Obstet Gynecol:FDA明确卵巢癌生物标志物测试HE4

2012-01-06 MedSci原创 MedSci原创

美国食品与药品管理局FDA明确卵巢癌生物标志物测试,FDA的批准扫清了此工具全国范围内使用的道路,这个工具在估计具有骨盆腔肿块女性的卵巢癌风险性方面表现出极大特异性。 FDA已批准卵巢恶性肿瘤风险计算法(ROMA)联合蛋白HE4和CA125血液测试的销售和使用。研究表明,用ROMA计算法检测HE4和CA125水平在测定绝经前和绝经后女性卵巢癌风险性上显示出高度精确性。 血液测试与ROMA计

美国食品与药品管理局FDA明确卵巢癌生物标志物测试,FDA的批准扫清了此工具全国范围内使用的道路,这个工具在估计具有骨盆腔肿块女性的卵巢癌风险性方面表现出极大特异性。

FDA已批准卵巢恶性肿瘤风险计算法(ROMA)联合蛋白HE4和CA125血液测试的销售和使用。研究表明,用ROMA计算法检测HE4和CA125水平在测定绝经前和绝经后女性卵巢癌风险性上显示出高度精确性。

血液测试与ROMA计算法联用是通过由Richard G. Moore医学博士带领的团队的研究而开发的,Richard G. Moore是Rhode Island妇婴医院女性肿瘤学项目的妇科学肿瘤专家、生物标志物与工程技术学中心的主任,也是多中心研究的主要作者,该研究主要调查使用HE4和 CA125来测定卵巢癌风险。

ROMA复合HE4和CA125的使用明显改善我们的鉴定能力,使我们对出现卵巢囊肿或肿块的女性可估算出她患卵巢癌可能性的高或低,Moore博士这样说,她是at 布朗大学沃伦珀特医学院(Warren Alpert Medical School of Brown University)的妇产科学助理教授。

CA125测试已经是监护已诊断卵巢癌的女性的金标准。但是,这个测试在其敏感性与特异性上有局限,就象它检测所有类型卵巢癌的能力一样。已经显示,HE4在最常见的上皮细胞卵巢癌中升高,但在许多良性妇科疾病中不升高。结合医生用独立证实的ROMA计算法的评估,在具有骨盆肿块的女性更加精确地从卵巢癌中层分出良性疾病。这也使医生能鉴定出那些恶性肿瘤高可能性的患者,这些人必须由妇产科学肿瘤专家实行外科手术。

当卵巢癌患者经中心的对这个病有治疗经验的妇产科学肿瘤专家进行外科手术时,她们有更好的结果,这个联合测试将改变医生诊断和治疗卵巢癌的方式。

卵巢癌被称为"沉默杀手",因为用症状难以诊断,且这些症状易与其他的非癌性情况相混淆。四分之三的卵巢癌病例都是在晚期被诊断,此时疾病更难治疗。那些早期诊断的患者(I-II期),90%以上的人将存活五年以上。但是,只有四分之一的病例是在早期诊断出来的。

今天任何测试都具有真实的临床影响,它们能有助于鉴定女性卵巢癌高风险性以便她们能通过正确的医生得到正确的治疗,女性肿瘤学项目主任 Cornelius "Skip" Granai医学博士这样说,它作为一个研究结果由我们一位医生做先锋所开发,这尤其让人兴奋。Moore博士已花费了多年时间为患卵巢癌的女性探寻答案,能有助于在早期检测卵巢癌是一个医学突破。

作为风险分层工具的ROMA清除率是以一个前景良好双盲的多中心临床试验的结果为基础的,该临床试验包括472名已列入外科手术表的具骨盆肿块的女性。血样从这些妇女获得,用以测量HE4与CA125水平。对绝经前与绝经后妇女的两种独立计算法将患者分层为低风险和高风险组。然后,所有病人接受手术移除骨盆肿块,如果患者被诊断为上皮细胞卵巢癌,手术阶段被记录是必需的。所有组织样本被检查以证实由位置病理学家所做的诊断。

此研究出现在今年的妇产科学肿瘤学专家协会的年会上,发表在2011年8月的《产科学与妇科学》(Obstetrics and Gynecology)上,它是美国产科医生与妇科医生协会的期刊。

发现复合HE4 与 CA125计算法在指定患者风险性上高度准确,95%的上皮细胞卵巢癌被正确地归类为高风险性。

通过提高分层卵巢癌患者方法的敏感性与特异性,ROMA测试被希望帮助成千上万的妇女测定她们患卵巢癌的风险,使那些高风险的人能接受妇科学肿瘤专家的指导--可提高治疗结果的援助。这种改善参照模式的增强了的能力,如与CA125测试相当的价格一样,关于癌症诊断与治疗的健康开支应该明显减少。(生物谷bioon.com)

Evaluation of the Diagnostic Accuracy of the Risk of Ovarian Malignancy Algorithm in Women With a Pelvic Mass

Moore, Richard G. MD; Miller, M. Craig BSc; Disilvestro, Paul MD; Landrum, Lisa M. MD; Gajewski, Walter MD; Ball, John J. MD; Skates, Steven J. PhD

Abstract

OBJECTIVE: It is often difficult to distinguish a benign pelvic mass from a malignancy and tools to help referring physician are needed. The purpose of this study was to validate the Risk of Ovarian Malignancy Algorithm in women presenting with a pelvic mass.

METHODS: This was a prospective, multicenter, blinded clinical trial that included women who presented to a gynecologist, a family practitioner, an internist, or a general surgeon with an adnexal mass. Serum HE4 and CA 125 were determined preoperatively. A Risk of Ovarian Malignancy Algorithm score was calculated and classified patients into high-risk and low-risk groups for having a malignancy. The sensitivity, specificity, negative predictive value, and positive predictive value of the Risk of Ovarian Malignancy Algorithm were estimated.

RESULTS: A total of 472 patients were evaluated with 383 women diagnosed with benign disease and 89 women with a malignancy. The incidence of all cancers was 15% and 10% for ovarian cancer. In the postmenopausal group, a sensitivity of 92.3% and a specificity of 76.0% and for the premenopausal group the Risk of Ovarian Malignancy Algorithm had a sensitivity of 100% and specificity of 74.2% for detecting ovarian cancer. When considering all women together, the Risk of Ovarian Malignancy Algorithm had a sensitivity of 93.8%, a specificity of 74.9%, and a negative predictive value of 99.0%.

CONCLUSION: The use of the serum biomarkers HE4 and CA 125 with the Risk of Ovarian Malignancy Algorithm has a high sensitivity for the prediction of ovarian cancer in women with a pelvic mass. These findings support the use of the Risk of Ovarian Malignancy Algorithm as a tool for the triage of women with an adnexal mass to gynecologic oncologists. LEVEL OF EVIDENCE: II

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  1. [GetPortalCommentsPageByObjectIdResponse(id=37362, encodeId=becd3e3628c, content=Heckuva good job. I sure apaticepre it., beContent=null, objectType=article, channel=null, level=null, likeNumber=114, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=f0620, createdName=kai, createdTime=Sun Sep 27 07:14:00 CST 2015, time=2015-09-27, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1746927, encodeId=7b1d1e46927c3, content=<a href='/topic/show?id=2103862699' target=_blank style='color:#2F92EE;'>#HE4#</a>, beContent=null, objectType=article, channel=null, level=null, likeNumber=36, replyNumber=0, topicName=null, topicId=null, topicList=[TopicDto(id=8626, encryptionId=2103862699, topicName=HE4)], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=53a535877307, createdName=cqlidoudou, createdTime=Wed Dec 12 21:31:00 CST 2012, time=2012-12-12, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=2011146, encodeId=b80320111469d, content=<a href='/topic/show?id=840d1e300e5' target=_blank style='color:#2F92EE;'>#TET#</a>, beContent=null, objectType=article, channel=null, level=null, likeNumber=26, replyNumber=0, topicName=null, topicId=null, topicList=[TopicDto(id=17300, encryptionId=840d1e300e5, topicName=TET)], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=cc52251, createdName=yhy100200, createdTime=Fri Nov 16 02:31:00 CST 2012, time=2012-11-16, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=2043799, encodeId=fed52043e9942, content=<a href='/topic/show?id=8513126129d' target=_blank style='color:#2F92EE;'>#NEC#</a>, beContent=null, objectType=article, channel=null, level=null, likeNumber=38, replyNumber=0, topicName=null, topicId=null, topicList=[TopicDto(id=12612, encryptionId=8513126129d, topicName=NEC)], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=1ac22500003, createdName=1249842em09(暂无昵称), createdTime=Fri Jun 01 09:31:00 CST 2012, time=2012-06-01, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1840718, encodeId=86cf1840e18ac, content=<a href='/topic/show?id=18bf61953d3' target=_blank style='color:#2F92EE;'>#标志物#</a>, beContent=null, objectType=article, channel=null, level=null, likeNumber=25, replyNumber=0, topicName=null, topicId=null, topicList=[TopicDto(id=61953, encryptionId=18bf61953d3, topicName=标志物)], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=6ab940, createdName=hukaixun, createdTime=Tue Sep 25 09:31:00 CST 2012, time=2012-09-25, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1560487, encodeId=f48d156048ec2, content=<a href='/topic/show?id=3c746936567' target=_blank style='color:#2F92EE;'>#生物标志#</a>, beContent=null, objectType=article, channel=null, level=null, likeNumber=37, replyNumber=0, topicName=null, topicId=null, topicList=[TopicDto(id=69365, encryptionId=3c746936567, topicName=生物标志)], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=63db15173977, createdName=zhu_jun9842, createdTime=Sun Jan 08 14:31:00 CST 2012, time=2012-01-08, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1566496, encodeId=3d08156649661, content=<a href='/topic/show?id=3c746936567' target=_blank style='color:#2F92EE;'>#生物标志#</a>, beContent=null, objectType=article, channel=null, level=null, likeNumber=21, replyNumber=0, topicName=null, topicId=null, topicList=[TopicDto(id=69365, encryptionId=3c746936567, topicName=生物标志)], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=63db15173977, createdName=zhu_jun9842, createdTime=Sun Jan 08 14:31:00 CST 2012, time=2012-01-08, status=1, ipAttribution=)]
    2015-09-27 kai

    Heckuva good job. I sure apaticepre it.

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    2012-12-12 cqlidoudou
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    2012-11-16 yhy100200
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