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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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.	李潔. 乳腺影像報告與數據系統乳腺影像圖譜[M]. 北京:北京大學醫學出版社, 2010:231-286.
9.	陳海燕, 宋樹良, 韓捷, 等. 乳腺腫塊的超聲聲像圖多元回歸分析[J]. 中國誤診學雜志, 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.	金占強, 徐曉紅, 劉爾球, 等. Logistic回歸分析在超聲多因素鑒別乳腺良惡性腫瘤中的應用[J]. 臨床超聲醫學雜志, 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, 羅葆明. 乳腺實性腫塊超聲特征的Logistic回歸分析[D]. 廣州:中山大學, 2008.

1. 黃哲宙, 陳萬青, 吳春曉, 等. 中國女性乳腺癌的發病和死亡現況——全國32個腫瘤登記點2003-2007年資料分析報告[J]. 腫瘤, 2012, 32(6):435-439.
2. 駱洪浩, 彭玉蘭. 基于超聲圖像特征和彈性成像的Logistic模型和神經網絡預測乳腺癌的初步研究[D]. 成都:四川大學, 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. 李潔. 乳腺影像報告與數據系統乳腺影像圖譜[M]. 北京:北京大學醫學出版社, 2010:231-286.
9. 陳海燕, 宋樹良, 韓捷, 等. 乳腺腫塊的超聲聲像圖多元回歸分析[J]. 中國誤診學雜志, 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. 金占強, 徐曉紅, 劉爾球, 等. Logistic回歸分析在超聲多因素鑒別乳腺良惡性腫瘤中的應用[J]. 臨床超聲醫學雜志, 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, 羅葆明. 乳腺實性腫塊超聲特征的Logistic回歸分析[D]. 廣州:中山大學, 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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