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Deng T, Liang J, Yan C, Ni M, Xiang H, Li C, Ou J, Lin Q, Liu L, Tang G, Luo R, An X, Gao Y, Lin X. Development and validation of ultrasound-based radiomics model to predict germline BRCA mutations in patients with breast cancer. Cancer Imaging 2024; 24:31. [PMID: 38424620 PMCID: PMC10905812 DOI: 10.1186/s40644-024-00676-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 02/20/2024] [Indexed: 03/02/2024] Open
Abstract
BACKGROUND Identifying breast cancer (BC) patients with germline breast cancer susceptibility gene (gBRCA) mutation is important. The current criteria for germline testing for BC remain controversial. This study aimed to develop a nomogram incorporating ultrasound radiomic features and clinicopathological factors to predict gBRCA mutations in patients with BC. MATERIALS AND METHODS In this retrospective study, 497 women with BC who underwent gBRCA genetic testing from March 2013 to May 2022 were included, including 348 for training (84 with and 264 without a gBRCA mutation) and 149 for validation(36 patients with and 113 without a gBRCA mutation). Factors associated with gBRCA mutations were identified to establish a clinicopathological model. Radiomics features were extracted from the intratumoral and peritumoral regions (3 mm and 5 mm) of each image. The least absolute shrinkage and selection operator regression algorithm was used to select the features and logistic regression analysis was used to construct three imaging models. Finally, a nomogram that combined clinicopathological and radiomics features was developed. The models were evaluated based on the area under the receiver operating characteristic curve (AUC), calibration, and clinical usefulness. RESULTS Age at diagnosis, family history of BC, personal history of other BRCA-related cancers, and human epidermal growth factor receptor 2 status were independent predictors of the clinicopathological model. The AUC of the imaging radiomics model combining intratumoral and peritumoral 3 mm areas in the validation set was 0.783 (95% confidence interval [CI]: 0.702-0.862), which showed the best performance among three imaging models. The nomogram yielded better performance than the clinicopathological model in validation sets (AUC: 0.824 [0.755-0.894] versus 0.659 [0.563-0.755], p = 0.007). CONCLUSION The nomogram based on ultrasound images and clinicopathological factors performs well in predicting gBRCA mutations in BC patients and may help to improve clinical decisions about genetic testing.
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Affiliation(s)
- Tingting Deng
- Department of Ultrasound, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - Jianwen Liang
- School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, 518000, China
| | - Cuiju Yan
- Department of Ultrasound, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - Mengqian Ni
- Department of Medical Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - Huiling Xiang
- Department of Ultrasound, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - Chunyan Li
- Department of Ultrasound, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - Jinjing Ou
- Department of Ultrasound, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - Qingguang Lin
- Department of Ultrasound, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - Lixian Liu
- Department of Ultrasound, Guangdong Second Provincial General Hospital, Guangzhou, 510060, China
| | - Guoxue Tang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Department of Ultrasound, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510060, China
| | - Rongzhen Luo
- Department of Pathology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - Xin An
- Department of Medical Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - Yi Gao
- School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, 518000, China.
| | - Xi Lin
- Department of Ultrasound, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China.
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Ahmad RM, Ali BR, Al-Jasmi F, Sinnott RO, Al Dhaheri N, Mohamad MS. A review of genetic variant databases and machine learning tools for predicting the pathogenicity of breast cancer. Brief Bioinform 2023; 25:bbad479. [PMID: 38149678 PMCID: PMC10782903 DOI: 10.1093/bib/bbad479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 09/22/2023] [Accepted: 12/04/2023] [Indexed: 12/28/2023] Open
Abstract
Studies continue to uncover contributing risk factors for breast cancer (BC) development including genetic variants. Advances in machine learning and big data generated from genetic sequencing can now be used for predicting BC pathogenicity. However, it is unclear which tool developed for pathogenicity prediction is most suited for predicting the impact and pathogenicity of variant effects. A significant challenge is to determine the most suitable data source for each tool since different tools can yield different prediction results with different data inputs. To this end, this work reviews genetic variant databases and tools used specifically for the prediction of BC pathogenicity. We provide a description of existing genetic variants databases and, where appropriate, the diseases for which they have been established. Through example, we illustrate how they can be used for prediction of BC pathogenicity and discuss their associated advantages and disadvantages. We conclude that the tools that are specialized by training on multiple diverse datasets from different databases for the same disease have enhanced accuracy and specificity and are thereby more helpful to the clinicians in predicting and diagnosing BC as early as possible.
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Affiliation(s)
- Rahaf M Ahmad
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Tawam road, Al Maqam district, Al Ain, Abu Dhabi, United Arab Emirates
| | - Bassam R Ali
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Tawam road, Al Maqam district, Al Ain, Abu Dhabi, United Arab Emirates
| | - Fatma Al-Jasmi
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Tawam road, Al Maqam district, Al Ain, Abu Dhabi, United Arab Emirates
- Division of Metabolic Genetics, Department of Pediatrics, Tawam Hospital, Al Ain, United Arab Emirates
| | - Richard O Sinnott
- School of Computing and Information System, Faculty of Engineering and Information Technology, The University of Melbourne, Melbourne, Victoria, Australia
| | - Noura Al Dhaheri
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Tawam road, Al Maqam district, Al Ain, Abu Dhabi, United Arab Emirates
- Division of Metabolic Genetics, Department of Pediatrics, Tawam Hospital, Al Ain, United Arab Emirates
| | - Mohd Saberi Mohamad
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Tawam road, Al Maqam district, Al Ain, Abu Dhabi, United Arab Emirates
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3
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Møller NB, Boonen DS, Feldner ES, Hao Q, Larsen M, Lænkholm AV, Borg Å, Kvist A, Törngren T, Jensen UB, Boonen SE, Thomassen M, Terkelsen T. Validation of the BOADICEA model for predicting the likelihood of carrying pathogenic variants in eight breast and ovarian cancer susceptibility genes. Sci Rep 2023; 13:8536. [PMID: 37237042 PMCID: PMC10220031 DOI: 10.1038/s41598-023-35755-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 05/23/2023] [Indexed: 05/28/2023] Open
Abstract
BOADICEA is a comprehensive risk prediction model for breast and/or ovarian cancer (BC/OC) and for carrying pathogenic variants (PVs) in cancer susceptibility genes. In addition to BRCA1 and BRCA2, BOADICEA version 6 includes PALB2, CHEK2, ATM, BARD1, RAD51C and RAD51D. To validate its predictions for these genes, we conducted a retrospective study including 2033 individuals counselled at clinical genetics departments in Denmark. All counselees underwent comprehensive genetic testing by next generation sequencing on suspicion of hereditary susceptibility to BC/OC. Likelihoods of PVs were predicted from information about diagnosis, family history and tumour pathology. Calibration was examined using the observed-to-expected ratio (O/E) and discrimination using the area under the receiver operating characteristics curve (AUC). The O/E was 1.11 (95% CI 0.97-1.26) for all genes combined. At sub-categories of predicted likelihood, the model performed well with limited misestimation at the extremes of predicted likelihood. Discrimination was acceptable with an AUC of 0.70 (95% CI 0.66-0.74), although discrimination was better for BRCA1 and BRCA2 than for the other genes in the model. This suggests that BOADICEA remains a valid decision-making aid for determining which individuals to offer comprehensive genetic testing for hereditary susceptibility to BC/OC despite suboptimal calibration for individual genes in this population.
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Affiliation(s)
- Nanna Bæk Møller
- Department of Clinical Genetics, Aarhus University Hospital, Brendstrupgårdsvej 21, 8200, Aarhus N, Denmark
| | - Desirée Sofie Boonen
- Department of Clinical Genetics, Odense University Hospital, J. B. Winsløws Vej 4, 5000, Odense, Denmark
| | - Elisabeth Simone Feldner
- Department of Clinical Genetics, Odense University Hospital, J. B. Winsløws Vej 4, 5000, Odense, Denmark
| | - Qin Hao
- Department of Clinical Genetics, Odense University Hospital, J. B. Winsløws Vej 4, 5000, Odense, Denmark
| | - Martin Larsen
- Department of Clinical Genetics, Odense University Hospital, J. B. Winsløws Vej 4, 5000, Odense, Denmark
| | - Anne-Vibeke Lænkholm
- Department of Surgical Pathology, Zealand University Hospital, Roskilde, Denmark
| | - Åke Borg
- Division of Oncology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Anders Kvist
- Division of Oncology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Therese Törngren
- Division of Oncology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Uffe Birk Jensen
- Department of Clinical Genetics, Aarhus University Hospital, Brendstrupgårdsvej 21, 8200, Aarhus N, Denmark
| | - Susanne Eriksen Boonen
- Department of Clinical Genetics, Odense University Hospital, J. B. Winsløws Vej 4, 5000, Odense, Denmark
| | - Mads Thomassen
- Department of Clinical Genetics, Odense University Hospital, J. B. Winsløws Vej 4, 5000, Odense, Denmark.
| | - Thorkild Terkelsen
- Department of Clinical Genetics, Aarhus University Hospital, Brendstrupgårdsvej 21, 8200, Aarhus N, Denmark.
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4
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Li Y, Chen L, Lv J, Chen X, Zeng B, Chen M, Guo W, Lin Y, Yu L, Hou J, Li J, Zhou P, Zhang W, Li S, Jin X, Cai W, Zhang K, Huang Y, Wang C, Fu F. Clinical application of artificial neural network (ANN) modeling to predict BRCA1/2 germline deleterious variants in Chinese bilateral primary breast cancer patients. BMC Cancer 2022; 22:1125. [PMID: 36324133 PMCID: PMC9628090 DOI: 10.1186/s12885-022-10160-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 09/19/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Bilateral breast cancer (BBC), as well as ovarian cancer, are significantly associated with germline deleterious variants in BRCA1/2, while BRCA1/2 germline deleterious variants carriers can exquisitely benefit from poly (ADP-ribose) polymerase (PARP) inhibitors. However, formal genetic testing could not be carried out for all patients due to extensive use of healthcare resources, which in turn results in high medical costs. To date, existing BRCA1/2 deleterious variants prediction models have been developed in women of European or other descent who are quite genetically different from Asian population. Therefore, there is an urgent clinical need for tools to predict the frequency of BRCA1/2 deleterious variants in Asian BBC patients balancing the increased demand for and cost of cancer genetics services. METHODS The entire coding region of BRCA1/2 was screened for the presence of germline deleterious variants by the next generation sequencing in 123 Chinese BBC patients. Chi-square test, univariate and multivariate logistic regression were used to assess the relationship between BRCA1/2 germline deleterious variants and clinicopathological characteristics. The R software was utilized to develop artificial neural network (ANN) and nomogram modeling for BRCA1/2 germline deleterious variants prediction. RESULTS Among 123 BBC patients, we identified a total of 20 deleterious variants in BRCA1 (8; 6.5%) and BRCA2 (12; 9.8%). c.5485del in BRCA1 is novel frameshift deleterious variant. Deleterious variants carriers were younger at first diagnosis (P = 0.0003), with longer interval between two tumors (P = 0.015), at least one medullary carcinoma (P = 0.001), and more likely to be hormone receptor negative (P = 0.006) and HER2 negative (P = 0.001). Area under the receiver operating characteristic curve was 0.903 in ANN and 0.828 in nomogram modeling individually (P = 0.02). CONCLUSION This study shows the spectrum of the BRCA1/2 germline deleterious variants in Chinese BBC patients and indicates that the ANN can accurately predict BRCA deleterious variants than conventional statistical linear approach, which confirms the BRCA1/2 deleterious variants carriers at the lowest costs without adding any additional examinations.
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Affiliation(s)
- Yan Li
- grid.411176.40000 0004 1758 0478Department of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001 Fuzhou, Fujian Province China ,grid.411176.40000 0004 1758 0478Department of General Surgery, Fujian Medical University Union Hospital, 350001 Fuzhou, Fujian Province China ,grid.256112.30000 0004 1797 9307Breast Cancer Institute, Fujian Medical University, 350001 Fuzhou, Fujian Province China
| | - Lili Chen
- grid.411176.40000 0004 1758 0478Department of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001 Fuzhou, Fujian Province China ,grid.411176.40000 0004 1758 0478Department of General Surgery, Fujian Medical University Union Hospital, 350001 Fuzhou, Fujian Province China ,grid.256112.30000 0004 1797 9307Breast Cancer Institute, Fujian Medical University, 350001 Fuzhou, Fujian Province China
| | - Jinxing Lv
- grid.411176.40000 0004 1758 0478Department of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001 Fuzhou, Fujian Province China ,grid.411176.40000 0004 1758 0478Department of General Surgery, Fujian Medical University Union Hospital, 350001 Fuzhou, Fujian Province China ,grid.256112.30000 0004 1797 9307Breast Cancer Institute, Fujian Medical University, 350001 Fuzhou, Fujian Province China ,grid.54549.390000 0004 0369 4060Sichuan Cancer Center, School of Medicine, Sichuan Cancer Hospital & Institute, University of Electronic Science and Technology of China, 610000 Chengdu, China
| | - Xiaobin Chen
- grid.411176.40000 0004 1758 0478Department of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001 Fuzhou, Fujian Province China ,grid.411176.40000 0004 1758 0478Department of General Surgery, Fujian Medical University Union Hospital, 350001 Fuzhou, Fujian Province China ,grid.256112.30000 0004 1797 9307Breast Cancer Institute, Fujian Medical University, 350001 Fuzhou, Fujian Province China
| | - Bangwei Zeng
- grid.411176.40000 0004 1758 0478Nosocomial Infection Control Branch, Fujian Medical University Union Hospital, Fuzhou, Fujian Province China
| | - Minyan Chen
- grid.411176.40000 0004 1758 0478Department of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001 Fuzhou, Fujian Province China ,grid.411176.40000 0004 1758 0478Department of General Surgery, Fujian Medical University Union Hospital, 350001 Fuzhou, Fujian Province China ,grid.256112.30000 0004 1797 9307Breast Cancer Institute, Fujian Medical University, 350001 Fuzhou, Fujian Province China
| | - Wenhui Guo
- grid.411176.40000 0004 1758 0478Department of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001 Fuzhou, Fujian Province China ,grid.411176.40000 0004 1758 0478Department of General Surgery, Fujian Medical University Union Hospital, 350001 Fuzhou, Fujian Province China ,grid.256112.30000 0004 1797 9307Breast Cancer Institute, Fujian Medical University, 350001 Fuzhou, Fujian Province China
| | - Yuxiang Lin
- grid.411176.40000 0004 1758 0478Department of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001 Fuzhou, Fujian Province China ,grid.411176.40000 0004 1758 0478Department of General Surgery, Fujian Medical University Union Hospital, 350001 Fuzhou, Fujian Province China ,grid.256112.30000 0004 1797 9307Breast Cancer Institute, Fujian Medical University, 350001 Fuzhou, Fujian Province China
| | - Liuwen Yu
- grid.411176.40000 0004 1758 0478Department of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001 Fuzhou, Fujian Province China ,grid.411176.40000 0004 1758 0478Department of General Surgery, Fujian Medical University Union Hospital, 350001 Fuzhou, Fujian Province China ,grid.256112.30000 0004 1797 9307Breast Cancer Institute, Fujian Medical University, 350001 Fuzhou, Fujian Province China
| | - Jialin Hou
- grid.411176.40000 0004 1758 0478Department of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001 Fuzhou, Fujian Province China ,grid.411176.40000 0004 1758 0478Department of General Surgery, Fujian Medical University Union Hospital, 350001 Fuzhou, Fujian Province China ,grid.256112.30000 0004 1797 9307Breast Cancer Institute, Fujian Medical University, 350001 Fuzhou, Fujian Province China
| | - Jing Li
- grid.411176.40000 0004 1758 0478Department of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001 Fuzhou, Fujian Province China ,grid.411176.40000 0004 1758 0478Department of General Surgery, Fujian Medical University Union Hospital, 350001 Fuzhou, Fujian Province China ,grid.256112.30000 0004 1797 9307Breast Cancer Institute, Fujian Medical University, 350001 Fuzhou, Fujian Province China
| | - Peng Zhou
- grid.411176.40000 0004 1758 0478Department of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001 Fuzhou, Fujian Province China ,grid.411176.40000 0004 1758 0478Department of General Surgery, Fujian Medical University Union Hospital, 350001 Fuzhou, Fujian Province China ,grid.256112.30000 0004 1797 9307Breast Cancer Institute, Fujian Medical University, 350001 Fuzhou, Fujian Province China
| | - Wenzhe Zhang
- grid.411176.40000 0004 1758 0478Department of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001 Fuzhou, Fujian Province China ,grid.411176.40000 0004 1758 0478Department of General Surgery, Fujian Medical University Union Hospital, 350001 Fuzhou, Fujian Province China ,grid.256112.30000 0004 1797 9307Breast Cancer Institute, Fujian Medical University, 350001 Fuzhou, Fujian Province China
| | - Shengmei Li
- grid.411176.40000 0004 1758 0478Department of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001 Fuzhou, Fujian Province China ,grid.411176.40000 0004 1758 0478Department of General Surgery, Fujian Medical University Union Hospital, 350001 Fuzhou, Fujian Province China ,grid.256112.30000 0004 1797 9307Breast Cancer Institute, Fujian Medical University, 350001 Fuzhou, Fujian Province China
| | - Xuan Jin
- grid.411176.40000 0004 1758 0478Department of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001 Fuzhou, Fujian Province China ,grid.411176.40000 0004 1758 0478Department of General Surgery, Fujian Medical University Union Hospital, 350001 Fuzhou, Fujian Province China ,grid.256112.30000 0004 1797 9307Breast Cancer Institute, Fujian Medical University, 350001 Fuzhou, Fujian Province China
| | - Weifeng Cai
- grid.411176.40000 0004 1758 0478Department of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001 Fuzhou, Fujian Province China ,grid.411176.40000 0004 1758 0478Department of General Surgery, Fujian Medical University Union Hospital, 350001 Fuzhou, Fujian Province China ,grid.256112.30000 0004 1797 9307Breast Cancer Institute, Fujian Medical University, 350001 Fuzhou, Fujian Province China
| | - Kun Zhang
- grid.411176.40000 0004 1758 0478Department of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001 Fuzhou, Fujian Province China ,grid.411176.40000 0004 1758 0478Department of General Surgery, Fujian Medical University Union Hospital, 350001 Fuzhou, Fujian Province China ,grid.256112.30000 0004 1797 9307Breast Cancer Institute, Fujian Medical University, 350001 Fuzhou, Fujian Province China
| | - Yeyuan Huang
- grid.256112.30000 0004 1797 9307Fujian Medical University, 350001 Fuzhou, Fujian Province China
| | - Chuan Wang
- grid.411176.40000 0004 1758 0478Department of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001 Fuzhou, Fujian Province China ,grid.411176.40000 0004 1758 0478Department of General Surgery, Fujian Medical University Union Hospital, 350001 Fuzhou, Fujian Province China ,grid.256112.30000 0004 1797 9307Breast Cancer Institute, Fujian Medical University, 350001 Fuzhou, Fujian Province China
| | - Fangmeng Fu
- grid.411176.40000 0004 1758 0478Department of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001 Fuzhou, Fujian Province China ,grid.411176.40000 0004 1758 0478Department of General Surgery, Fujian Medical University Union Hospital, 350001 Fuzhou, Fujian Province China ,grid.256112.30000 0004 1797 9307Breast Cancer Institute, Fujian Medical University, 350001 Fuzhou, Fujian Province China
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