J Cancer 2021; 12(1):292-304. doi:10.7150/jca.51302

Research Paper

Risk-predicted dual nomograms consisting of clinical and ultrasound factors for downgrading BI-RADS category 4a breast lesions - A multiple centre study

Zihan Niu1, Jia-Wei Tian2, Hai-Tao Ran3, Wei-Dong Ren4, Cai Chang5, Jian-Jun Yuan6, Chun-Song Kang7, You-Bin Deng8, Hui Wang9, Bao-Ming Luo10, Sheng-Lan Guo11, Qi Zhou12, En-Sheng Xue13, Wei-Wei Zhan14, Qing Zhou15, Jie Li16, Ping Zhou17, Chun-Quan Zhang18, Man Chen19, Ying Gu20, Jin-Feng Xu21, Wu Chen22, Yu-Hong Zhang23, Hong-Qiao Wang24, Jian-Chu Li1, Hong-Yan Wang1✉, Yu-Xin Jiang1✉

1. Department of Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China.
2. Department of Ultrasound, the Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China.
3. Department of Ultrasound, the Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China; Chongqing Key Laboratory of Ultrasound Molecular Imaging, Chongqing 400010, China.
4. Department of Ultrasound, Shengjing Hospital of China Medical University, Shenyang 110004, China.
5. Department of Medical Ultrasound, Fudan University Shanghai Cancer Center & Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.
6. Department of Ultrasonography, Henan Provincial People′s Hospital, Zhengzhou 450003, China.
7. Department of Ultrasound, Shanxi Academy of Medical Science, Dayi Hospital of Shanxi Medical University, Taiyuan 030032, China.
8. Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan 430030, China.
9. Department of Ultrasound, China-Japan Union Hospital of Jilin University, Changchun 130033, China.
10. Department of Ultrasound, the Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China.
11. Department of Ultrasonography, First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China.
12. Department of Medical Ultrasound, the Second Affiliated Hospital, School of Medicine, Xi'an Jiaotong University, Xi'an 710004, China.
13. Department of Ultrasound, Union Hospital of Fujian Medical University, Fujian Institute of Ultrasound Medicine, Fuzhou 350001, China.
14. Department of Ultrasound, Ruijin Hospital, Shanghai Jiaotong University, School of Medicine, Shanghai 200025, China.
15. Department of Ultrasonography, Renmin Hospital of Wuhan University, Wuhan 430060, China.
16. Department of Ultrasound, Qilu Hospital, Shandong University, Jinan 250012, China.
17. Department of Ultrasound, the Third Xiangya Hospital of Central South University, Changsha 410013, China.
18. Department of Ultrasound, the Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China.
19. Department of Ultrasound Medicine, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200336, China.
20. Department of Ultrasonography, the Affiliated Hospital of Guizhou Medical University, Guiyang 550004, China.
21. Department of Ultrasound, Shenzhen People's Hospital, the Second Clinical Medical College of Jinan University, Shenzhen 518020, China.
22. Department of Ultrasound, the First Hospital of Shanxi Medical University, Taiyuan 030001, China.
23. Department of Ultrasound, the Second Hospital of Dalian Medical University, Dalian 116027, China.
24. Department of Ultrasound, the Affiliated Hospital of Qingdao University, Qingdao 266003, China.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions.
Niu Z, Tian JW, Ran HT, Ren WD, Chang C, Yuan JJ, Kang CS, Deng YB, Wang H, Luo BM, Guo SL, Zhou Q, Xue ES, Zhan WW, Zhou Q, Li J, Zhou P, Zhang CQ, Chen M, Gu Y, Xu JF, Chen W, Zhang YH, Wang HQ, Li JC, Wang HY, Jiang YX. Risk-predicted dual nomograms consisting of clinical and ultrasound factors for downgrading BI-RADS category 4a breast lesions - A multiple centre study. J Cancer 2021; 12(1):292-304. doi:10.7150/jca.51302. Available from https://www.jcancer.org/v12p0292.htm

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Purpose: To develop and to validate a risk-predicted nomogram for downgrading Breast Imaging Reporting and Data System (BI-RADS) category 4a breast lesions.

Patients and Methods: We enrolled 680 patients with breast lesions that were diagnosed as BI-RADS category 4a by conventional ultrasound from December 2018 to June 2019. All 4a lesions were randomly divided into development and validation groups at the ratio of 3:1. In the development group consisting of 499 cases, the multiple clinical and ultrasound predicted factors were extracted, and dual-predicted nomograms were constructed by multivariable logistic regression analysis, named clinical nomogram and ultrasound nomogram, respectively. Patients were twice classified as either “high risk” or “low risk” in the two nomograms. The performance of these dual nomograms was assessed by an independent validation group of 181 cases. Receiver Operating Characteristic (ROC) curve and diagnostic value were calculated to evaluate the applicability of the new model.

Results: After multiple logistic regression analysis, the clinical nomogram included 2 predictors: age and the first-degree family members with breast cancer. The area under the curve (AUC) value for the clinical nomogram was 0.661 and 0.712 for the development and validation groups, respectively. The ultrasound nomogram included 3 independent predictors (margins, calcification and strain ratio), and the AUC value in this nomogram was 0.782 and 0.747 in the development and validation groups, respectively. In the development group of 499 patients, approximately 50.90% (254/499) of patients were twice classified “low risk”, with a malignancy rate of 1.18%. In the validation group of 181 patients, approximately 47.51% (86/181) of patients had been twice classified as “low risk”, with a malignancy rate of 1.16%.

Conclusions: A dual-predicted nomogram incorporating clinical factors and imaging characteristics is an applicable model for downgrading the low-risk lesions in BI-RADS category 4a and shows good stability and accuracy, which is useful for decreasing the rate of invasive examinations and surgery.

Keywords: Breast cancer, ultrasonography, risk factors, elastography, Nomogram