卵巢附件报告和数据系统、卵巢恶性肿瘤风险算法和哥本哈根指数在卵巢癌术前预测中的诊断性能:一项前瞻性队列研究。

IF 3.4 2区 医学 Q1 OBSTETRICS & GYNECOLOGY Journal of Gynecologic Oncology Pub Date : 2024-09-24 DOI:10.3802/jgo.2025.36.e30
Thi Quynh Nhu Vo, Doan Tu Tran, Tran Thao Nguyen Nguyen, Van Duc Vo, Minh Tam Le, Vu Quoc Huy Nguyen
{"title":"卵巢附件报告和数据系统、卵巢恶性肿瘤风险算法和哥本哈根指数在卵巢癌术前预测中的诊断性能:一项前瞻性队列研究。","authors":"Thi Quynh Nhu Vo, Doan Tu Tran, Tran Thao Nguyen Nguyen, Van Duc Vo, Minh Tam Le, Vu Quoc Huy Nguyen","doi":"10.3802/jgo.2025.36.e30","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>This study aimed to assess the diagnostic performance of the Risk of Ovarian Malignancy Algorithm (ROMA), Copenhagen Index (CPH-I), and Ovarian Adnexal Reporting and Data System (O-RADS) for the preoperative prediction of ovarian cancer (OC).</p><p><strong>Methods: </strong>A prospective cohort study was conducted on 462 patients diagnosed with ovarian tumors admitted to the Departments of Obstetrics and Gynecology, Hue University of Medicine and Pharmacy Hospital, and Hue Central Hospital from May 2020 to December 2022. ROMA and CPH-I were calculated using cancer antigen 125 (CA125), human epididymal protein 4 (HE4) levels, and patient characteristics (age and menopausal status). O-RADS criteria were applied to describe ovarian tumor characteristics from ultrasound findings. Compared with histopathological results, the predictive values of ROMA, CPH-I, and O-RADS alone or in combination with CA125/HE4 for OC were calculated.</p><p><strong>Results: </strong>Among 462 patients, 381 had benign tumors, 11 had borderline tumors, and 50 had OC. At optimal cut-off points, ROMA's and CPH-I's areas under the curves (AUCs) were 0.880 (95% confidence interval [CI]=0.846-0.909) and 0.890 (95% CI=0.857-0.918), respectively, and ROMA and CPH-I sensitivities/specificities (Se/Sp) were 68.85%/95.01% and 77.05%/91.08%, respectively. O-RADS ≥3 yielded an AUCs of 0.949 (95% CI=0.924-0.968), with Se/Sp of 88.52%/88.98% (p<0.001). Combining O-RADS with CA125 demonstrated the highest predictive value, with AUCs of 0.969 (95% CI=0.949-0.983) and Se/Sp of 98.36%/86.09% (p<0.001).</p><p><strong>Conclusion: </strong>The ROMA, CPH-I, O-RADS, O-RADS + CA125, and O-RADS + HE4 models demonstrated good predictive values for OC; the combination of O-RADS and CA125 yielded the highest values.</p>","PeriodicalId":15868,"journal":{"name":"Journal of Gynecologic Oncology","volume":" ","pages":""},"PeriodicalIF":3.4000,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Diagnostic performances of the Ovarian Adnexal Reporting and Data System, the Risk of Ovarian Malignancy Algorithm, and the Copenhagen Index in the preoperative prediction of ovarian cancer: a prospective cohort study.\",\"authors\":\"Thi Quynh Nhu Vo, Doan Tu Tran, Tran Thao Nguyen Nguyen, Van Duc Vo, Minh Tam Le, Vu Quoc Huy Nguyen\",\"doi\":\"10.3802/jgo.2025.36.e30\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>This study aimed to assess the diagnostic performance of the Risk of Ovarian Malignancy Algorithm (ROMA), Copenhagen Index (CPH-I), and Ovarian Adnexal Reporting and Data System (O-RADS) for the preoperative prediction of ovarian cancer (OC).</p><p><strong>Methods: </strong>A prospective cohort study was conducted on 462 patients diagnosed with ovarian tumors admitted to the Departments of Obstetrics and Gynecology, Hue University of Medicine and Pharmacy Hospital, and Hue Central Hospital from May 2020 to December 2022. ROMA and CPH-I were calculated using cancer antigen 125 (CA125), human epididymal protein 4 (HE4) levels, and patient characteristics (age and menopausal status). O-RADS criteria were applied to describe ovarian tumor characteristics from ultrasound findings. Compared with histopathological results, the predictive values of ROMA, CPH-I, and O-RADS alone or in combination with CA125/HE4 for OC were calculated.</p><p><strong>Results: </strong>Among 462 patients, 381 had benign tumors, 11 had borderline tumors, and 50 had OC. At optimal cut-off points, ROMA's and CPH-I's areas under the curves (AUCs) were 0.880 (95% confidence interval [CI]=0.846-0.909) and 0.890 (95% CI=0.857-0.918), respectively, and ROMA and CPH-I sensitivities/specificities (Se/Sp) were 68.85%/95.01% and 77.05%/91.08%, respectively. O-RADS ≥3 yielded an AUCs of 0.949 (95% CI=0.924-0.968), with Se/Sp of 88.52%/88.98% (p<0.001). Combining O-RADS with CA125 demonstrated the highest predictive value, with AUCs of 0.969 (95% CI=0.949-0.983) and Se/Sp of 98.36%/86.09% (p<0.001).</p><p><strong>Conclusion: </strong>The ROMA, CPH-I, O-RADS, O-RADS + CA125, and O-RADS + HE4 models demonstrated good predictive values for OC; the combination of O-RADS and CA125 yielded the highest values.</p>\",\"PeriodicalId\":15868,\"journal\":{\"name\":\"Journal of Gynecologic Oncology\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2024-09-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Gynecologic Oncology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.3802/jgo.2025.36.e30\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"OBSTETRICS & GYNECOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Gynecologic Oncology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3802/jgo.2025.36.e30","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"OBSTETRICS & GYNECOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

研究目的本研究旨在评估卵巢恶性肿瘤风险算法(ROMA)、哥本哈根指数(CPH-I)和卵巢附件报告和数据系统(O-RADS)对卵巢癌(OC)术前预测的诊断性能:2020年5月至2022年12月,顺化医科大学附属医院妇产科和顺化中心医院对462名确诊为卵巢肿瘤的患者进行了前瞻性队列研究。根据癌抗原125(CA125)、人类附睾蛋白4(HE4)水平和患者特征(年龄和绝经状态)计算出ROMA和CPH-I。根据超声检查结果,采用 O-RADS 标准描述卵巢肿瘤特征。与组织病理学结果相比,计算了ROMA、CPH-I和O-RADS单独或与CA125/HE4联合对OC的预测值:在462名患者中,381人患有良性肿瘤,11人患有边缘性肿瘤,50人患有OC。在最佳截断点,ROMA和CPH-I的曲线下面积(AUC)分别为0.880(95%置信区间[CI]=0.846-0.909)和0.890(95% CI=0.857-0.918),ROMA和CPH-I的敏感性/特异性(Se/Sp)分别为68.85%/95.01%和77.05%/91.08%。O-RADS ≥3的AUCs为0.949(95% CI=0.924-0.968),Se/Sp为88.52%/88.98%(p结论:ROMA、CPH-I、O-RADS、O-RADS + CA125和O-RADS + HE4模型对OC具有良好的预测价值;O-RADS和CA125组合的预测价值最高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Diagnostic performances of the Ovarian Adnexal Reporting and Data System, the Risk of Ovarian Malignancy Algorithm, and the Copenhagen Index in the preoperative prediction of ovarian cancer: a prospective cohort study.

Objective: This study aimed to assess the diagnostic performance of the Risk of Ovarian Malignancy Algorithm (ROMA), Copenhagen Index (CPH-I), and Ovarian Adnexal Reporting and Data System (O-RADS) for the preoperative prediction of ovarian cancer (OC).

Methods: A prospective cohort study was conducted on 462 patients diagnosed with ovarian tumors admitted to the Departments of Obstetrics and Gynecology, Hue University of Medicine and Pharmacy Hospital, and Hue Central Hospital from May 2020 to December 2022. ROMA and CPH-I were calculated using cancer antigen 125 (CA125), human epididymal protein 4 (HE4) levels, and patient characteristics (age and menopausal status). O-RADS criteria were applied to describe ovarian tumor characteristics from ultrasound findings. Compared with histopathological results, the predictive values of ROMA, CPH-I, and O-RADS alone or in combination with CA125/HE4 for OC were calculated.

Results: Among 462 patients, 381 had benign tumors, 11 had borderline tumors, and 50 had OC. At optimal cut-off points, ROMA's and CPH-I's areas under the curves (AUCs) were 0.880 (95% confidence interval [CI]=0.846-0.909) and 0.890 (95% CI=0.857-0.918), respectively, and ROMA and CPH-I sensitivities/specificities (Se/Sp) were 68.85%/95.01% and 77.05%/91.08%, respectively. O-RADS ≥3 yielded an AUCs of 0.949 (95% CI=0.924-0.968), with Se/Sp of 88.52%/88.98% (p<0.001). Combining O-RADS with CA125 demonstrated the highest predictive value, with AUCs of 0.969 (95% CI=0.949-0.983) and Se/Sp of 98.36%/86.09% (p<0.001).

Conclusion: The ROMA, CPH-I, O-RADS, O-RADS + CA125, and O-RADS + HE4 models demonstrated good predictive values for OC; the combination of O-RADS and CA125 yielded the highest values.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Journal of Gynecologic Oncology
Journal of Gynecologic Oncology ONCOLOGY-OBSTETRICS & GYNECOLOGY
CiteScore
6.00
自引率
2.60%
发文量
84
审稿时长
>12 weeks
期刊介绍: The Journal of Gynecologic Oncology (JGO) is an official publication of the Asian Society of Gynecologic Oncology. Abbreviated title is ''J Gynecol Oncol''. It was launched in 1990. The JGO''s aim is to publish the highest quality manuscripts dedicated to the advancement of care of the patients with gynecologic cancer. It is an international peer-reviewed periodical journal that is published bimonthly (January, March, May, July, September, and November). Supplement numbers are at times published. The journal publishes editorials, original and review articles, correspondence, book review, etc.
期刊最新文献
Ovarian squamous cell carcinoma: clinicopathological features, prognosis and immunotherapy outcomes. Is presumed clinical stage I endometrial cancer using PET-CT and MRI accurate in predicting surgical staging? Fertility-sparing treatment outcomes using immune checkpoint inhibitors in endometrial cancer patients with Lynch syndrome. Poor accuracy of endometrial sampling in patients with uterine carcinosarcomas: a nationwide analysis. Early prediction and risk stratification of ovarian cancer based on clinical data using machine learning approaches.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1