Teng Fei, Xie Honglei, Wei Hong, Che Dehong, Wang Hongbo, Wu Chengwei, He Xin, Dong Xiaoqiu
In-Patient Ultrasound Department, Second Affiliated Hospital of Harbin Medical University, Harbin, 150081, China.
Ultrasound Department, Fourth Affiliated Hospital of Harbin Medical University, Harbin, 150001, China.
Br J Radiol. 2025 Mar 1;98(1167):448-457. doi: 10.1093/bjr/tqae247.
This study aimed to assess the diagnostic efficacy of the ovarian adnexal reporting and data system (O-RADS) and ultrasound (US) and its sub-classification system for distinguishing ovarian masses.
O-RADS US was used for the retrospective analysis of 606 ovarian masses of Chinese from 2 medical centres by 2 gynaecologic sonographers with varying experience. The O-RADS 4 categories masses were further sub-classified into O-RADS 4a and O-RADS 4b through 3 different approaches (O-RADS A1/A2/A3).
The AUC of O-RADS US for differentiating benign from malignant ovarian masses was 0.927 (95% CI, 0.903-0.946, P < .001). The optimal cut-off value for predicting malignancy was >O-RADS 3, with sensitivity and specificity of 98.60% and 68.90%, respectively. The diagnostic efficacy of the 3 sub-classification systems surpassed that of O-RADS US (P < .05). Specifically, A2 approach (within O-RADS 4 lesions, unilocular and multilocular cysts with solid components were sub-classified as O-RADS 4b, whereas the remaining O-RADS 4 lesions were sub-classified as O-RADS 4a) resulted in an AUC of 0.942 (95% CI, 0.921-0.960, P < .001). The best cut-off value predicting malignancy was >O-RADS 4a, exhibiting relatively high specificity (82.51%) and maintaining a high sensitivity (93.01%).
The diagnostic efficacy of O-RADS US for identifying ovarian tumours is good, but specificity is slightly lower. This study enhanced diagnostic specificity after subclassifying O-RADS 4 lesions, especially A2 approach. It holds significant clinical value for Chinese women and merits further clinical promotion and application.
The sub-classification of O-RADS US allows better identifying ovarian tumours, facilitating informed preoperative clinical management and diagnosis.
本研究旨在评估卵巢附件报告和数据系统（O-RADS）及超声（US）及其亚分类系统在鉴别卵巢肿块方面的诊断效能。
由两名经验不同的妇科超声医师，采用O-RADS US对来自两个医疗中心的606例中国患者的卵巢肿块进行回顾性分析。O-RADS 4类肿块通过3种不同方法（O-RADS A1/A2/A3）进一步细分为O-RADS 4a和O-RADS 4b。
O-RADS US鉴别卵巢良恶性肿块的AUC为0.927（95%CI，0.903-0.946，P＜0.001）。预测恶性肿瘤的最佳截断值为＞O-RADS 3，敏感性和特异性分别为98.60%和68.90%。3种亚分类系统的诊断效能超过了O-RADS US（P＜0.05）。具体而言，A2方法（在O-RADS 4类病变中，单房和多房含实性成分囊肿细分为O-RADS 4b，其余O-RADS 4类病变细分为O-RADS 4a）的AUC为0.942（95%CI，0.921-0.960，P＜0.001）。预测恶性肿瘤的最佳截断值为＞O-RADS 4a，特异性相对较高（82.51%），且敏感性保持在较高水平（93.01%）。
O-RADS US识别卵巢肿瘤的诊断效能良好，但特异性略低。本研究通过对O-RADS 4类病变进行亚分类提高了诊断特异性，尤其是A2方法。对中国女性具有重要临床价值，值得进一步临床推广应用。
O-RADS US的亚分类有助于更好地识别卵巢肿瘤，便于术前进行合理的临床管理和诊断。
