基于临床及超声声像图特征的乳腺癌风险预测模型A Breast Cancer Risk Prediction Model Based on the Clinical Characteristics and Sonographic Features
游珊珊;姜玉新;朱庆莉;张璟;刘赫;孝梦甦;戴晴;孙强;
YOU Shan-shan;JIANG Yu-xin;ZHU Qing-li;ZHANG Jing;LIU He;XIAO Meng-su;DAI Qing;SUN Qiang;Department of Ultrasound,Peking Union Medical College Hospital,Chinese Academy of Medical Sciences & Peking Union Medical College;Department of Breast Surgery,Peking Union Medical College Hospital,Chinese Academy of Medical Sciences & Peking Union Medical College;
摘要(Abstract):
目的通过分析乳腺病灶超声征象及部分临床特征建立乳腺癌的风险预测模型。方法回顾性研究2007年7月至2009年1月于本院进行乳腺病灶切除活检术的连续性病例116例,用多因素Logistic回归得到超声及部分临床征象(包括患者年龄、乳腺癌家族史、病灶硬度、活动度、形状、边界、方向、后方回声及钙化)中的独立危险因素,提出乳腺癌风险预测模型,并用受试者工作特征曲线评价模型效果。结果 116例乳腺病灶中,52例最终诊断为乳腺癌,其中年龄大于50岁(OR=6.61,95%可信区间1.0740.72)、临床触诊质硬肿物(OR=6.56,95%可信区间1.3240.72)、临床触诊质硬肿物(OR=6.56,95%可信区间1.3232.58)、超声声像图形态不规则(OR=19.93,95%可信区间2.4932.58)、超声声像图形态不规则(OR=19.93,95%可信区间2.49159.45)、边界模糊(OR=21.32,95%可信区间1.98159.45)、边界模糊(OR=21.32,95%可信区间1.98230.14)、边缘成角或毛刺状(OR=31.33,95%可信区间2.61230.14)、边缘成角或毛刺状(OR=31.33,95%可信区间2.61376.02)为乳腺癌的独立危险因素(P<0.05)。据此建立乳腺癌风险预测模型,该模型整体预测的准确性达96.7%。结论本研究建立的乳腺癌风险预测模型并提出的患乳腺癌风险独立危险因素,在临床实践中具有较高的客观性和可操作性。
Objective To propose a breast cancer risk prediction model by analyzing the clinical characteristics and sonographic features of breast lesions.Methods A total of 116 consecutive breast lesion samples obtained by biopsy in our hospital from July 2007 to January 2009 were retrospectively examined.Open biopsies were performed on each patient.The pathological results were used as the golden standard of diagnosis.Multivariate logistic regression analysis was used to identify the independent risk factors of breast cancer including age,family history of breast cancer,the hardness,mobility,shape,margin,orientation,posterior acoustic features,and calcification of the masses.The prediction model was developed and a receiver operating characteristic( ROC) curve was used to evaluate the efficacy of the prediction model.Results Of the 116 breast lesions examined,52 breast lesions were diagnosed as breast cancer.The independent risk factors included the patient's age ofmore than 50 years old( OR = 6.61,95% CI 1.07-40.72),hard mass( OR = 6.56,95% CI 1.32-32.58),irregular shape( OR = 19.93,95% CI 2.49-159.45),instinct margins( OR = 21.32,95% CI 1.98-230.14) and angular or speculated margins( OR = 31.33,95% CI 2.61-376.02).The whole accuracy of this prediction model was 96.7%.Conclusions We developed a breast cancer risk prediction model and proposed independent risk factors,which can help predict the risk of breast cancer in clinical practices.
关键词(KeyWords):
乳腺癌;乳腺影像报告和数据系统;阳性预测值;相对危险度;风险预测模型
breast cancer;Breast Imaging Reporting and Data System;positive predictive value;odd ratio;risk prediction model
基金项目(Foundation): 国家自然科学基金(81201112)
作者(Authors):
游珊珊;姜玉新;朱庆莉;张璟;刘赫;孝梦甦;戴晴;孙强;
YOU Shan-shan;JIANG Yu-xin;ZHU Qing-li;ZHANG Jing;LIU He;XIAO Meng-su;DAI Qing;SUN Qiang;Department of Ultrasound,Peking Union Medical College Hospital,Chinese Academy of Medical Sciences & Peking Union Medical College;Department of Breast Surgery,Peking Union Medical College Hospital,Chinese Academy of Medical Sciences & Peking Union Medical College;
参考文献(References):
- [1]Euhus DM.Understanding mathematical models for breast cancer risk assessment and counseling[J].Breast J,2001,7:224-232.
- [2]Amir E,Evans DG,Shenton A,et al.Evaluation of breast cancer risk assessment packages in the family history evaluation and screening programme[J].J Med Genet,2003,40:807-814.
- [3]Fasching PA,Bani MR,Nestle-Kramling C,et al.Evaluation of mathematical models for breast cancer risk assessment in routineclinical use[J].Eur J Cancer Prev,2007,16:216-224.
- [4]Gail MH.Personalized estimates of breast cancer risk in clinical practice and public health[J].Stat Med,2011,30:1090-1104.
- [5]徐雅莉,孙强,单广良,等.中国女性乳腺癌发病相关危险因素:病例对照研究.[J]协和医学杂志,2011,2:7-14.
- [6]Armstrong K,Handorf EA,Chen J,et al.Breast cancer risk prediction and mammography biopsy decisions:a model-based study[J].Am J Prev Med,2013,44:15-22.
- [7]Timmers JM,Verbeek AL,Inthout J,et al.Breast cancer risk prediction model:a nomogram based on common mammographic screening findings[J].Eur Radiol,2013,23:2413-2419.
- [8]Weisstock CR,Rajapakshe R,Bitgood C,et al.Assessing the breast cancer risk distribution for women undergoing screening in British Columbia[J].Cancer Prev Res(Phila),2013,6:1084-1092.
- [9]BI-RADS-Ultrasound,First Edition[EB/OL].[2013-09-25].http://www.acr.org/Quality-Safety/Resources/BIRADS/Ultrasound.
- [10]Lazarus E,Mainiero MB,Schepps B,et al.BI-RADS lexicon for US and mammography:interobserver variability and positive predictive value[J].Radiology,2006,239:385-391.
- [11]Abdullah N,Mesurolle B,El-Khoury M,et al.Breast imaging reporting and data system lexicon for US:interobserver agreement for assessment of breast masses[J].Radiology,2009,252:665-672.
- [12]Orel SG,Kay N,Reynolds C,et al.BI-RADS categorization as a predictor of malignancy[J].Radiology,1999,211:845-850.
- [13]Raza S,Chikarmane SA,Neilsen SS,et al.BI-RADS 3,4,and 5 lesions:value of US in management—follow-up and outcome[J].Radiology,2008,248:773-781.
- [14]Aho M,Irshad A,Ackerman SJ,et al.Correlation of sonographic features of invasive ductal mammary carcinoma with age,tumor grade,and hormone-receptor status[J].J Clin Ultrasound,2013,41:10-17.
- 乳腺癌
- 乳腺影像报告和数据系统
- 阳性预测值
- 相对危险度
- 风险预测模型
breast cancer - Breast Imaging Reporting and Data System
- positive predictive value
- odd ratio
- risk prediction model
- 游珊珊
- 姜玉新
- 朱庆莉
- 张璟
- 刘赫
- 孝梦甦
- 戴晴
- 孙强
YOU Shan-shan- JIANG Yu-xin
- ZHU Qing-li
- ZHANG Jing
- LIU He
- XIAO Meng-su
- DAI Qing
- SUN Qiang
- Department of Ultrasound
- Peking Union Medical College Hospital
- Chinese Academy of Medical Sciences & Peking Union Medical College
- Department of Breast Surgery
- Peking Union Medical College Hospital
- Chinese Academy of Medical Sciences & Peking Union Medical College
- 游珊珊
- 姜玉新
- 朱庆莉
- 张璟
- 刘赫
- 孝梦甦
- 戴晴
- 孙强
YOU Shan-shan- JIANG Yu-xin
- ZHU Qing-li
- ZHANG Jing
- LIU He
- XIAO Meng-su
- DAI Qing
- SUN Qiang
- Department of Ultrasound
- Peking Union Medical College Hospital
- Chinese Academy of Medical Sciences & Peking Union Medical College
- Department of Breast Surgery
- Peking Union Medical College Hospital
- Chinese Academy of Medical Sciences & Peking Union Medical College