A Logistic Regression Model Based on Breast Imaging Report And Data System Lexicon to Predict the Risk of Malignancy_West China Medical Journal

Authors：

 ZHAOHai-na ,  PENGYu-lan , LUOHong-hao , HEYu-shuang , JINYa , YANGPan

Department of Ultrasound, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P. R. China;

Corresponding author：

PENGYu-lan, Email: yulanpeng@163.com

Keywords：

Ultrasound; Breast imaging report and data system; Logistic regression model

DOI：

10.7507/1002-0179.20150645

Video：

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Abstract Full text Figures/Tables Video References Cited by

Objective To establish logistic regression analysis model to evaluate the diagnostic efficacy of breast imaging report and data system (BI-RADS) ultrasound signs in forecasting malignant risk of breast lesions. Method Ultrasound graphic materials of 1 660 breast lesions diagnosed during January to September 2011 were retrospectively studied and standardized by BI-RADS. Pathology results were regarded as gold standard reference. Ultrasound signs with significant efficacy after single-factor logistic regression were evaluated in multi-factor logistic regression model to predict the malignant risk of breast lesions. Results Eighteen ultrasound signs of breast lesions on BI-RADS were included in the final regression model. Among them, Cooper ligaments stretch, echogenic halo, skin thickening, axillary lymph node abnormalities, structural distortions and speculation had high OR values of 30 or more and had higher specificity than 90%. The diagnosis values of regressions model were high, with a sensitivity of 84.5%, specificity 95.5% and accuracy 91.4%. The area under ROC curve was 0.964 and prediction accuracy was 91.0%. Conclusions The logistic regression model based on BI-RADS ultrasound signs of breast lesions has high diagnostic values in detecting breast cancer.

Citation： ZHAOHai-na, PENGYu-lan, LUOHong-hao, HEYu-shuang, JINYa, YANGPan. A Logistic Regression Model Based on Breast Imaging Report And Data System Lexicon to Predict the Risk of Malignancy. West China Medical Journal, 2015, 30(12): 2249-2253. doi: 10.7507/1002-0179.20150645 Copy

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7.	Costantini M, Belli P, Lombardi R, et al. Characterization of solid breast masses:use of the sonographic breast imaging reporting and data system lexicon[J]. J Ultrasound Med, 2006, 25(5):649-659.
8.	李洁. 乳腺影像报告与数据系统乳腺影像图谱[M]. 北京:北京大学医学出版社, 2010:231-286.
9.	陈海燕, 宋树良, 韩捷, 等. 乳腺肿块的超声声像图多元回归分析[J]. 中国误诊学杂志, 2007, 3(7):419-421.
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11.	金占强, 徐晓红, 刘尔球, 等. Logistic回归分析在超声多因素鉴别乳腺良恶性肿瘤中的应用[J]. 临床超声医学杂志, 2011, 13(3):169-172.
12.	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(2):385-391.
13.	Yoon JH, Kim MJ, Moon HJ, et al. Subcategorization of ultrasonographic BI-RADS category 4:positive predictive value and clinical factors affecting it[J]. Ultrasound Med Biol, 2011, 37(5):693-699.
14.	Abdullah N, Mesurolle B, Khoury ME, et al. Breast imaging reporting and data system lexicon for US:inter-observer agreement for assessment of breast masses[J]. Radioligy, 2009, 252(3):665-672.
15.	曾婕, 罗葆明. 乳腺实性肿块超声特征的Logistic回归分析[D]. 广州:中山大学, 2008.

1. 黄哲宙, 陈万青, 吴春晓, 等. 中国女性乳腺癌的发病和死亡现况——全国32个肿瘤登记点2003-2007年资料分析报告[J]. 肿瘤, 2012, 32(6):435-439.
2. 骆洪浩, 彭玉兰. 基于超声图像特征和弹性成像的Logistic模型和神经网络预测乳腺癌的初步研究[D]. 成都:四川大学, 2012.
3. Lee HJ, Kim EK, Kim MJ, et al. Observer variability of Breast Imaging Reporting and Data System (BI-RADS) for breast ultrasound[J]. Eur J Radiol, 2008, 65(2):293-298.
4. American College of Radiology (ACR). Breast Imaging Reporting and Data System (BI-RADS^TM)[M]. 4th ed. Reston:American College of Radiology:2003.
5. Adler DD, Carson PL, Rubin JM, et al. Doppler ultrasound color flow imaging in the study of breast cancer:preliminary findings[J]. Ultrasound Med Biol, 1990, 16(6):553-559.
6. Park YM, Park JS, Jung HS, et al. Imaging features of benign adenomyoepithelioma of the breast[J]. Clin Ultrasound, 2013, 41(4):218-223.
7. Costantini M, Belli P, Lombardi R, et al. Characterization of solid breast masses:use of the sonographic breast imaging reporting and data system lexicon[J]. J Ultrasound Med, 2006, 25(5):649-659.
8. 李洁. 乳腺影像报告与数据系统乳腺影像图谱[M]. 北京:北京大学医学出版社, 2010:231-286.
9. 陈海燕, 宋树良, 韩捷, 等. 乳腺肿块的超声声像图多元回归分析[J]. 中国误诊学杂志, 2007, 3(7):419-421.
10. Kim EK, Ko KH, Oh KK, et al. Clinical application of the BI-RADS final assessment to breast sonography in conjunction with mammography[J]. AJR Am J Roentgenol, 2008, 190(5):1209-1215.
11. 金占强, 徐晓红, 刘尔球, 等. Logistic回归分析在超声多因素鉴别乳腺良恶性肿瘤中的应用[J]. 临床超声医学杂志, 2011, 13(3):169-172.
12. 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(2):385-391.
13. Yoon JH, Kim MJ, Moon HJ, et al. Subcategorization of ultrasonographic BI-RADS category 4:positive predictive value and clinical factors affecting it[J]. Ultrasound Med Biol, 2011, 37(5):693-699.
14. Abdullah N, Mesurolle B, Khoury ME, et al. Breast imaging reporting and data system lexicon for US:inter-observer agreement for assessment of breast masses[J]. Radioligy, 2009, 252(3):665-672.
15. 曾婕, 罗葆明. 乳腺实性肿块超声特征的Logistic回归分析[D]. 广州:中山大学, 2008.

West China Medical Journal

A Logistic Regression Model Based on Breast Imaging Report And Data System Lexicon to Predict the Risk of Malignancy

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