• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

利用自我评估的风险因素识别黑色素瘤高危人群。

Identifying Persons at Highest Risk of Melanoma Using Self-Assessed Risk Factors.

作者信息

Williams Lisa H, Shors Andrew R, Barlow William E, Solomon Cam, White Emily

机构信息

Department of Dermatology, Group Health Cooperative, Seattle, Washington, USA.

出版信息

J Clin Exp Dermatol Res. 2011;2(6). doi: 10.4172/2155-9554.1000129.

DOI:10.4172/2155-9554.1000129
PMID:22229112
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3252382/
Abstract

OBJECTIVE

To develop a self-assessed melanoma risk score to identify high-risk persons for screening METHODS: We used data from a 1997 melanoma case-control study from Washington State, USA, where 386 cases with invasive cutaneous melanoma and 727 controls were interviewed by telephone. A logistic regression prediction model was developed on 75% of the data and validated in the remaining 25% by calculating the area under the receiver operating characteristic curve (AUC), a measure of predictive accuracy from 0.5-1 (higher scores indicating better prediction). A risk score was calculated for each individual, and sensitivities for various risk cutoffs were calculated. RESULTS: The final model included sex, age, hair color, density of freckles, number of severe sunburns in childhood and adolescence, number of raised moles on the arms, and history of non-melanoma skin cancer. The area under the receiver operating characteristic curve(AUC) was 0.70 (95% CI: 0.64, 0.77). The top 15% risk group included 50% of melanomas (sensitivity 50%). CONCLUSIONS: This self-assessed score could be used as part of a comprehensive melanoma screening and public education program to identify high-risk individuals in the general population. This study suggests it may be possible to capture a large proportion of melanomas by screening a small high-risk group. Further study is needed to determine the costs, feasibility, and risks of this approach.

摘要

目的

制定一个自我评估的黑色素瘤风险评分,以识别需要筛查的高危人群。方法:我们使用了来自美国华盛顿州1997年黑色素瘤病例对照研究的数据,通过电话对386例侵袭性皮肤黑色素瘤患者和727名对照进行了访谈。在75%的数据上建立逻辑回归预测模型,并通过计算受试者工作特征曲线下面积(AUC)在其余25%的数据中进行验证,AUC是一种预测准确性的度量,范围为0.5至1(分数越高表明预测越好)。为每个个体计算风险评分,并计算各种风险临界值的敏感性。结果:最终模型包括性别、年龄、头发颜色、雀斑密度、儿童和青少年时期严重晒伤的次数、手臂上凸起痣的数量以及非黑色素瘤皮肤癌病史。受试者工作特征曲线下面积(AUC)为0.70(95%CI:0.64,0.77)。最高15%风险组包含50%的黑色素瘤患者(敏感性为50%)。结论:这种自我评估评分可作为综合黑色素瘤筛查和公众教育项目的一部分,用于识别普通人群中的高危个体。本研究表明,通过筛查一小部分高危人群,有可能发现很大比例的黑色素瘤患者。需要进一步研究以确定这种方法的成本、可行性和风险。

相似文献

1
Identifying Persons at Highest Risk of Melanoma Using Self-Assessed Risk Factors.利用自我评估的风险因素识别黑色素瘤高危人群。
J Clin Exp Dermatol Res. 2011;2(6). doi: 10.4172/2155-9554.1000129.
2
Development and External Validation of a Melanoma Risk Prediction Model Based on Self-assessed Risk Factors.基于自我评估风险因素的黑色素瘤风险预测模型的建立和外部验证。
JAMA Dermatol. 2016 Aug 1;152(8):889-96. doi: 10.1001/jamadermatol.2016.0939.
3
Identifying individuals at high risk of melanoma: a simple tool.识别黑色素瘤高危个体:一种简单的工具。
Eur J Cancer Prev. 2010 Sep;19(5):393-400. doi: 10.1097/CEJ.0b013e32833b492f.
4
Association of Phenotypic Characteristics and UV Radiation Exposure With Risk of Melanoma on Different Body Sites.表型特征和紫外线辐射暴露与不同身体部位黑素瘤风险的关联。
JAMA Dermatol. 2019 Jan 1;155(1):39-49. doi: 10.1001/jamadermatol.2018.3964.
5
Dermoscopy, with and without visual inspection, for diagnosing melanoma in adults.使用或不使用肉眼检查的皮肤镜检查在成人黑色素瘤诊断中的应用
Cochrane Database Syst Rev. 2018 Dec 4;12(12):CD011902. doi: 10.1002/14651858.CD011902.pub2.
6
Development and external validation study of a melanoma risk prediction model incorporating clinically assessed naevi and solar lentigines.纳入临床评估的痣和日光性黑子的黑色素瘤风险预测模型的建立和外部验证研究。
Br J Dermatol. 2020 May;182(5):1262-1268. doi: 10.1111/bjd.18411. Epub 2019 Sep 22.
7
Pigmentary characteristics and moles in relation to melanoma risk.色素沉着特征及痣与黑色素瘤风险的关系
Int J Cancer. 2005 Aug 10;116(1):144-9. doi: 10.1002/ijc.21001.
8
Ultrasound, CT, MRI, or PET-CT for staging and re-staging of adults with cutaneous melanoma.超声、CT、MRI或PET-CT用于成人皮肤黑色素瘤的分期及再分期。
Cochrane Database Syst Rev. 2019 Jul 1;7(7):CD012806. doi: 10.1002/14651858.CD012806.pub2.
9
[Establishment and Validation of a Predictive Model for Gallstone Disease in the General Population: A Multicenter Study].[普通人群胆结石疾病预测模型的建立与验证:一项多中心研究]
Sichuan Da Xue Xue Bao Yi Xue Ban. 2024 May 20;55(3):641-652. doi: 10.12182/20240560501.
10
Venous Thrombosis Risk after Cast Immobilization of the Lower Extremity: Derivation and Validation of a Clinical Prediction Score, L-TRiP(cast), in Three Population-Based Case-Control Studies.下肢石膏固定后的静脉血栓形成风险:三项基于人群的病例对照研究中临床预测评分L-TRiP(石膏固定)的推导与验证
PLoS Med. 2015 Nov 10;12(11):e1001899; discussion e1001899. doi: 10.1371/journal.pmed.1001899. eCollection 2015 Nov.

引用本文的文献

1
Melanoma Skin Cancer: A Comprehensive Review of Current Knowledge.黑色素瘤皮肤癌:当前知识的全面综述
Cancers (Basel). 2025 Sep 5;17(17):2920. doi: 10.3390/cancers17172920.
2
Leveraging AI and patient metadata to develop a novel risk score for skin cancer detection.利用人工智能和患者元数据开发一种用于皮肤癌检测的新型风险评分。
Sci Rep. 2024 Sep 6;14(1):20842. doi: 10.1038/s41598-024-71244-2.
3
Protocol for a cluster-randomized trial of a school-based skin cancer preventive intervention for adolescents.一项针对青少年的基于学校的皮肤癌预防干预措施的整群随机试验方案。

本文引用的文献

1
Predicting melanoma risk for the Australian population.预测澳大利亚人群的黑色素瘤风险。
Australas J Dermatol. 2011 May;52(2):109-16. doi: 10.1111/j.1440-0960.2010.00727.x. Epub 2011 Mar 1.
2
Development of a targeted risk-group model for skin cancer screening based on more than 100,000 total skin examinations.基于超过 10 万次全面皮肤检查,开发一种针对皮肤癌筛查的目标风险群体模型。
J Eur Acad Dermatol Venereol. 2012 Jan;26(1):86-94. doi: 10.1111/j.1468-3083.2011.04014.x. Epub 2011 Mar 4.
3
Identifying individuals at high risk of melanoma: a simple tool.
Contemp Clin Trials. 2024 May;140:107494. doi: 10.1016/j.cct.2024.107494. Epub 2024 Mar 6.
4
Patients' Experiences of Using Skin Self-monitoring Apps With People at Higher Risk of Melanoma: Qualitative Study.黑色素瘤高危人群使用皮肤自我监测应用程序的患者体验:定性研究
JMIR Dermatol. 2021 Aug 13;4(2):e22583. doi: 10.2196/22583.
5
The Family Lifestyles, Actions and Risk Education (FLARE) study: Protocol for a randomized controlled trial of a sun protection intervention for children of melanoma survivors.家庭生活方式、行为和风险教育(FLARE)研究:一项针对黑色素瘤幸存者子女的防晒干预措施的随机对照试验方案。
Contemp Clin Trials. 2023 Aug;131:107276. doi: 10.1016/j.cct.2023.107276. Epub 2023 Jun 29.
6
Autoimmune alleles at the major histocompatibility locus modify melanoma susceptibility.主要组织相容性基因座上的自身免疫等位基因修饰黑色素瘤易感性。
Am J Hum Genet. 2023 Jul 6;110(7):1138-1161. doi: 10.1016/j.ajhg.2023.05.013. Epub 2023 Jun 19.
7
Inter-Rater Agreement in Assessing Risk of Bias in Melanoma Prediction Studies Using the Prediction Model Risk of Bias Assessment Tool (PROBAST): Results from a Controlled Experiment on the Effect of Specific Rater Training.使用预测模型偏倚风险评估工具(PROBAST)评估黑色素瘤预测研究中偏倚风险的评分者间一致性:关于特定评分者培训效果的对照实验结果
J Clin Med. 2023 Mar 2;12(5):1976. doi: 10.3390/jcm12051976.
8
Using the Prediction Model Risk of Bias Assessment Tool (PROBAST) to Evaluate Melanoma Prediction Studies.使用预测模型偏倚风险评估工具(PROBAST)评估黑色素瘤预测研究。
Cancers (Basel). 2022 Jun 20;14(12):3033. doi: 10.3390/cancers14123033.
9
Reporting Quality of Studies Developing and Validating Melanoma Prediction Models: An Assessment Based on the TRIPOD Statement.开发和验证黑色素瘤预测模型的研究报告质量:基于TRIPOD声明的评估
Healthcare (Basel). 2022 Jan 26;10(2):238. doi: 10.3390/healthcare10020238.
10
The Value of Total Body Photography for the Early Detection of Melanoma: A Systematic Review.全身摄影在早期发现黑色素瘤中的价值:系统评价。
Int J Environ Res Public Health. 2021 Feb 10;18(4):1726. doi: 10.3390/ijerph18041726.
识别黑色素瘤高危个体:一种简单的工具。
Eur J Cancer Prev. 2010 Sep;19(5):393-400. doi: 10.1097/CEJ.0b013e32833b492f.
4
Creation and test of a questionnaire for self-assessment of melanoma risk factors.创建和测试用于自我评估黑色素瘤危险因素的问卷。
Eur J Cancer Prev. 2010 Jan;19(1):48-54. doi: 10.1097/CEJ.0b013e328333d113.
5
Final version of 2009 AJCC melanoma staging and classification.2009 年 AJCC 黑色素瘤分期与分类的最终版。
J Clin Oncol. 2009 Dec 20;27(36):6199-206. doi: 10.1200/JCO.2009.23.4799. Epub 2009 Nov 16.
6
Utility of adjuvant systemic therapy in melanoma.辅助性全身治疗在黑色素瘤中的效用。
Ann Oncol. 2009 Aug;20 Suppl 6(Suppl 6):vi30-4. doi: 10.1093/annonc/mdp250.
7
Melanoma.黑色素瘤
J Natl Compr Canc Netw. 2009 Mar;7(3):250-75. doi: 10.6004/jnccn.2009.0020.
8
Screening for skin cancer: an update of the evidence for the U.S. Preventive Services Task Force.皮肤癌筛查:美国预防服务工作组证据更新
Ann Intern Med. 2009 Feb 3;150(3):194-8. doi: 10.7326/0003-4819-150-3-200902030-00009.
9
Factors related to the presentation of thin and thick nodular melanoma from a population-based cancer registry in Queensland Australia.来自澳大利亚昆士兰一个基于人群的癌症登记处的薄型和厚型结节性黑色素瘤呈现相关因素。
Cancer. 2009 Mar 15;115(6):1318-27. doi: 10.1002/cncr.24162.
10
Increasing burden of melanoma in the United States.美国黑色素瘤负担日益加重。
J Invest Dermatol. 2009 Jul;129(7):1666-74. doi: 10.1038/jid.2008.423. Epub 2009 Jan 8.