Lu-Ying Gao1, Yang Gu1, Jia-Wei Tian2, Hai-Tao Ran3, Wei-Dong Ren4, Cai Chang5, Jian-Jun Yuan6, Chun-Song Kang7, You-Bin Deng8, Bao-Ming Luo9, Qi Zhou10, Wei-Wei Zhan11, Qing Zhou12, Jie Li13, Ping Zhou14, Chun-Quan Zhang15, Man Chen16, Ying Gu17, Jian-Feng Guo18, Wu Chen19, Yu-Hong Zhang20, Jian-Chu Li1, Hong-Yan Wang21, Yu-Xin Jiang22. 1. Department of Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No.1 Shuai Fu Yuan, Dong Cheng District, Beijing 100730, China. 2. Department of Ultrasound, the Second Affiliated Hospital of Harbin Medical University, Harbin, China. 3. Department of Ultrasound, the second Affiliated Hospital of Chongqing Medical University, Chongqing Key laboratory of Ultrasound Molecular Imaging, Chongqing, China. 4. Department of Ultrasound, Shengjing Hospital of China Medical University, Shenyang, China. 5. Department of Medical Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China. 6. Department of Ultrasonography, Henan Provincial People's Hospital, Zhengzhou , China. 7. Department of Ultrasound, Shanxi Academy of Medical Science, Dayi Hospital of Shanxi Medical University, Taiyuan, China. 8. Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China. 9. Department of Ultrasound, the Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China. 10. Department of Medical Ultrasound, the Second Affiliated Hospital, School of Medicine, Xi'an Jiaotong University, Xi'an, China. 11. Department of Ultrasound, Ruijin Hospital, Shanghai Jiaotong University, School of Medicine, Shanghai, China. 12. Department of Ultrasonography, Renmin Hospital of Wuhan University, Wuhan, China. 13. Department of Ultrasound, Qilu Hospital, Shandong University, Jinan, China. 14. Department of Ultrasound, the Third Xiangya Hospital of Central South University, Changsha, China. 15. Department of Ultrasound, the Second Affiliated Hospital of Nanchang University, Nanchang, China. 16. Department of Ultrasound Medicine, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. 17. Department of Ultrasonography, the Affiliated Hospital of Guizhou Medical University, Guiyang, China. 18. Department of Ultrasound, the Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Suzhou, China. 19. Department of Ultrasound, the First Hospital of Shanxi Medical University, Taiyuan, China. 20. Department of Ultrasound, the Second Hospital of Dalian Medical University, Dalian, China. 21. Department of Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No.1 Shuai Fu Yuan, Dong Cheng District, Beijing 100730, China. Electronic address: whychina@126.com. 22. Department of Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No.1 Shuai Fu Yuan, Dong Cheng District, Beijing 100730, China. Electronic address: jiangyuxinxh@163.com.
Abstract
RATIONALE AND OBJECTIVES: The sonographic appearance of benign and malignant breast nodules overlaps to some extent, and we aimed to assess the performance of the Gail model as an adjunctive tool to ultrasound (US) Breast Imaging Reporting and Data System (BI-RADS) for predicting the malignancy of nodules. MATERIALS AND METHODS: From 2018 to 2019, 2607 patients were prospectively enrolled by 35 health care facilities. An individual breast cancer risk was assessed by the Gail model. Based on B-mode US, color Doppler, and elastography, all nodules were evaluated according to the fifth edition of BI-RADS, and these nodules were all confirmed later by pathology. RESULTS: We demonstrated that the Gail model, age, tumor size, tumor shape, growth orientation, margin, contour, acoustic shadowing, microcalcification, presence of duct ectasia, presence of architectural distortion, color Doppler flow, BI-RADS, and elastography score were significantly related to breast cancer (all p < 0.001). The sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and area under the curve (AUC) for combining the Gail model with the BI-RADS category were 95.6%, 91.3%, 85.0%, 97.6%, 92.8%, and 0.98, respectively. Combining the Gail model with the BI-RADS showed better diagnostic efficiency than the BI-RADS and Gail model alone (AUC 0.98 vs 0.80, p < 0.001; AUC 0.98 vs 0.55, p < 0.001) and demonstrated a higher specificity than the BI-RADS (91.3% vs 59.4%, p < 0.001). CONCLUSION: The Gail model could be used to differentiate malignant and benign breast lesions. Combined with the BI-RADS category, the Gail model was adjunctive to US for predicting breast lesions for malignancy. For the diagnosis of malignancy, more attention should be paid to high-risk patients with breast lesions.
RATIONALE AND OBJECTIVES: The sonographic appearance of benign and malignant breast nodules overlaps to some extent, and we aimed to assess the performance of the Gail model as an adjunctive tool to ultrasound (US) Breast Imaging Reporting and Data System (BI-RADS) for predicting the malignancy of nodules. MATERIALS AND METHODS: From 2018 to 2019, 2607 patients were prospectively enrolled by 35 health care facilities. An individual breast cancer risk was assessed by the Gail model. Based on B-mode US, color Doppler, and elastography, all nodules were evaluated according to the fifth edition of BI-RADS, and these nodules were all confirmed later by pathology. RESULTS: We demonstrated that the Gail model, age, tumor size, tumor shape, growth orientation, margin, contour, acoustic shadowing, microcalcification, presence of duct ectasia, presence of architectural distortion, color Doppler flow, BI-RADS, and elastography score were significantly related to breast cancer (all p < 0.001). The sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and area under the curve (AUC) for combining the Gail model with the BI-RADS category were 95.6%, 91.3%, 85.0%, 97.6%, 92.8%, and 0.98, respectively. Combining the Gail model with the BI-RADS showed better diagnostic efficiency than the BI-RADS and Gail model alone (AUC 0.98 vs 0.80, p < 0.001; AUC 0.98 vs 0.55, p < 0.001) and demonstrated a higher specificity than the BI-RADS (91.3% vs 59.4%, p < 0.001). CONCLUSION: The Gail model could be used to differentiate malignant and benign breast lesions. Combined with the BI-RADS category, the Gail model was adjunctive to US for predicting breast lesions for malignancy. For the diagnosis of malignancy, more attention should be paid to high-risk patients with breast lesions.
Authors: Motoyo Yano; Anup S Shetty; Greg A Williams; Samantha Lancia; Nikolaos A Trikalinos; Chet W Hammill; William G Hawkins; Amber Salter; Deyali Chatterjee Journal: Abdom Radiol (NY) Date: 2022-02-15
Authors: Kaitlyn Kennard; Olivia Wang; Stephanie Kjelstrom; Sharon Larson; Lina M Sizer; Catherine Carruthers; William B Carter; Robin Ciocca; Jennifer Sabol; Thomas G Frazier; Ned Z Carp Journal: Ann Surg Oncol Date: 2022-07-20 Impact factor: 4.339
Authors: Ling Huo; Yao Tan; Shu Wang; Cuizhi Geng; Yi Li; XiangJun Ma; Bin Wang; YingJian He; Chen Yao; Tao Ouyang Journal: Cancer Manag Res Date: 2021-04-16 Impact factor: 3.989