Department of Ultrasound, Zhejiang Provincial People's Hospital, Hangzhou, 310014, Zhejiang Province, China.
Department of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150001, Heilongjiang Province, China.
J Ovarian Res. 2023 Mar 21;16(1):57. doi: 10.1186/s13048-023-01133-1.
The accurate preoperative differentiation of benign and malignant adnexal masses, especially those with complex ultrasound morphology, remains a great challenge for junior sonographers. The purpose of this study was to develop and validate a nomogram based on the Ovarian-Adnexal Reporting and Data System (O-RADS) for predicting the malignancy risk of adnexal masses with complex ultrasound morphology.
A total of 243 patients with data on adnexal masses with complex ultrasound morphology from January 2019 to December 2020 were selected to establish the training cohort, while 106 patients with data from January 2021 to December 2021 served as the validation cohort. Univariate and multivariate analyses were used to determine independent risk factors for malignant tumors in the training cohort. Subsequently, a predictive nomogram model was developed and validated in the validation cohort. The calibration, discrimination, and clinical net benefit of the nomogram model were assessed separately by calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA). Finally, we compared this model to the O-RADS.
The O-RADS category, an elevated CA125 level, acoustic shadowing and a papillary projection with color Doppler flow were the independent predictors and were incorporated into the nomogram model. The area under the ROC curve (AUC) of the nomogram model was 0.958 (95% CI, 0.932-0.984) in the training cohort. The specificity and sensitivity were 0.939 and 0.893, respectively. This nomogram also showed good discrimination in the validation cohort (AUC = 0.940, 95% CI, 0.899-0.981), with a sensitivity of 0.915 and specificity of 0.797. In addition, the nomogram model showed good calibration efficiency in both the training and validation cohorts. DCA indicated that the nomogram was clinically useful. Furthermore, the nomogram model had higher AUC and net benefit than the O-RADS.
The nomogram based on the O-RADS showed a good predictive ability for the malignancy risk of adnexal masses with complex ultrasound morphology and could provide help for junior sonographers.
准确区分良性和恶性附件肿块,尤其是那些具有复杂超声形态的肿块,对于初级超声医师来说仍然是一个巨大的挑战。本研究的目的是开发和验证一个基于卵巢附件报告和数据系统(O-RADS)的列线图,用于预测具有复杂超声形态的附件肿块的恶性风险。
选取 2019 年 1 月至 2020 年 12 月具有复杂超声形态的附件肿块数据的 243 例患者作为训练队列,而 2021 年 1 月至 2021 年 12 月具有数据的 106 例患者作为验证队列。使用单因素和多因素分析确定训练队列中恶性肿瘤的独立危险因素。随后,在验证队列中开发和验证预测列线图模型。分别通过校准曲线、接受者操作特征(ROC)曲线和决策曲线分析(DCA)评估列线图模型的校准、判别和临床净获益。最后,我们将该模型与 O-RADS 进行了比较。
O-RADS 类别、CA125 水平升高、声影和彩色多普勒血流中的乳头状突起是独立的预测因素,并被纳入列线图模型。列线图模型在训练队列中的 ROC 曲线下面积(AUC)为 0.958(95%CI,0.932-0.984)。特异性和敏感性分别为 0.939 和 0.893。该列线图在验证队列中也具有良好的判别能力(AUC=0.940,95%CI,0.899-0.981),敏感性为 0.915,特异性为 0.797。此外,该列线图模型在训练和验证队列中均显示出良好的校准效率。DCA 表明该列线图具有临床应用价值。此外,该列线图模型的 AUC 和净获益均高于 O-RADS。
基于 O-RADS 的列线图对具有复杂超声形态的附件肿块的恶性风险具有良好的预测能力,可为初级超声医师提供帮助。