切换至 "中华医学电子期刊资源库"

中华乳腺病杂志(电子版) ›› 2024, Vol. 18 ›› Issue (04) : 217 -223. doi: 10.3877/cma.j.issn.1674-0807.2024.04.005

论著

BI-RADS 4类结节患者的乳腺癌风险预测模型
余晓青1, 高欣1, 罗文培1, 杨露1,()   
  1. 1. 40010 重庆医科大学附属第二医院乳腺甲状腺外科
  • 收稿日期:2024-02-06 出版日期:2024-08-01
  • 通信作者: 杨露
  • 基金资助:
    重庆市自然科学基金面上项目(CSTB2024NSCQ-MSX0331)

Breast cancer risk prediction model for patients with BI-RADS 4 nodules

Xiaoqing Yu1, Xin Gao1, Wenpei Luo1, Lu Yang1,()   

  1. 1. Department of Breast and Thyroid Surgery, Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China
  • Received:2024-02-06 Published:2024-08-01
  • Corresponding author: Lu Yang
引用本文:

余晓青, 高欣, 罗文培, 杨露. BI-RADS 4类结节患者的乳腺癌风险预测模型[J]. 中华乳腺病杂志(电子版), 2024, 18(04): 217-223.

Xiaoqing Yu, Xin Gao, Wenpei Luo, Lu Yang. Breast cancer risk prediction model for patients with BI-RADS 4 nodules[J]. Chinese Journal of Breast Disease(Electronic Edition), 2024, 18(04): 217-223.

目的

利用乳腺成像报告和数据系统(BI-RADS)4类结节的临床和超声数据,开发和验证一种乳腺癌风险预测模型。

方法

回顾性分析2017年1月至2018年12月在重庆医科大学附属第二医院就诊的338例患者的377个BI-RADS 4类乳腺结节临床资料。按照7∶3的比例随机将入组的BI-RADS 4类结节分为训练组和验证组。采用单因素Logistic回归和多因素Logistic逐步回归分析,最终确定一组乳腺癌风险独立预测因素的变量组合,创建列线图预测模型。利用受试者操作特征(ROC)曲线和校准曲线来评价列线图模型的性能。使用Hosmer-Lemeshow检验检测列线图模型的拟合度。采用临床决策曲线(DCA)评估该模型的临床预测效能。

结果

338例患者的377个BI-RADS 4类结节(202个良性和175个恶性),分为训练组263个,验证组114个。年龄(OR =1.06,95%CI:1.03~1.08,P<0.001)、边界(OR=2.22,95%CI:1.19~4.13,P=0.012)、形态(OR=1.96,95%CI:1.01~3.77,P=0.045)、钙化(OR =2.43,95%CI:1.35~4.36,P=0.003)、结节最大直径(OR =1.93,95%CI:1.38~2.69,P<0.001)和内部血流(OR=1.95,95%CI:1.08~3.51,P=0.026)是乳腺癌的独立预测因素。训练组和验证组通过列线图画出的ROC曲线下面积(AUC)分别为0.807(95%CI:0.755~0.858)和0.837(95%CI:0.764~0.910)。列线图预测模型具有良好拟合度(训练组:P=0.656;验证组:P=0.502);校准曲线表明列线图模型与实际观测结果有较好的一致性。DCA显示示当阈值概率大于0.1时,该模型预测净获益值较高。

结论

基于临床和超声特征建立的列线图模型可以准确预测BI-RADS 4类结节乳腺癌风险,从而减少不必要手术活组织检查。

Objective

To develop and validate a breast cancer risk prediction model using clinical and ultrasound imaging data of the patients with the Breast Imaging-Reporting and Data System (BI-RADS) 4 nodules.

Methods

A retrospective analysis was conducted on the clinical data of 377 breast nodules of BI-RADS 4 from 338 patients who were treated in the Second Affiliated Hospital of Chongqing Medical University between January 2017 and December 2018. The nodules were randomly divided into the training group and the validation group at the ratio of 7∶3. Univariate and multivariate logistic step-wise regression analyses were used to identify a combination of variables that were independent predictive factors for breast cancer, and then nomogram prediction was constructed. The performance of the nomogram model was evaluated using the receiver operating characteristic (ROC) and calibration curves. The Hosmer-Lemeshow test was used to assess the goodness-of-fit of the nomogram model, and the clinical decision curve analysis (DCA) was used to evaluate the clinical predictive efficacy of the model.

Results

The study included 377 BI-RADS 4 nodules (202 benign and 175 malignant) from 338 patients. All nodules were divided into two groups: training group (263 nodules) and validation group (114 groups). Age (OR=1.06, 95%CI: 1.03-1.08, P< 0.001), margins (OR=2.22, 95%CI: 1.19-4.13, P=0.012), shape (OR=1.96, 95%CI: 1.01-3.77, P=0.045), calcification (OR=2.43, 95%CI: 1.35-4.36, P=0.003), maximum diameter of the nodule (OR=1.93, 95%CI: 1.38-2.69, P<0.001) and internal blood flow (OR=1.95, 95%CI: 1.08-3.51, P=0.026) were independent predictive factors for breast cancer. The area under the ROC curve for the nomogram was 0.807 (95%CI: 0.755-0.858) in the training group and 0.837 (95%CI: 0.764-0.910) in the validation group. The nomogram prediction model showed a good fit (training group: P=0.656; validation group: P=0.502), and the calibration curve indicated a good consistency between the nomogram and the actual observation. The DCA showed higher net benefit for the model when the threshold probability was greater than 0.1.

Conclusion

The nomogram model based on clinical and ultrasound features can accurately predict the risk of breast cancer in the patients with BI-RADS 4 nodules, thereby reducing unnecessary surgical biopsies.

表1 变量赋值表
表2 训练组和验证组BI-RADS 4类结节的基线特征比较[个(%)]
表3 训练组BI-RADS 4类结节乳腺癌发生风险的单因素Logistic回归分析
表4 训练组BI-RADS 4类结节乳腺癌发生风险的多因素Logistic逐步回归分析
图1 预测BI-RADS 4类结节发生乳腺癌风险的列线图模型
图2 训练组和验证组列线图模型的受试者操作特征曲线 a、b图分别为训练组和验证组的受试者操作特征曲线
图3 训练组和验证组列线图模型的校准曲线 a、b图分别为训练组和验证组的校准曲线图。
图4 训练组和验证组列线图模型的临床决策曲线 a、b图分别为训练组和验证组的临床决策曲线
[1]
Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries [J]. CA Cancer J Clin202474(3):229-263.
[2]
Srivastava S, Koay EJ, Borowsky AD, et al. Cancer overdiagnosis: a biological challenge and clinical dilemma [J]. Nat Rev Cancer, 201919(6):349-358.
[3]
Xin Y, Zhang X, Yang Y, et al. A multicenter, hospital-based and non-inferiority study for diagnostic efficacy of automated whole breast ultrasound for breast cancer in China [J]. Sci Rep202111(1):13902.
[4]
Mendelson, EB, Böhm-Vélez MBerg WA. Breast imaging reporting and data system: ACR BI-RADS-breast imaging[M]. Reston, VA: American College of Radiology, 2003.
[5]
Mendelson EB, Böhm-Vélez M, Berg WA, et al. ACR BI-RADS Ultrasound[M]. Reston, VA: American College of Radiology,2013:149.
[6]
Bruening W, Fontanarosa J, Tipton K, et al. Systematic review: comparative effectiveness of core-needle and open surgical biopsy to diagnose breast lesions [J]. Ann Intern Med2010152(4):238-246.
[7]
Park HL, Hong J. Vacuum-assisted breast biopsy for breast cancer[J]. Gland Surg20143(2):120-127.
[8]
Lei S, Zheng R, Zhang S, et al. Global patterns of breast cancer incidence and mortality: a population-based cancer registry data analysis from 2000 to 2020 [J]. Cancer Commun (Lond)202141(11):1183-1194.
[9]
Luo WQ, Huang QX, Huang XW, et al. Predicting breast cancer in Breast Imaging Reporting and Data System (BI-RADS) ultrasound category 4 or 5 lesions: a nomogram combining radiomics and BI-RADS [J]. Sci Rep20199(1):11921.
[10]
Niu Z, Tian JW, Ran HT, et al. Risk-predicted dual nomograms consisting of clinical and ultrasound factors for downgrading BI-RADS category 4A breast lesions - a multiple centre study [J]. J Cancer202112(1):292-304.
[11]
Yang Y, Hu Y, Shen S, et al. A new nomogram for predicting the malignant diagnosis of Breast Imaging Reporting and Data System (BI-RADS) ultrasonography category 4A lesions in women with dense breast tissue in the diagnostic setting [J]. Quant Imaging Med Surg202111(7):3005-3017.
[12]
Zhou P, Jin C, Lu J, et al. Modified model for diagnosing Breast Imaging Reporting and Data System category 3 to 5 breast lesions: retrospective analysis and nomogram development [J]. J Ultrasound Med202140(1):151-161.
[13]
Nindrea RD, Aryandono T, Lazuardi L. Breast cancer risk from modifiable and non-modifiable risk factors among women in southeast Asia: a meta-analysis [J]. Asian Pac J Cancer Prev, 201718(12):3201-3206.
[14]
Mazouni C, Sneige N, Rouzier R, et al. A nomogram to predict for malignant diagnosis of BI-RADS category 4 breast lesions [J]. J Surg Oncol2010102(3):220-224.
[15]
Liang T, Cong S, Yi Z, et al. Ultrasound-based nomogram for distinguishing malignant tumors from nodular sclerosing adenoses in solid breast lesions [J]. J Ultrasound Med202140(10):2189-2200.
[16]
Tot T, Gere M, Hofmeyer S,et al. The clinical value of detecting microcalcifications on a mammogram [J]. Semin Cancer Biol202172:165-174.
[17]
Elverici E, Barça AN, Aktaş H, et al. Nonpalpable BI-RADS 4 breast lesions: sonographic findings and pathology correlation [J]. Diagn Interv Radiol, 201521(3):189-194.
[18]
Li T, Li Y, Yang Y, et al. Logistic regression analysis of ultrasound findings in predicting the malignant and benign phyllodes tumor of breast [J]. PLoS One, 202217(3):e0265952.
[19]
Rahbar G, Sie AC, Hansen GC, et al. Benign versus malignant solid breast masses: US differentiation [J]. Radiology, 1999213(3):889-894.
[1] 李洋, 蔡金玉, 党晓智, 常婉英, 巨艳, 高毅, 宋宏萍. 基于深度学习的乳腺超声应变弹性图像生成模型的应用研究[J]. 中华医学超声杂志(电子版), 2024, 21(06): 563-570.
[2] 洪玮, 叶细容, 刘枝红, 杨银凤, 吕志红. 超声影像组学联合临床病理特征预测乳腺癌新辅助化疗完全病理缓解的价值[J]. 中华医学超声杂志(电子版), 2024, 21(06): 571-579.
[3] 项文静, 徐燕, 茹彤, 郑明明, 顾燕, 戴晨燕, 朱湘玉, 严陈晨. 神经学超声检查在产前诊断胼胝体异常中的应用价值[J]. 中华医学超声杂志(电子版), 2024, 21(05): 470-476.
[4] 胡可, 鲁蓉. 基于多参数超声特征的中老年女性压力性尿失禁诊断模型研究[J]. 中华医学超声杂志(电子版), 2024, 21(05): 477-483.
[5] 张妍, 原韶玲, 史泽洪, 郭馨阳, 牛菁华. 小肾肿瘤超声漏诊原因分析新思路[J]. 中华医学超声杂志(电子版), 2024, 21(05): 500-504.
[6] 席芬, 张培培, 孝梦甦, 刘真真, 张一休, 张璟, 朱庆莉, 孟华. 乳腺错构瘤的临床与超声影像学特征分析[J]. 中华医学超声杂志(电子版), 2024, 21(05): 505-510.
[7] 侯中光, 詹韵韵, 毕玉, 王佳佳, 吴瑕璧, 彭梅. 三维反转成像技术在BI-RADS 4类乳腺肿块应用中的初步研究[J]. 中华医学超声杂志(电子版), 2024, 21(04): 370-376.
[8] 袁晓峰, 惠品晶, 颜燕红, 张炎, 蔡忻懿. 椎动脉椎间段血流动力学参数评估椎动脉颅内段狭窄性病变的效能及可行性研究[J]. 中华医学超声杂志(电子版), 2024, 21(04): 399-407.
[9] 龚艺燃, 李雯婷, 方雅滨, 杨楷熠, 何聚馨, 陈树强. 超声评估远端指间关节指伸肌腱附着点炎对炎性关节病的临床诊断价值[J]. 中华医学超声杂志(电子版), 2024, 21(04): 408-413.
[10] 伍先权, 张立果, 周璇, 梁建深. 乳腺包裹性乳头状癌的临床病理与手术策略联系[J]. 中华普通外科学文献(电子版), 2024, 18(04): 294-297.
[11] 李雪, 韩萌萌, 冯雪园, 马宁. 人表皮生长因子受体2低表达乳腺癌的研究进展及挑战[J]. 中华普通外科学文献(电子版), 2024, 18(04): 308-312.
[12] 蔡大明, 陆晓峰, 王行舟, 王萌, 刘颂, 夏雪峰, 沈晓菲, 杜峻峰, 管文贤. 三级淋巴结构在胃神经内分泌瘤中的预后价值及预后预测模型构建[J]. 中华普外科手术学杂志(电子版), 2024, 18(04): 401-405.
[13] 杨秀君, 崔梦莹, 刘水, 盛基尧, 张丹. 基于SEER数据库胰头部胰腺神经内分泌癌患者预后列线图构建与验证[J]. 中华肝脏外科手术学电子杂志, 2024, 13(04): 520-525.
[14] 张红君, 郑博文, 廖梅, 任杰. 超声及超声造影在肝移植术后上腹部淋巴结良恶性鉴别诊断中的应用[J]. 中华肝脏外科手术学电子杂志, 2024, 13(04): 562-567.
[15] 刘燚隆, 党荣广, 艾蓉, 张凯. 肝硬化合并静脉曲张出血患者内镜治疗后再出血风险的模型建立与验证[J]. 中华消化病与影像杂志(电子版), 2024, 14(04): 336-342.
阅读次数
全文


摘要