Department of Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No.1 Shuai Fu Yuan, Dong Cheng District, Beijing, 100730, China.
Sci Rep. 2023 Sep 21;13(1):15667. doi: 10.1038/s41598-023-42836-1.
The aim of this study was to validate the performance of the Ovarian-Adnexal Reporting and Data Systems (O-RADS) series models proposed by the American College of Radiology (ACR) in the preoperative diagnosis of adnexal masses (AMs). Two experienced sonologists examined 218 patients with AMs and gave the assessment results after the examination. Pathological findings were used as a reference standard. Of the 218 lesions, 166 were benign and 52 were malignant. Based on the receiver operating characteristic (ROC) curve, we defined a malignant lesion as O-RADS > 3 (i.e., lesions in O-RADS categories 4 and 5 were malignant). The area under the curve (AUC) of O-RADS (v2022) was 0.970 (95% CI 0.938-0.988), which wasn't statistically significantly different from the O-RADS (v1) combined Simple Rules Risk (SRR) assessment model with the largest AUC of 0.976 (95% CI 0.946-0.992) (p = 0.1534), but was significantly higher than the O-RADS (v1) (AUC = 0.959, p = 0.0133) and subjective assessment (AUC = 0.918, p = 0.0255). The O-RADS series models have good diagnostic performance for AMs. Where, O-RADS (v2022) has higher accuracy and specificity than O-RADS (v1). The accuracy and specificity of O-RADS (v1), however, can be further improved when combined with SRR assessment.
本研究旨在验证美国放射学院(ACR)提出的卵巢-附件报告和数据系统(O-RADS)系列模型在附件肿块(AMs)术前诊断中的性能。两位经验丰富的超声科医生检查了 218 名 AMs 患者,并在检查后给出了评估结果。病理发现被用作参考标准。在 218 个病灶中,166 个为良性,52 个为恶性。基于受试者工作特征(ROC)曲线,我们将恶性病变定义为 O-RADS>3(即 O-RADS 类别 4 和 5 的病变为恶性)。O-RADS(v2022)的曲线下面积(AUC)为 0.970(95%CI 0.938-0.988),与 AUC 最大的 O-RADS(v1)联合简单规则风险(SRR)评估模型(0.976,95%CI 0.946-0.992)(p=0.1534)无统计学差异,但明显高于 O-RADS(v1)(AUC=0.959,p=0.0133)和主观评估(AUC=0.918,p=0.0255)。O-RADS 系列模型对 AMs 具有良好的诊断性能。其中,O-RADS(v2022)的准确性和特异性均高于 O-RADS(v1)。然而,当与 SRR 评估相结合时,O-RADS(v1)的准确性和特异性可以进一步提高。