卵巢-附件影像报告和数据系统鉴别101例卵巢附件良恶性肿物的应用价值

doi:10.6040/j.issn.1671-7554.0.2021.0048

摘要/Abstract

摘要： 目的探讨卵巢-附件影像报告和数据系统(O-RADS)在鉴别卵巢附件良恶性肿物上的应用价值。方法回顾性分析2018年11月至2020年11月行超声检查并经手术病理证实的101例卵巢附件肿物患者的超声图像,评估所有图像特征,使用O-RADS进行分类,以病理结果为金标准,绘制受试者工作特征(ROC)曲线,计算O-RADS对恶性卵巢附件肿物的诊断效能。结果 (1) O-RADS 2~5类卵巢附件肿物恶性率分别为0%、3.85%、27.78%、68.75%;(2) 不同医师对O-RADS诊断一致性的加权Kappa系数为0.84,P<0.001;(3) O-RADS诊断恶性卵巢附件肿物的曲线下面积(AUC)为0.83,截断值为O-RADS 5类,其诊断恶性卵巢附件肿物的敏感度、特异度、阳性预测值、阴性预测值、准确率分别为66.67%、85.29%、68.75%、84.06%、79.21%。结论 O-RADS 对于鉴别卵巢附件良恶性肿物具有一定的应用价值。

关键词: 超声, 卵巢, 附件, 肿物, 影像报告和数据系统

Abstract: Objective To explore the value of ovarian-adnexal reporting and data system(O-RADS)in differentiating benign and malignant ovarian-adnexal masses. Methods Totally, 101 patients who were diagnosed as ovarian or adnexal diseases were evaluated by ultrasound from November 2018 to November 2020. The ultrasonic images were analyzed retrospectively using O-RADS to categorize the patients. Taken pathological results as the gold standard, the receiver operating characteristic(ROC)curve was described to get the diagnostic efficacy. Results (1) The incidence rate of malignancies of O-RADS 2-5 types were 0%, 3.85%, 27.78%, and 68.75%, respectively. (2) The weighted Kappa coefficient between two different sonographers diagnoses was 0.84, P<0.001. (3) The area under the curve(AUC)of O-RADS was 0.83, cutoff value was O-RADS 5 type. The sensitivity, specificity, positive predictive value, negative predictive value, accuracy were 66.67%, 85.29%, 68.75%, 84.06%, and 79.21%, respectively. Conclusion The value of O-RADS is relatively high in differentiating benign and malignant ovarian-adnexal masses.

Key words: Ultrasonography, Ovarian, Adnexal, Mass, Reporting and data system

中图分类号:

R445.1

王艺璇,李菲,胡蓉,王蓓. 卵巢-附件影像报告和数据系统鉴别101例卵巢附件良恶性肿物的应用价值[J]. 山东大学学报 (医学版), 2021, 59(4): 87-92.

WANG Yixuan, LI Fei, HU Rong, WANG Bei. Value of ovarian-adnexal reporting and data system in differentiating 101 benign and malignant ovarian-adnexal masses[J]. Journal of Shandong University (Health Sciences), 2021, 59(4): 87-92.

参考文献

[1] 郑荣寿,孙可欣,张思维,等.2015年中国恶性肿瘤流行情况分析[J].中华肿瘤杂志,2019, 41(1): 19-28. ZHENG Rongshou, SUN Kexin, ZHANG Siwei, et al. Report of cancer epidemiology in China, 2015[J]. Chinese Journal of Oncology, 2019, 41(1): 19-28.
[2] 周永美. 血清CA125、CA724、AFP联合检测在卵巢肿瘤鉴别诊断及临床分期中的应用[J]. 山东医药, 2014, 54(24): 56-57.
[3] Gao YQ, Cheng HY, Liu KF. Long non-coding RNA DANCR upregulates IGF2 expression and promotes ovarian cancer progression[J]. Eur Rev Med Pharmacol Sci, 2019, 23(9): 3621-3626.
[4] Oliveira D, Carlsen AL, Heegaard N, et al. Diagnostic plasma miRNA-profiles for ovarian cancer in patients with pelvic mass[J]. PLoS One, 2019, 14(11): e0225249. doi:10.1371/journal.pone.0225249.
[5] Hu Y, Zhu Y, Zhang W, et al. Utility of plasma circBNC2 as a diagnostic biomarker in epithelial ovarian cancer[J]. Onco Targets Ther, 2019, 12: 9715-9723. doi: 10.2147/OTT.S211413.
[6] 候广霞,韩阳.血清指标结合彩色多普勒超声在早期诊断老年卵巢癌中的临床意义[J].中国老年学杂志, 2020, 40(12): 70-72.
[7] 潘淑淑,沈起钧,陈文辉,等.增强CT影像组学鉴别卵巢良性与交界性浆液性肿瘤[J]. 中国介入影像与治疗学, 2020, 17(6): 41-45. PAN Shushu, SHEN Qijun, CHEN Wenhui, et al. Radiomic of enhanced CT for identification of benign and borderline serous tumors of ovary[J]. Chinese Journal of Interventional Imaging and Therapy, 2020, 17(6): 41-45.
[8] 方如旗,曹代荣,翁淑萍,等.磁共振动态增强及扩散加权成像对复杂性卵巢肿瘤定性诊断的价值[J].临床放射学杂志, 2014, 33(11): 1705-1709. FANG Ruqi, CAO Dairong, WENG Shuping, et al. Evaluation of dynamic contrast-enhanced MR imaging and diffusion weighted MR imaging in qualitatively diagnosing complex ovarian tumors[J]. Journal of Clinical Radioloy, 2014, 33(11): 1705-1709.
[9] 韩运生,沈吉,丁耘峰.超声在卵巢肿瘤及预后分析中的临床应用[J].中华全科医学, 2018, 16(3): 456-460. HAN Yunsheng, SHEN Ji, DING Yunfeng. Clinical application of ultrasound in the diagnosis and prognosis of ovarian tumors[J]. Chinese Journal of General Practice, 2018, 16(3): 456-460.
[10] Hafeez S, Sufian S, Beg M, et al. Role of ultrasound in characterization of ovarian masses[J]. Asian Pac J Cancer Prev, 2013, 14(1): 603-606.
[11] 肖艳菊,郑红雨,杜阳春,等.声脉冲辐射力弹性成像对良、恶性卵巢肿瘤的鉴别诊断价值[J].广西医科大学学报, 2020, 37(1): 83-86. XIAO Yanju, ZHENG Hongyu, DU Yangchun, et al. The value of acoustic pulse radiation force elastography in the differential diagnosis of benign and malignant ovarian tumors[J]. Journal of Guangxi Medical University, 2020, 37(1): 83-86.
[12] Andreotti RF, Timmerman D, Benacerraf BR, et al. Ovarian-adnexal reporting lexicon for ultrasound: a white paper of the ACR Ovarian-Adnexal Reporting and Data System Committee[J]. J Am Coll Radiol, 2018, 15(10): 1415-1429.
[13] Andreotti RF, Timmerman D, Strachowski LM, et al. O-RADS US risk stratification and management system: a consensus guideline from the ACR Ovarian-Adnexal Reporting and Data System Committee[J]. Radiology, 2020, 294(1): 168-185.
[14] Froyman W, Landolfo C, De Cock B, et al. Risk of complications in patients with conservatively managed ovarian tumours(IOTA5): a 2-year interim analysis of a multicentre, prospective, cohort study[J]. Lancet Oncol, 2019, 20(3): 448-458.
[15] Timmerman D, Testa AC, Bourne T, et al. Simple ultrasound based rules for the diagnosis of ovarian cancer[J]. Ultrasound Obster Gynecol, 2008, 31(6): 681-690.
[16] 白博, 韩慧娟, 周毓青. IOTA简易原则预测卵巢肿瘤良恶性的临床价值研究[J]. 中华医学超声杂志(电子版), 2018, 15(8): 620-622. BAI Bo, HAN Huijuan, ZHOU Yuqing. Clinical value of IOTA simple principle in predicting benign and malignant ovarian tumors[J]. Chinese Journal of Medical Ultrasound(Electronic Edition), 2018, 15(8): 620-622.
[17] Shetty J, Saradha A, Pandey D, et al. IOTA simple ultrasound rules for triage of adnexal mass: experience from South India[J]. J Obstet Gynaecol India, 2019, 69(4): 352-362.
[18] 和平,吴青青,孙丽娟,等.对比观察ADNEX模型、简单规则风险估计模型及恶性风险指数诊断卵巢良恶性肿瘤[J].中国医学影像技术, 2019, 35(1): 104-107. HE Ping, WU Qingqing, SUN Lijuan, et al. Comparison of ADNEX model, simple rules risk model and risk of malignancy index in diagnosis of benign and malignant ovarian tumors[J]. Chinese Journal of Medical Imaging Technology, 2019, 35(1): 104-107.
[19] 刘婧,陈秋月,吕国荣.超声国际卵巢肿瘤研究组简单法则与妇科影像报告与数据系统分类诊断卵巢肿瘤的比较[J].中国医学影像技术, 2017, 33(5): 739-742. LIU Jing, CHEN Qiuyue, LYU Guorong. Comparison of ultrasound IOTA simple rules and GI-RADS ultrasonographic stratification in diagnosis of ovarian neoplasms[J]. Chinese Journal of Medical Imaging Technology, 2017, 33(5): 739-742.
[20] Amor F, Vaccaro H, Alcázar JL, et al. Gynecologic imaging reporting and data system: a new proposal for classifying adnexal masses on the basis of sonographic findings[J]. J Ultrasound Med, 2009, 28(3): 285-291.
[21] 陈秋月,吕国荣. GI-RADS分类在妇科附件肿块诊断中的应用[J]. 中国超声医学杂志, 2013, 29(6): 527-530. CHEN Qiuyue, LYU Guorong. The appicational value of GI-RADS ultrasonographic stratification in diagnosing adnexal masses[J]. Chinese Journal of Ultrasound in Medicine, 2013, 29(6): 527-530.
[22] 黄冰冰,陈秋月,吕国荣.比较超声妇科影像报告和数据系统分类与恶性风险指数4鉴别卵巢良恶性肿块的价值[J].中国医学影像技术, 2019, 35(4): 569-572. HUANG Bingbing, CHEN Qiuyue, LYU Guorong. Comparison of value on gynecologic imaging reporting and data system and risk of malignancy index 4 in differentiating benign and malignant ovarian neoplasms[J]. Chinese Journal of Medical Imaging Technology, 2019, 35(4): 569-572.
[23] 王霞丽,杨舒萍,吕国荣,等.妇科超声影像报告和数据系统联合三维超声造影鉴别诊断卵巢良恶性肿块[J].中国医学影像技术, 2018, 34(6): 888-892. WANG Xiali, YANG Shuping, LYU Guorong, et al. Gynecologic imaging reporting and data system combined with three-dimensional contrast-enhanced ultrasonography for differential diagnosis of benign and malignant ovarian masses[J]. Chinese Journal of Medical Imaging Technology, 2018, 34(6): 888-892.
[24] Migda M, Bartosz M, Migda MS, et al. Diagnostic value of the gynecology imaging reporting and data system(GI-RADS)with the ovarian malignancy marker CA-125 in preoperative adnexal tumor assessment[J]. J Ovarian Res, 2018, 11(1): 92. doi: 10.1186/s13048-018-0465-1.
[25] 谢艳秋,任敏,王大维,等.超声改良妇科影像学报告和数据系统(mGI-RADS)分级诊断卵巢肿瘤的临床应用价值[J].中华超声影像学杂志, 2020, 29(5): 421-426. XIE Yanqiu, REN Min, WANG Dawei, et al. Clinical application of ultrasound modified gynecology imaging reporting and data system mGI-RADS in the diagnosis of ovarian tumors[J]. Chinese Journal of Ultrasonography, 2020, 29(5): 421-426.

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