Diagnosis; Nomogram
• DOI: 10.1016/j.ygyno.2020.12.006
• ISSN: 0090-8258; 1095-6859

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. •A novel diagnostic nomogram based on age, CA125, FAR, MLR, and ultrasound result accurately predicts malignancy in patients with ovarian masses•This nomogram is easily accessible and has a higher diagnostic efficiency than ROMA, CPH-I, and RMI•Our model also has good diagnostic performance in patients with early-stage ovarian cancer