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Valencia-Moreno JM, Gonzalez-Fraga JA, Gutierrez-Lopez E, Estrada-Senti V, Cantero-Ronquillo HA, Kober V. Breast cancer risk estimation with intelligent algorithms and risk factors for Cuban women. Comput Biol Med 2024; 179:108818. [PMID: 38991318 DOI: 10.1016/j.compbiomed.2024.108818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 06/21/2024] [Accepted: 06/24/2024] [Indexed: 07/13/2024]
Abstract
Breast cancer is the most common malignant neoplasm and the leading cause of cancer mortality among women globally. Current prediction models based on risk factors are inefficient in specific populations, so an appropriate and calibrated breast cancer prediction model for Cuban women is essential. This article proposes a conceptual model for breast cancer risk estimation for Cuban women using machine learning algorithms and risk factors. The model has three main components: knowledge representation, risk estimation modeling, and risk predictor evaluation. Nine of the most common machine learning algorithms were used to generate risk predictors using the proposed model. Two data sources served as case studies: the first comprised data collected from Cuban women, and the second included data from US Hispanic women obtained from the Breast Cancer Surveillance Consortium dataset. The results show that the model effectively estimates breast cancer risk and could be a valuable tool for early detection of breast cancer and identification of patients at risk. According to the first experiment results, the best predictor of breast cancer risk for the Cuban female population corresponds to the Random Forest algorithm with a weighted score of 5.981, a training accuracy of 0.996 and a training AUC of 0.997. In a second experiment, it was demonstrated that the risk predictors generated by the proposed model using data from Cuban women obtained better AUC and accuracy values compared to the predictors generated by using the US Hispanic population, potentially generalizable to other Hispanic populations. Implementing this model could be an economically viable alternative to reduce the mortality rate of this type of cancer in Latin American countries such as Cuba.
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Affiliation(s)
- Jose Manuel Valencia-Moreno
- Universidad Autónoma de Baja California, Ensenada, Baja California, Mexico; Universidad de las Ciencias Informáticas, La Habana, Cuba
| | - Jose Angel Gonzalez-Fraga
- Universidad Autónoma de Baja California, Ensenada, Baja California, Mexico; Centro de Investigación Científica y de Educación Superior de Ensenada, Ensenada, Baja California, Mexico.
| | | | | | | | - Vitaly Kober
- Centro de Investigación Científica y de Educación Superior de Ensenada, Ensenada, Baja California, Mexico; Department of Mathematics, Chelyabinsk State University, Russian Federation
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Shang H, Ding Y, Venkateswaran V, Boulier K, Kathuria-Prakash N, Malidarreh PB, Luber JM, Pasaniuc B. Generalizability of PGS 313 for breast cancer risk in a Los Angeles biobank. HGG ADVANCES 2024; 5:100302. [PMID: 38704641 PMCID: PMC11137525 DOI: 10.1016/j.xhgg.2024.100302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 04/30/2024] [Accepted: 04/30/2024] [Indexed: 05/06/2024] Open
Abstract
Polygenic scores (PGSs) summarize the combined effect of common risk variants and are associated with breast cancer risk in patients without identifiable monogenic risk factors. One of the most well-validated PGSs in breast cancer to date is PGS313, which was developed from a Northern European biobank but has shown attenuated performance in non-European ancestries. We further investigate the generalizability of the PGS313 for American women of European (EA), African (AFR), Asian (EAA), and Latinx (HL) ancestry within one institution with a singular electronic health record (EHR) system, genotyping platform, and quality control process. We found that the PGS313 achieved overlapping areas under the receiver operator characteristic (ROC) curve (AUCs) in females of HL (AUC = 0.68, 95% confidence interval [CI] = 0.65-0.71) and EA ancestry (AUC = 0.70, 95% CI = 0.69-0.71) but lower AUCs for the AFR and EAA populations (AFR: AUC = 0.61, 95% CI = 0.56-0.65; EAA: AUC = 0.64, 95% CI = 0.60-0.680). While PGS313 is associated with hormone-receptor-positive (HR+) disease in EA Americans (odds ratio [OR] = 1.42, 95% CI = 1.16-1.64), this association is lost in African, Latinx, and Asian Americans. In summary, we found that PGS313 was significantly associated with breast cancer but with attenuated accuracy in women of AFR and EAA descent within a singular health system in Los Angeles. Our work further highlights the need for additional validation in diverse cohorts prior to the clinical implementation of PGSs.
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Affiliation(s)
- Helen Shang
- Division of Internal Medicine, Ronald Reagan UCLA Medical Center, Los Angeles, CA, USA; Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX, USA.
| | - Yi Ding
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA
| | | | - Kristin Boulier
- Division of Cardiology, Department of Medicine, Ronald Reagan UCLA Medical Center, Los Angeles, CA, USA
| | - Nikhita Kathuria-Prakash
- Division of Hematology-Oncology, Department of Medicine, Ronald Reagan UCLA Medical Center, Los Angeles, CA, USA
| | - Parisa Boodaghi Malidarreh
- Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX, USA; Multi-Interprofessional Center for Health Informatics, University of Texas at Arlington, Arlington, TX, USA
| | - Jacob M Luber
- Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX, USA; Multi-Interprofessional Center for Health Informatics, University of Texas at Arlington, Arlington, TX, USA; Department of Bioengineering, University of Texas at Arlington, Arlington, TX, USA
| | - Bogdan Pasaniuc
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA; Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Institute of Precision Health, University of California, Los Angeles, Los Angeles, CA, USA
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Filip CI, Cătană A, Kutasi E, Roman SA, Militaru MS, Risteiu GA, Dindelengan GC. Breast Cancer Screening and Prophylactic Mastectomy for High-Risk Women in Romania. MEDICINA (KAUNAS, LITHUANIA) 2024; 60:570. [PMID: 38674216 PMCID: PMC11052261 DOI: 10.3390/medicina60040570] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 03/10/2024] [Accepted: 03/26/2024] [Indexed: 04/28/2024]
Abstract
Breast cancer remains a significant contributor to morbidity and mortality within oncology. Risk factors, encompassing genetic and environmental influences, significantly contribute to its prevalence. While germline mutations, notably within the BRCA genes, are commonly associated with heightened breast cancer risk, a spectrum of other variants exists among affected individuals. Diagnosis relies on imaging techniques, biopsies, biomarkers, and genetic testing, facilitating personalised risk assessment through specific scoring systems. Breast cancer screening programs employing mammography and other imaging modalities play a crucial role in early detection and management, leading to improved outcomes for affected individuals. Regular screening enables the identification of suspicious lesions or abnormalities at earlier stages, facilitating timely intervention and potentially reducing mortality rates associated with breast cancer. Genetic mutations guide screening protocols, prophylactic interventions, treatment modalities, and patient prognosis. Prophylactic measures encompass a range of interventions, including chemoprevention, hormonal inhibition, oophorectomy, and mastectomy. Despite their efficacy in mitigating breast cancer incidence, these interventions carry potential side effects and psychological implications, necessitating comprehensive counselling tailored to individual cases.
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Affiliation(s)
- Claudiu Ioan Filip
- Department of Plastic Surgery and Burn Unit, Emergency District Hospital, 400535 Cluj-Napoca, Romania; (C.I.F.); (G.C.D.)
- First Surgical Clinic, Faculty of Medicine, University of Medicine and Pharmacy “Iuliu Hatieganu”, 400012 Cluj-Napoca, Romania
| | - Andreea Cătană
- Department of Molecular Sciences, Faculty of Medicine, University of Medicine and Pharmacy “Iuliu Hatieganu”, 400012 Cluj-Napoca, Romania; (A.C.); (E.K.); (S.A.R.); (G.A.R.)
- Department of Oncogeneticcs, Institute of Oncology, “Prof. Dr. I. Chiricuță”, 400015 Cluj-Napoca, Romania
- Regional Laboratory Cluj-Napoca, Department of Medical Genetics, Regina Maria Health Network, 400363 Cluj-Napoca, Romania
| | - Eniko Kutasi
- Department of Molecular Sciences, Faculty of Medicine, University of Medicine and Pharmacy “Iuliu Hatieganu”, 400012 Cluj-Napoca, Romania; (A.C.); (E.K.); (S.A.R.); (G.A.R.)
| | - Sara Alexia Roman
- Department of Molecular Sciences, Faculty of Medicine, University of Medicine and Pharmacy “Iuliu Hatieganu”, 400012 Cluj-Napoca, Romania; (A.C.); (E.K.); (S.A.R.); (G.A.R.)
| | - Mariela Sanda Militaru
- Department of Molecular Sciences, Faculty of Medicine, University of Medicine and Pharmacy “Iuliu Hatieganu”, 400012 Cluj-Napoca, Romania; (A.C.); (E.K.); (S.A.R.); (G.A.R.)
- Regional Laboratory Cluj-Napoca, Department of Medical Genetics, Regina Maria Health Network, 400363 Cluj-Napoca, Romania
| | - Giulia Andreea Risteiu
- Department of Molecular Sciences, Faculty of Medicine, University of Medicine and Pharmacy “Iuliu Hatieganu”, 400012 Cluj-Napoca, Romania; (A.C.); (E.K.); (S.A.R.); (G.A.R.)
| | - George Călin Dindelengan
- Department of Plastic Surgery and Burn Unit, Emergency District Hospital, 400535 Cluj-Napoca, Romania; (C.I.F.); (G.C.D.)
- First Surgical Clinic, Faculty of Medicine, University of Medicine and Pharmacy “Iuliu Hatieganu”, 400012 Cluj-Napoca, Romania
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Wu L, Chen GZ, Zeng ZR, Ji CW, Zhang AQ, Xia JH, Liu GC. Analysis of Breast Cancer Screening Results and Influencing Factors of Breast Cancer in Guangdong Province from 2017 to 2021. J Epidemiol Glob Health 2024; 14:131-141. [PMID: 38224387 PMCID: PMC11043295 DOI: 10.1007/s44197-023-00176-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 11/30/2023] [Indexed: 01/16/2024] Open
Abstract
BACKGROUNDS Breast cancer screening plays an important role in the early detection, diagnosis and treatment of breast cancer. The aim of this study was to evaluate the screening results and explore the influencing factors of breast cancer detection rate in Guangdong. METHODS This cross-sectional study was conducted among 2,024,960 women aged 35-64 in Guangdong Province during 2017-2021. The data about breast cancer screening information were collected from the Guangdong maternal and child health information system. Descriptive statistical analysis was used to explain demographic characteristics and results of breast cancer screening. The generalized linear regression model was applied to analyze the related influencing factors of breast cancer detection rate. RESULTS The estimated detection rate of breast cancer in Guangdong Province is 70.32/105, with an early diagnosis rate of 82.06%. After adjusting covariates, those women with older age (45-55 [OR (95% CI) 2.174 (1.872, 2.526)], 55-65 [OR (95% CI) 2.162 (1.760, 2.657)]), education for high school ([OR (95% CI) 1.491 (1.254, 1.773)]) and older age at first birth ([OR (95% CI) 1.632 (1.445, 1.844)]) were more likely to have higher detection rate of breast cancer. No history of surgery or biopsy ([OR (95% CI) 0.527 (0.387, 0.718)]), no history of breast cancer check ([OR (95% CI) 0.873 (0.774, 0.985)]) and no family history of breast cancer ([OR (95% CI) 0.255 (0.151, 0.432)]) women were more likely to screen negative for breast cancer (P < 0.05). CONCLUSION The detection rate of breast cancer in screening showed an increasing trend year by year in Guangdong Province. Older age, education for high school and older age at first birth were risk factors for breast cancer detection rate, while no surgery or biopsy history, no family history of breast cancer and no history of breast cancer check were protective factors.
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Affiliation(s)
- Li Wu
- Guangdong Women and Children Hospital, Xingnan Road 521, Guangzhou, 511442, Guangdong, China
| | - Guo-Zhen Chen
- School of Basic Medicine and Public Health, Jinan University, Guangzhou, 510632, Guangdong, China
| | - Zu-Rui Zeng
- School of Basic Medicine and Public Health, Jinan University, Guangzhou, 510632, Guangdong, China
| | - Cun-Wei Ji
- Guangdong Women and Children Hospital, Xingnan Road 521, Guangzhou, 511442, Guangdong, China
| | - An-Qin Zhang
- Guangdong Women and Children Hospital, Xingnan Road 521, Guangzhou, 511442, Guangdong, China
| | - Jian-Hong Xia
- Guangdong Women and Children Hospital, Xingnan Road 521, Guangzhou, 511442, Guangdong, China.
| | - Guo-Cheng Liu
- Guangdong Women and Children Hospital, Xingnan Road 521, Guangzhou, 511442, Guangdong, China.
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Wilkerson AD, Gentle CK, Ortega C, Al-Hilli Z. Disparities in Breast Cancer Care-How Factors Related to Prevention, Diagnosis, and Treatment Drive Inequity. Healthcare (Basel) 2024; 12:462. [PMID: 38391837 PMCID: PMC10887556 DOI: 10.3390/healthcare12040462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Revised: 02/05/2024] [Accepted: 02/07/2024] [Indexed: 02/24/2024] Open
Abstract
Breast cancer survival has increased significantly over the last few decades due to more effective strategies for prevention and risk modification, advancements in imaging detection, screening, and multimodal treatment algorithms. However, many have observed disparities in benefits derived from such improvements across populations and demographic groups. This review summarizes published works that contextualize modern disparities in breast cancer prevention, diagnosis, and treatment and presents potential strategies for reducing disparities. We conducted searches for studies that directly investigated and/or reported disparities in breast cancer prevention, detection, or treatment. Demographic factors, social determinants of health, and inequitable healthcare delivery may impede the ability of individuals and communities to employ risk-mitigating behaviors and prevention strategies. The disparate access to quality screening and timely diagnosis experienced by various groups poses significant hurdles to optimal care and survival. Finally, barriers to access and inequitable healthcare delivery patterns reinforce inequitable application of standards of care. Cumulatively, these disparities underlie notable differences in the incidence, severity, and survival of breast cancers. Efforts toward mitigation will require collaborative approaches and partnerships between communities, governments, and healthcare organizations, which must be considered equal stakeholders in the fight for equity in breast cancer care and outcomes.
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Affiliation(s)
- Avia D Wilkerson
- Department of General Surgery, Digestive Disease and Surgery Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Corey K Gentle
- Department of General Surgery, Digestive Disease and Surgery Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Camila Ortega
- Department of General Surgery, Digestive Disease and Surgery Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Zahraa Al-Hilli
- Department of General Surgery, Digestive Disease and Surgery Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Breast Center, Integrated Surgical Institute, Cleveland Clinic, Cleveland, OH 44195, USA
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6
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Abstract
Multiple tools exist to assess a patient's breast cancer risk. The choice of risk model depends on the patient's risk factors and how the calculation will impact care. High-risk patients-those with a lifetime breast cancer risk of ≥20%-are, for instance, eligible for supplemental screening with breast magnetic resonance imaging. Those with an elevated short-term breast cancer risk (frequently defined as a 5-year risk ≥1.66%) should be offered endocrine prophylaxis. High-risk patients should also receive guidance on modification of lifestyle factors that affect breast cancer risk.
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Affiliation(s)
- Amy E Cyr
- Department of Medicine, Washington University, Box 8056, 660 South Euclid Avenue, Saint Louis, MO 63110, USA.
| | - Kaitlyn Kennard
- Department of Surgery, Washington University, Box 8051, 660 South Euclid Avenue, Saint louis, MO 63110, USA
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Hernández A, Miranda DA, Pertuz S. An in silico study on the detectability of field cancerization through parenchymal analysis of digital mammograms. Med Phys 2023; 50:6379-6389. [PMID: 36994613 DOI: 10.1002/mp.16401] [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: 10/17/2022] [Revised: 03/07/2023] [Accepted: 03/15/2023] [Indexed: 03/31/2023] Open
Abstract
BACKGROUND Parenchymal analysis has shown promising performance for the assessment of breast cancer risk through the characterization of the texture features of mammography images. However, the working principles behind this practice are yet not well understood. Field cancerization is a phenomenon associated with genetic and epigenetic alterations in large volumes of cells, putting them on a path of malignancy before the appearance of recognizable cancer signs. Evidence suggests that it can induce changes in the biochemical and optical properties of the tissue. PURPOSE The aim of this work was to study whether the extended genetic mutations and epigenetic changes due to field cancerization, and the impact they have on the biochemistry of breast tissues are detectable in the radiological patterns of mammography images. METHODS An in silico experiment was designed, which implied the development of a field cancerization model to modify the optical tissue properties of a cohort of 60 voxelized virtual breast phantoms. Mammography images from these phantoms were generated and compared with images obtained from their non-modified counterparts, that is, without field cancerization. We extracted 33 texture features from the breast area to quantitatively assess the impact of the field cancerization model. We analyzed the similarity and statistical equivalence of texture features with and without field cancerization using the t-test, Wilcoxon sign rank test and Kolmogorov-Smirnov test, and performed a discrimination test using multinomial logistic regression analysis with lasso regularization. RESULTS With modifications of the optical tissue properties on 3.9% of the breast volume, some texture features started to fail to show equivalence (p < 0.05). At 7.9% volume modification, a high percent of texture features showed statistically significant differences (p < 0.05) and non-equivalence. At this level, multinomial logistic regression analysis of texture features showed a statistically significant performance in the discrimination of mammograms from breasts with and without field cancerization (AUC = 0.89, 95% CI: 0.75-1.00). CONCLUSIONS These results support the idea that field cancerization is a feasible underlying working principle behind the distinctive performance of parenchymal analysis in breast cancer risk assessment.
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Affiliation(s)
- Angie Hernández
- Connectivity and Signal Processing group - CPS, Universidad Industrial de Santander, Bucaramanga, Colombia
| | - David A Miranda
- Biological and Semiconductor Materials Science - CIMBIOS, Universidad Industrial de Santander, Bucaramanga, Colombia
| | - Said Pertuz
- Connectivity and Signal Processing group - CPS, Universidad Industrial de Santander, Bucaramanga, Colombia
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Lauritzen AD, von Euler-Chelpin MC, Lynge E, Vejborg I, Nielsen M, Karssemeijer N, Lillholm M. Robust cross-vendor mammographic texture models using augmentation-based domain adaptation for long-term breast cancer risk. J Med Imaging (Bellingham) 2023; 10:054003. [PMID: 37780685 PMCID: PMC10539784 DOI: 10.1117/1.jmi.10.5.054003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 08/08/2023] [Accepted: 09/13/2023] [Indexed: 10/03/2023] Open
Abstract
Purpose Risk-stratified breast cancer screening might improve early detection and efficiency without comprising quality. However, modern mammography-based risk models do not ensure adaptation across vendor-domains and rely on cancer precursors, associated with short-term risk, which might limit long-term risk assessment. We report a cross-vendor mammographic texture model for long-term risk. Approach The texture model was robustly trained using two systematically designed case-control datasets. Textural features, indicative of future breast cancer, were learned by excluding samples with diagnosed/potential malignancies from training. An augmentation-based domain adaption technique, based on flavorization of mammographic views, ensured generalization across vendor-domains. The model was validated in 66,607 consecutively screened Danish women with flavorized Siemens views and 25,706 Dutch women with Hologic-processed views. Performances were evaluated for interval cancers (IC) within 2 years from screening and long-term cancers (LTC) from 2 years after screening. The texture model was combined with established risk factors to flag 10% of women with the highest risk. Results In Danish women, the texture model achieved an area under the receiver operating characteristic curve (AUC) of 0.71 and 0.65 for ICs and LTCs, respectively. In Dutch women with Hologic-processed views, the AUCs were not different from AUCs in Danish women with flavorized views. The AUC for texture combined with established risk factors increased to 0.68 for LTCs. The 10% of women flagged as high-risk accounted for 25.5% of ICs and 24.8% of LTCs. Conclusions The texture model robustly estimated long-term breast cancer risk while adapting to an unseen processed vendor-domain and identified a clinically relevant high-risk subgroup.
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Affiliation(s)
- Andreas D. Lauritzen
- University of Copenhagen, Department of Computer Science, Faculty of Science, Copenhagen, Denmark
| | | | - Elsebeth Lynge
- University of Copenhagen, Department of Public Health, Faculty of Health and Medical Sciences, Copenhagen, Denmark
| | - Ilse Vejborg
- Gentofte Hospital, Department of Breast Examinations, Gentofte, Denmark
| | - Mads Nielsen
- University of Copenhagen, Department of Computer Science, Faculty of Science, Copenhagen, Denmark
| | | | - Martin Lillholm
- University of Copenhagen, Department of Computer Science, Faculty of Science, Copenhagen, Denmark
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Zhu Y, Chen X, Dou H, Liu Y, Li F, Wang Y, Xiao M. Vacuum-assisted biopsy system for breast lesions: a potential therapeutic approach. Front Oncol 2023; 13:1230083. [PMID: 37593094 PMCID: PMC10430071 DOI: 10.3389/fonc.2023.1230083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Accepted: 07/11/2023] [Indexed: 08/19/2023] Open
Abstract
Purpose The primary objective is to optimize the population eligible for Mammotome Minimally Invasive Surgery (MIS) by refining selection criteria. This involves maximizing procedure benefits, minimizing malignancy risk, and reducing the rate of malignant outcomes. Patients and methods A total of 1158 female patients who came to our hospital from November 2016 to August 2021 for the Mammotome MIS were analyzed retrospectively. Following χ2 tests to screen for risk variables, binary logistic regression analysis was used to determine the independent predictors of malignant lesions. In addition, the correlation between age and lesion diameter was investigated for BI-RADS ultrasound (US) category 4a lesions in order to better understand the relationship between these variables. Results The malignancy rates of BI-RADS US category 3, category 4a and category 4b patients who underwent the Mammotome MIS were 0.6% (9/1562), 6.4% (37/578) and 8.3% (2/24) respectively. Malignant lesions were more common in patients over the age of 40, have visible blood supply, and BI-RADS category 4 of mammography. In BI-RADS US category 4a lesions, the diameter of malignant tumor was highly correlated with age, and this correlation was strengthened in patients over the age of 40 and with BI-RADS category 4 of mammography. Conclusion The results of this study demonstrate that the clinical data and imaging results, particularly age, blood supply, and mammography classification, offer valuable insights to optimize patients' surgical options and decrease the incidence of malignant outcomes.
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Affiliation(s)
| | | | | | | | | | | | - Min Xiao
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, China
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10
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Su Y, Huang C, Zhu W, Lyu X, Ji F. Multi-party Diabetes Mellitus risk prediction based on secure federated learning. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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11
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Zou S, Lin Y, Yu X, Eriksson M, Lin M, Fu F, Yang H. Genetic and lifestyle factors for breast cancer risk assessment in Southeast China. Cancer Med 2023; 12:15504-15514. [PMID: 37264741 PMCID: PMC10417168 DOI: 10.1002/cam4.6198] [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: 09/25/2022] [Revised: 04/01/2023] [Accepted: 05/23/2023] [Indexed: 06/03/2023] Open
Abstract
BACKGROUND Despite the rising incidence and mortality of breast cancer among women in China, there are currently few predictive models for breast cancer in the Chinese population and with low accuracy. This study aimed to identify major genetic and life-style risk factors in a Chinese population for potential application in risk assessment models. METHODS A case-control study in southeast China was conducted including 1321 breast cancer patients and 2045 controls during 2013-2016, in which the data were randomly divided into a training set and a test set on a 7:3 scale. The association between genetic and life-style factors and breast cancer was examined using logistic regression models. Using AUC curves, we also compared the performance of the logistic model to machine learning models, namely LASSO regression model and support vector machine (SVM), and the scores calculated from CKB, Gail and Tyrer-Cuzick models in the test set. RESULTS Among all factors considered, the best model was achieved when polygenetic risk score, lifestyle, and reproductive factors were considered jointly in the logistic regression model (AUC = 0.73; 95% CI: 0.70-0.77). The models created in this study performed better than those using scores calculated from the CKB, Gail, and Tyrer-Cuzick models. However, the logistic model and machine learning models did not significantly differ from one another. CONCLUSION In summary, we have found genetic and lifestyle risk predictors for breast cancer with moderate discrimination, which might provide reference for breast cancer screening in southeast China. Further population-based studies are needed to validate the model for future applications in personalized breast cancer screening programs.
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Affiliation(s)
- Shuqing Zou
- Department of Epidemiology and Health Statistics, School of Public HealthFujian Medical UniversityFuzhouChina
| | - Yuxiang Lin
- Department of Breast SurgeryFujian Medical University Union HospitalFuzhouChina
- Department of General SurgeryFujian Medical University Union HospitalFuzhouChina
- Breast Cancer Institute, Fujian Medical UniversityFuzhouChina
| | - Xingxing Yu
- Department of Epidemiology and Health Statistics, School of Public HealthFujian Medical UniversityFuzhouChina
| | - Mikael Eriksson
- Department of Medical Epidemiology and BiostatisticsKarolinska InstitutetStockholmSweden
| | | | - Fangmeng Fu
- Department of Breast SurgeryFujian Medical University Union HospitalFuzhouChina
- Department of General SurgeryFujian Medical University Union HospitalFuzhouChina
- Breast Cancer Institute, Fujian Medical UniversityFuzhouChina
| | - Haomin Yang
- Department of Epidemiology and Health Statistics, School of Public HealthFujian Medical UniversityFuzhouChina
- Department of Medical Epidemiology and BiostatisticsKarolinska InstitutetStockholmSweden
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12
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Ho PJ, Lim EH, Mohamed Ri NKB, Hartman M, Wong FY, Li J. Will Absolute Risk Estimation for Time to Next Screen Work for an Asian Mammography Screening Population? Cancers (Basel) 2023; 15:cancers15092559. [PMID: 37174025 PMCID: PMC10177032 DOI: 10.3390/cancers15092559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 04/26/2023] [Accepted: 04/27/2023] [Indexed: 05/15/2023] Open
Abstract
Personalized breast cancer risk profiling has the potential to promote shared decision-making and improve compliance with routine screening. We assessed the Gail model's performance in predicting the short-term (2- and 5-year) and the long-term (10- and 15-year) absolute risks in 28,234 asymptomatic Asian women. Absolute risks were calculated using different relative risk estimates and Breast cancer incidence and mortality rates (White, Asian-American, or the Singapore Asian population). Using linear models, we tested the association of absolute risk and age at breast cancer occurrence. Model discrimination was moderate (AUC range: 0.580-0.628). Calibration was better for longer-term prediction horizons (E/Olong-term ranges: 0.86-1.71; E/Oshort-term ranges:1.24-3.36). Subgroup analyses show that the model underestimates risk in women with breast cancer family history, positive recall status, and prior breast biopsy, and overestimates risk in underweight women. The Gail model absolute risk does not predict the age of breast cancer occurrence. Breast cancer risk prediction tools performed better with population-specific parameters. Two-year absolute risk estimation is attractive for breast cancer screening programs, but the models tested are not suitable for identifying Asian women at increased risk within this short interval.
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Affiliation(s)
- Peh Joo Ho
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Genome, Singapore 138672, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117549, Singapore
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore 119228, Singapore
| | - Elaine Hsuen Lim
- Division of Medical Oncology, National Cancer Centre Singapore, Singapore 168583, Singapore
| | - Nur Khaliesah Binte Mohamed Ri
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117549, Singapore
| | - Mikael Hartman
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117549, Singapore
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore 119228, Singapore
- Department of Surgery, University Surgical Cluster, National University Hospital, Singapore 119228, Singapore
| | - Fuh Yong Wong
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore 168583, Singapore
| | - Jingmei Li
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Genome, Singapore 138672, Singapore
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore 119228, Singapore
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Ashi K, Ndom P, Gakwaya A, Makumbi T, Olopade OI, Huo D. Validation of the Nigerian Breast Cancer Study Model for Predicting Individual Breast Cancer Risk in Cameroon and Uganda. Cancer Epidemiol Biomarkers Prev 2023; 32:98-104. [PMID: 36215182 PMCID: PMC9839477 DOI: 10.1158/1055-9965.epi-22-0869] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 09/12/2022] [Accepted: 10/04/2022] [Indexed: 01/17/2023] Open
Abstract
BACKGROUND The Nigerian Breast Cancer Study (NBCS) model is a new risk assessment tool developed for predicting risk of invasive breast cancer in Nigeria. Its applicability outside of Nigeria remains uncertain as it has not been validated in other sub-Saharan Africa populations. METHODS We conducted a case-control study among women with breast cancer and controls ascertained in Cameroon and Uganda from 2011 to 2016. Structured questionnaire interviews were performed to collect risk factor characteristics. The NBCS model, the Gail model, the Gail model for Black population, and the Black Women's Health Study model were applied to the Cameroon and Uganda samples separately. Nigerian as well as local incidence rates were incorporated into the models. Receiver-Operating Characteristic analyses were performed to indicate discriminating capacity. RESULTS The study included 550 cases (mean age 46.8 ± 11.9) and 509 controls (mean age 46.3 ± 11.7). Compared with the other three models, the NBCS model performed best in both countries. The discriminating accuracy of the NBCS model in Cameroon (age-adjusted C-index = 0.602; 95% CI, 0.542-0.661) was better than in Uganda (age-adjusted C-index = 0.531; 95% CI, 0.459-0.603). CONCLUSIONS These findings demonstrate the potential clinical utility of the NBCS model for risk assessment in Cameroon. All currently available models performed poorly in Uganda, which suggests that the NBCS model may need further calibration before use in other regions of Africa. IMPACT Differences in risk profiles across the African diaspora underscores the need for larger studies and may require development of region-specific risk assessment tools for breast cancer.
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Affiliation(s)
- Kevin Ashi
- Pritzker School of Medicine, University of Chicago, Chicago, Illinois, USA
| | - Paul Ndom
- Hôpital Général Yaoundé, Yaoundé, Cameroon
| | | | | | - Olufunmilayo I. Olopade
- Center for Clinical Cancer Genetics and Global Health, Department of Medicine, University of Chicago, Chicago, Illinois, USA,To whom correspondence should be addressed: Dezheng Huo, MD, PhD, Department of Public Health Sciences, The University of Chicago, 5841 S. Maryland Ave., Chicago, IL 60637, ; Olufunmilayo I. Olopade, MD, Center for Clinical Cancer Genetics and Global Health, Department of Medicine, The University of Chicago, 5841 S. Maryland Ave., Chicago, IL 60637,
| | - Dezheng Huo
- Department of Public Health Sciences, University of Chicago, Chicago, Illinois, USA,To whom correspondence should be addressed: Dezheng Huo, MD, PhD, Department of Public Health Sciences, The University of Chicago, 5841 S. Maryland Ave., Chicago, IL 60637, ; Olufunmilayo I. Olopade, MD, Center for Clinical Cancer Genetics and Global Health, Department of Medicine, The University of Chicago, 5841 S. Maryland Ave., Chicago, IL 60637,
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Qi R, Lin J, Chen S, Jiang J, Zhang X, Yao B, Zheng H, Jin Z, Yuan Y, Hou W, Hua B, Guo Q. Breast cancer prognosis and P-cadherin expression: systematic review and study-level meta-analysis. BMJ Support Palliat Care 2022; 12:e893-e905. [PMID: 32943470 DOI: 10.1136/bmjspcare-2020-002204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 07/01/2020] [Accepted: 07/23/2020] [Indexed: 12/24/2022]
Abstract
OBJECTIVE P-cadherin can act both as a tumour suppressor and an oncogene. The clinical prognostic value of P-cadherin overexpression in breast cancer (BC) remains unclear. We conducted a study-level meta-analysis to determine whether P-cadherin expression can help predict prognosis in BC. METHODS A systematic literature search was performed to review eligible studies and clarify the relationship between P-cadherin overexpression and overall survival (OS), disease-free survival (DFS), pathological features, molecular subtypes and molecular phenotypes in BC. RESULTS Thirty-one studies including 12 332 patients were included. P-cadherin overexpression was correlated with significantly worse OS (HR=1.77, p<0.00001) and DFS (HR=1.96, p<0.00001) than P-cadherin-negative. P-cadherin overexpression could lead to high histological grade (OR=3.33, p<0.00001) and lymph node metastasis (OR=1.62, p<0.00001). Moreover, P-cadherin overexpression was associated with low odds of the luminal A subtype and high odds of the human epidermal growth factor receptor-2 (HER2)-positive and triple-negative subtypes. P-cadherin expression led to low expression of oestrogen and progesterone receptor (OR=0.37 and OR=0.36, respectively, both p<0.00001) and high expression of HER2 (OR=2.31, p<0.00001), Ki-67 (OR=2.79, p<0.00001), epidermal growth factor receptor (OR=5.85, p<0.00001) and cytokeratin 5/6 (OR=6.79, p<0.00001). CONCLUSIONS P-cadherin was found to be associated with invasiveness and metastasis. P-cadherin expression can probably be a useful biomarker for predicting poor survival and may act as an independent prognostic predictor.
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Affiliation(s)
- Runzhi Qi
- Department of Oncology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Jinyin Lin
- Administrative Department, Beijing Tongren Hospital Affiliated to Capital Medical University, Beijing, China
| | - Shuntai Chen
- Beijing University of Chinese Medicine, Beijing, China
| | - Juling Jiang
- Department of Oncology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Xing Zhang
- Department of Oncology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Bo Yao
- First Clinical Medical College, Heilongjiang University of Chinese Medicine, Heilongjiang, China
| | - Honggang Zheng
- Department of Oncology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Zhichao Jin
- Department of Oncology, Jiangsu Province Hospital of Chinese Medicine, Nanjing, China
| | - Yuan Yuan
- Department of Pneumology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Wei Hou
- Department of Oncology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Baojin Hua
- Department of Oncology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Qiujun Guo
- Department of Oncology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
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15
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Feng R, Su Q, Huang X, Basnet T, Xu X, Ye W. Cancer situation in China: what does the China cancer map indicate from the first national death survey to the latest cancer registration? CANCER COMMUNICATIONS (LONDON, ENGLAND) 2022; 43:75-86. [PMID: 36397729 PMCID: PMC9859730 DOI: 10.1002/cac2.12393] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Revised: 10/06/2022] [Accepted: 11/04/2022] [Indexed: 11/19/2022]
Abstract
BACKGROUND Over the past four decades, the Chinese government has conducted three surveys on the distribution of causes of death and built cancer registration. In order to shine a new light on better cancer prevention strategies in China, we evaluated the profile of cancer mortality over the forty years and analyzed the policies that have been implemented. METHODS We described spatial and temporal changes in both cancer mortality and the ranking of major cancer types in China based on the data collected from three national surveys during 1973-1975, 1990-1992, 2004-2005, and the latest cancer registration data published by National Central Cancer Registry of China. The mortality data were compared after conversion to age-standardized mortality rates based on the world standard population (Segi's population). The geographical distribution characteristics were explored by marking hot spots of different cancers on the map of China. RESULTS From 1973 to 2016, China witnessed an evident decrease in mortality rate of stomach, esophageal, and cervical cancer, while a gradual increase was recorded in lung, colorectal, and female breast cancer. A slight decrease of mortality rate has been observed in liver cancer since 2004. Lung and liver cancer, however, have become the top two leading causes of cancer death for the last twenty years. From the three national surveys, similar profiles of leading causes of cancer death were observed among both urban and rural areas. Lower mortality rates from esophageal and stomach cancer, however, have been demonstrated in urban than in rural areas. Rural areas had similar mortality rates of the five leading causes of cancer death with the small urban areas in 1973-1975. Additionally, rural areas in 2016 also had approximate mortality rates of the five leading causes with urban areas in 2004-2005. Moreover, stomach, esophageal, and liver cancer showed specific geographical distributions. Although mortality rates have decreased at most of the hotspots of these cancers, they were still higher than the national average levels during the same time periods. CONCLUSIONS Building up a strong primary public health system especially among rural areas may be one critical step to reduce cancer burden in China.
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Affiliation(s)
- Ruimei Feng
- Department of EpidemiologySchool of Public HealthShanxi Medical UniversityTaiyuanShanxiP. R. China,Department of Epidemiology and Health Statistics & Key Laboratory of Ministry of Education for Gastrointestinal CancerFujian Medical UniversityFuzhouFujianP. R. China
| | - Qingling Su
- Department of Epidemiology and Health Statistics & Key Laboratory of Ministry of Education for Gastrointestinal CancerFujian Medical UniversityFuzhouFujianP. R. China
| | - Xiaoyin Huang
- Department of Epidemiology and Health Statistics & Key Laboratory of Ministry of Education for Gastrointestinal CancerFujian Medical UniversityFuzhouFujianP. R. China
| | - Til Basnet
- Department of Epidemiology and Health Statistics & Key Laboratory of Ministry of Education for Gastrointestinal CancerFujian Medical UniversityFuzhouFujianP. R. China
| | - Xin Xu
- Department of Epidemiology and Health Statistics & Key Laboratory of Ministry of Education for Gastrointestinal CancerFujian Medical UniversityFuzhouFujianP. R. China
| | - Weimin Ye
- Department of Epidemiology and Health Statistics & Key Laboratory of Ministry of Education for Gastrointestinal CancerFujian Medical UniversityFuzhouFujianP. R. China,Department of Medical Epidemiology and BiostatisticsKarolinska InstituteStockholmSweden
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16
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Lawson-Michod KA, Watt MH, Grieshober L, Green SE, Karabegovic L, Derzon S, Owens M, McCarty RD, Doherty JA, Barnard ME. Pathways to ovarian cancer diagnosis: a qualitative study. BMC Womens Health 2022; 22:430. [PMCID: PMC9636716 DOI: 10.1186/s12905-022-02016-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 10/14/2022] [Indexed: 11/06/2022] Open
Abstract
Abstract
Background
Ovarian cancer is often diagnosed at a late stage, when survival is poor. Qualitative narratives of patients’ pathways to ovarian cancer diagnoses may identify opportunities for earlier cancer detection and, consequently, earlier stage at diagnosis.
Methods
We conducted semi-structured interviews of ovarian cancer patients and survivors (n = 14) and healthcare providers (n = 11) between 10/2019 and 10/2021. Interviews focused on the time leading up to an ovarian cancer diagnosis. Thematic analysis was conducted by two independent reviewers using a two-phase deductive and inductive coding approach. Deductive coding used a priori time intervals from the validated Model of Pathways to Treatment (MPT), including self-appraisal and management of symptoms, medical help-seeking, diagnosis, and pre-treatment. Inductive coding identified common themes within each stage of the MPT across patient and provider interviews.
Results
The median age at ovarian cancer diagnosis was 61.5 years (range, 29–78 years), and the majority of participants (11/14) were diagnosed with advanced-stage disease. The median time from first symptom to initiation of treatment was 2.8 months (range, 19 days to 4.7 years). The appraisal and help-seeking intervals contributed the greatest delays in time-to-diagnosis for ovarian cancer. Nonspecific symptoms, perceptions of health and aging, avoidant coping strategies, symptom embarrassment, and concerns about potential judgment from providers prolonged the appraisal and help-seeking intervals. Patients and providers also emphasized access to care, including financial access, as critical to a timely diagnosis.
Conclusion
Interventions are urgently needed to reduce ovarian cancer morbidity and mortality. Population-level screening remains unlikely to improve ovarian cancer survival, but findings from our study suggest that developing interventions to improve self-appraisal of symptoms and reduce barriers to help-seeking could reduce time-to-diagnosis for ovarian cancer. Affordability of care and insurance may be particularly important for ovarian cancer patients diagnosed in the United States.
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17
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Huang Y, Wang H, Lyu Z, Dai H, Liu P, Zhu Y, Song F, Chen K. Development and evaluation of the screening performance of a low-cost high-risk screening strategy for breast cancer. Cancer Biol Med 2022; 19:j.issn.2095-3941.2020.0758. [PMID: 34570443 PMCID: PMC9500221 DOI: 10.20892/j.issn.2095-3941.2020.0758] [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] [Indexed: 01/31/2023] Open
Abstract
OBJECTIVE To develop and evaluate the screening performance of a low-cost high-risk screening strategy for breast cancer in low resource areas. METHODS Based on the Multi-modality Independent Screening Trial, 6 questionnaire-based risk factors of breast cancer (age at menarche, age at menopause, age at first live birth, oral contraceptive, obesity, family history of breast cancer) were used to determine the women with high risk of breast cancer. The screening performance of clinical breast examination (CBE), breast ultrasonography (BUS), and mammography (MAM) were calculated and compared to determine the optimal screening method for these high risk women. RESULTS A total of 94 breast cancers were detected among 31,720 asymptomatic Chinese women aged 45-65 years. Due to significantly higher detection rates (DRs) and suitable coverage of the population, high risk women were defined as those with any of 6 risk factors. Among high risk women, the DR for BUS [3.09/1,000 (33/10,694)] was similar to that for MAM [3.18/1,000 (34/10,696)], while it was significantly higher than that for the CBE [1.73/1,000 (19/10,959), P = 0.002]. Compared with MAM, BUS showed significantly higher specificity [98.64% (10,501/10,646) vs. 98.06% (10,443/10,650), P = 0.001], but no significant differences in sensitivity [68.75% (33/48) vs. 73.91% (34/46)], positive prediction values [18.54% (33/178) vs. 14.11% (34/241)], and negative prediction values [99.86% (10,501/10,516) vs. 99.89% (10,443/10,455)]. Further analyses showed no significant difference in the percentages of early stage breast cancer [53.57% (15/28) vs. 50.00% (15/30)], lymph node involvement [22.73% (5/22) vs. 28.00% (7/25)], and tumor size ≥ 2 cm [37.04% (10/27) vs. 29.03% (9/31)] between BUS and MAM. Subgroup analyses stratified by breast densities or age at enrollment showed similar results. CONCLUSIONS The low-cost high-risk screening strategy based on 6 questionnaire-based risk factors was an easy-to-use method to identify women with high risk of breast cancer. Moreover, BUS and MAM had comparable screening performances among high risk women.
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Affiliation(s)
- Yubei Huang
- Department of Cancer Epidemiology and Biostatistics, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Molecular Epidemiology of Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, Tianjin 300060, China
| | - Huan Wang
- Department of Cancer Epidemiology and Biostatistics, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Molecular Epidemiology of Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, Tianjin 300060, China
| | - Zhangyan Lyu
- Department of Cancer Epidemiology and Biostatistics, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Molecular Epidemiology of Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, Tianjin 300060, China
| | - Hongji Dai
- Department of Cancer Epidemiology and Biostatistics, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Molecular Epidemiology of Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, Tianjin 300060, China
| | - Peifang Liu
- Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Molecular Epidemiology of Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, Tianjin 300060, China
| | - Ying Zhu
- Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Molecular Epidemiology of Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, Tianjin 300060, China
| | - Fengju Song
- Department of Cancer Epidemiology and Biostatistics, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Molecular Epidemiology of Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, Tianjin 300060, China
| | - Kexin Chen
- Department of Cancer Epidemiology and Biostatistics, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Molecular Epidemiology of Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, Tianjin 300060, China,Correspondence to: Kexin Chen, E-mail:
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Ren W, Chen M, Qiao Y, Zhao F. Global guidelines for breast cancer screening: A systematic review. Breast 2022; 64:85-99. [PMID: 35636342 PMCID: PMC9142711 DOI: 10.1016/j.breast.2022.04.003] [Citation(s) in RCA: 74] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Revised: 03/28/2022] [Accepted: 04/09/2022] [Indexed: 11/30/2022] Open
Abstract
Objectives Breast cancer screening guidelines could provide valuable tools for clinical decision making by reviewing the available evidence and providing recommendations. Little information is known about how many countries have issued breast cancer screening guidelines and the differences among existing guidelines. We systematically reviewed current guidelines and summarized corresponding recommendations, to provide references for good clinical practice in different countries. Methods Systematic searches of MEDLINE, EMBASE, Web of Science, and Scopus from inception to March 27th, 2021 were conducted and supplemented by reviewing the guideline development organizations. The quality of screening guidelines was assessed from six domains of the Appraisal of Guidelines for Research and Evaluation Ⅱ (AGREE Ⅱ) instrument by two appraisers. The basic information and recommendations of the issued guidelines were extracted and summarized. Results A total of 23 guidelines issued between 2010 and 2021 in 11 countries or regions were identified for further review. The content and quality varied across the guidelines. The average AGREE Ⅱ scores of the guidelines ranged from 33.3% to 87.5%. The highest domain score was "clarity of presentation" while the domain with the lowest score was "applicability". For average-risk women, most of the guidelines recommended mammographic screening for those aged 40–74 years, specifically, those aged 50–69 years were regarded as the optimal age group for screening. Nine of 23 guidelines recommended against an upper age limit for breast cancer screening. Mammography (MAM) was recommended as the primary screening modality for average-risk women by all included guidelines. Most guidelines suggested annual or biennial mammographic screening. Risk factors of breast cancer identified in the guidelines mainly fell within five categories which could be broadly summarized as the personal history of pre-cancerous lesions and/or breast cancer; the family history of breast cancer; the known genetic predisposition of breast cancer; the history of mantle or chest radiation therapy; and dense breasts. For women at higher risk, there was a consensus among most guidelines that annual MAM or annual magnetic resonance imaging (MRI) should be given, and the screening should begin earlier than the average-risk group. Conclusions The majority of 23 included international guidelines were issued by developed countries which contained roughly the same but not identical recommendations on breast cancer screening age, methods, and intervals. Most guidelines recommended annual or biennial mammographic screening between 40 and 74 years for average-risk populations and annual MAM or annual MRI starting from a younger age for high-risk populations. Current guidelines varied in quality and increased efforts are needed to improve the methodological quality of guidance documents. Due to lacking clinical practice guidelines tailored to different economic levels, low- and middle-income countries (LMICs) should apply and implement the evidence-based guidelines with higher AGREE Ⅱ scores considering local adaption. This systematic review comprehensively maps the recommendations of the latest international breast screening guidelines, providing valuable tools for clinical decision making in different settings. Most guidelines recommend annual or biennial mammographic screening between 40 and 74 years for the average-risk populations and annual MAM or annual MRI starting from a younger age for the high-risk populations. However, there are indeed discrepancies in screening age, methods, and intervals among countries. High-quality evidence and rigorous methodology are the keys to guidance development, but current guidelines vary in methodological quality.
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Affiliation(s)
- Wenhui Ren
- Department of Cancer Epidemiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
| | - Mingyang Chen
- Center for Global Health, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
| | - Youlin Qiao
- Center for Global Health, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
| | - Fanghui Zhao
- Department of Cancer Epidemiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
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19
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BREAst screening Tailored for HEr (BREATHE)-A study protocol on personalised risk-based breast cancer screening programme. PLoS One 2022; 17:e0265965. [PMID: 35358246 PMCID: PMC8970365 DOI: 10.1371/journal.pone.0265965] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 02/22/2022] [Indexed: 12/29/2022] Open
Abstract
Routine mammography screening is currently the standard tool for finding cancers at an early stage, when treatment is most successful. Current breast screening programmes are one-size-fits-all which all women above a certain age threshold are encouraged to participate. However, breast cancer risk varies by individual. The BREAst screening Tailored for HEr (BREATHE) study aims to assess acceptability of a comprehensive risk-based personalised breast screening in Singapore. Advancing beyond the current age-based screening paradigm, BREATHE integrates both genetic and non-genetic breast cancer risk prediction tools to personalise screening recommendations. BREATHE is a cohort study targeting to recruit ~3,500 women. The first recruitment visit will include questionnaires and a buccal cheek swab. After receiving a tailored breast cancer risk report, participants will attend an in-person risk review, followed by a final session assessing the acceptability of our risk stratification programme. Risk prediction is based on: a) Gail model (non-genetic), b) mammographic density and recall, c) BOADICEA predictions (breast cancer predisposition genes), and d) breast cancer polygenic risk score. For national implementation of personalised risk-based breast screening, exploration of the acceptability within the target populace is critical, in addition to validated predication tools. To our knowledge, this is the first study to implement a comprehensive risk-based mammography screening programme in Asia. The BREATHE study will provide essential data for policy implementation which will transform the health system to deliver a better health and healthcare outcomes.
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20
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Hou C, Xu B, Hao Y, Yang D, Song H, Li J. Development and validation of polygenic risk scores for prediction of breast cancer and breast cancer subtypes in Chinese women. BMC Cancer 2022; 22:374. [PMID: 35395775 PMCID: PMC8991589 DOI: 10.1186/s12885-022-09425-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 03/15/2022] [Indexed: 02/08/2023] Open
Abstract
Background Studies investigating breast cancer polygenic risk score (PRS) in Chinese women are scarce. The objectives of this study were to develop and validate PRSs that could be used to stratify risk for overall and subtype-specific breast cancer in Chinese women, and to evaluate the performance of a newly proposed Artificial Neural Network (ANN) based approach for PRS construction. Methods The PRSs were constructed using the dataset from a genome-wide association study (GWAS) and validated in an independent case-control study. Three approaches, including repeated logistic regression (RLR), logistic ridge regression (LRR) and ANN based approach, were used to build the PRSs for overall and subtype-specific breast cancer based on 24 selected single nucleotide polymorphisms (SNPs). Predictive performance and calibration of the PRSs were evaluated unadjusted and adjusted for Gail-2 model 5-year risk or classical breast cancer risk factors. Results The primary PRSANN and PRSLRR both showed modest predictive ability for overall breast cancer (odds ratio per interquartile range increase of the PRS in controls [IQ-OR] 1.76 vs 1.58; area under the receiver operator characteristic curve [AUC] 0.601 vs 0.598) and remained to be predictive after adjustment. Although estrogen receptor negative (ER−) breast cancer was poorly predicted by the primary PRSs, the ER− PRSs trained solely on ER− breast cancer cases saw a substantial improvement in predictions of ER− breast cancer. Conclusions The 24 SNPs based PRSs can provide additional risk information to help breast cancer risk stratification in the general population of China. The newly proposed ANN approach for PRS construction has potential to replace the traditional approaches, but more studies are needed to validate and investigate its performance. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-022-09425-3.
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Affiliation(s)
- Can Hou
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, No. 37 Guo Xue Xiang, Chengdu, 610047, Sichuan, China.,Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, No.16 Ren Min Nan Lu, Chengdu, 610041, Sichuan, China.,Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Bin Xu
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, No.16 Ren Min Nan Lu, Chengdu, 610041, Sichuan, China
| | - Yu Hao
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, No.16 Ren Min Nan Lu, Chengdu, 610041, Sichuan, China
| | - Daowen Yang
- Robot Perception and Control Joint Lab, Sichuan University & Aisono, Chengdu, China
| | - Huan Song
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, No. 37 Guo Xue Xiang, Chengdu, 610047, Sichuan, China. .,Med-X Center for Informatics, Sichuan University, Chengdu, China.
| | - Jiayuan Li
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, No.16 Ren Min Nan Lu, Chengdu, 610041, Sichuan, China.
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21
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Ding R, Xiao Y, Mo M, Zheng Y, Jiang YZ, Shao ZM. Breast cancer screening and early diagnosis in Chinese women. Cancer Biol Med 2022; 19:j.issn.2095-3941.2021.0676. [PMID: 35380032 PMCID: PMC9088185 DOI: 10.20892/j.issn.2095-3941.2021.0676] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 02/21/2022] [Indexed: 01/01/2023] Open
Abstract
Breast cancer is the most common malignant tumor in Chinese women, and its incidence is increasing. Regular screening is an effective method for early tumor detection and improving patient prognosis. In this review, we analyze the epidemiological changes and risk factors associated with breast cancer in China and describe the establishment of a screening strategy suitable for Chinese women. Chinese patients with breast cancer tend to be younger than Western patients and to have denser breasts. Therefore, the age of initial screening in Chinese women should be earlier, and the importance of screening with a combination of ultrasound and mammography is stressed. Moreover, Chinese patients with breast cancers have several ancestry-specific genetic features, and aiding in the determination of genetic screening strategies for identifying high-risk populations. On the basis of current studies, we summarize the development of risk-stratified breast cancer screening guidelines for Chinese women and describe the significant improvement in the prognosis of patients with breast cancer in China.
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Affiliation(s)
- Rui Ding
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Yi Xiao
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Miao Mo
- Department of Cancer Prevention, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Ying Zheng
- Department of Cancer Prevention, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Yi-Zhou Jiang
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Zhi-Ming Shao
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
- Precision Cancer Medicine Center, Fudan University Shanghai Cancer Center, Shanghai 200032, China
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22
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Lamb LR, Baird GL, Roy IT, Choi PHS, Lehman CD, Miles RC. Are English-language online patient education materials related to breast cancer risk assessment understandable, readable, and actionable? Breast 2022; 61:29-34. [PMID: 34894464 PMCID: PMC8665407 DOI: 10.1016/j.breast.2021.11.012] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 11/16/2021] [Accepted: 11/16/2021] [Indexed: 11/19/2022] Open
Abstract
PURPOSE To evaluate the readability, understandability, and actionability of online patient education materials (OPEM) related to breast cancer risk assessment. MATERIAL AND METHODS We queried seven English-language search terms related to breast cancer risk assessment: breast cancer high-risk, breast cancer risk factors, breast cancer family history, BRCA, breast cancer risk assessment, Tyrer-Cuzick, and Gail model. Websites were categorized as: academic/hospital-based, commercial, government, non-profit or academic based on the organization hosting the site. Grade-level readability of qualifying websites and categories was determined using readability metrics and generalized estimating equations based on written content only. Readability scores were compared to the recommended parameters set by the American Medical Association (AMA). Understandability and actionability of OPEM related to breast cancer high-risk were evaluated using the Patient Education Materials Assessment Tool (PEMAT) and compared to criteria set at ≥70%. Descriptive statistics and inter-rater reliability analysis were utilized. RESULTS 343 websites were identified, of which 162 met study inclusion criteria. The average grade readability score was 12.1 across all websites (range 10.8-13.4). No website met the AMA recommendation. Commercial websites demonstrated the highest overall average readability of 13.1. Of the 26 websites related to the search term breast cancer high-risk, the average understandability and actionability scores were 62% and 34% respectively, both below criteria. CONCLUSIONS OPEM on breast cancer risk assessment available to the general public do not meet criteria for readability, understandability, or actionability. To ensure patient comprehension of medical information online, future information should be published in simpler, more appropriate terms.
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Affiliation(s)
- Leslie R Lamb
- Massachusetts General Hospital, Department of Radiology, 55 Fruit Street Boston, MA, 02114-2696, USA.
| | - Grayson L Baird
- Rhode Island Hospital, Warren Alpert School of Medicine at Brown University, Department of Diagnostic Imaging, 593 Eddy Street, Providence, RI, 02903, USA.
| | - Ishita T Roy
- Massachusetts General Hospital, Department of Radiology, 55 Fruit Street Boston, MA, 02114-2696, USA.
| | - Paul H S Choi
- Tufts Medical Center, 800 Washington St Boston, MA, 02111, USA.
| | - Constance D Lehman
- Massachusetts General Hospital, Department of Radiology, 55 Fruit Street Boston, MA, 02114-2696, USA.
| | - Randy C Miles
- Massachusetts General Hospital, Department of Radiology, 55 Fruit Street Boston, MA, 02114-2696, USA.
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23
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Validation of Breast Cancer Risk Models by Race/Ethnicity, Family History and Molecular Subtypes. Cancers (Basel) 2021; 14:cancers14010045. [PMID: 35008209 PMCID: PMC8750569 DOI: 10.3390/cancers14010045] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 12/09/2021] [Accepted: 12/20/2021] [Indexed: 12/28/2022] Open
Abstract
Simple Summary Several statistical models exist to predict a person’s risk of breast cancer. Risk assessment models can guide cancer screening approaches by identifying individuals who would benefit from additional screening. In this study, we compared the performance of four models in predicting the 5-year risk of breast cancer in a cohort of women aged 40–84 years who underwent screening mammography at three large health systems. Models showed comparable discrimination (ability to distinguish between cases and non-cases) and calibration (ability to accurately predict risk) overall, with no difference by race. Model discrimination was poorer for some cancer subtypes, and better for women with high BMI. The combined BRCAPRO+BCRAT model had improved calibration and discrimination among women with a family history of breast cancer. Our results can inform risk-based screening approaches by identifying women at a high risk of breast cancer. Abstract (1) Background: The purpose of this study is to compare the performance of four breast cancer risk prediction models by race, molecular subtype, family history of breast cancer, age, and BMI. (2) Methods: Using a cohort of women aged 40–84 without prior history of breast cancer who underwent screening mammography from 2006 to 2015, we generated breast cancer risk estimates using the Breast Cancer Risk Assessment tool (BCRAT), BRCAPRO, Breast Cancer Surveillance Consortium (BCSC) and combined BRCAPRO+BCRAT models. Model calibration and discrimination were compared using observed-to-expected ratios (O/E) and the area under the receiver operator curve (AUC) among patients with at least five years of follow-up. (3) Results: We observed comparable discrimination and calibration across models. There was no significant difference in model performance between Black and White women. Model discrimination was poorer for HER2+ and triple-negative subtypes compared with ER/PR+HER2−. The BRCAPRO+BCRAT model displayed improved calibration and discrimination compared to BRCAPRO among women with a family history of breast cancer. Across models, discriminatory accuracy was greater among obese than non-obese women. When defining high risk as a 5-year risk of 1.67% or greater, models demonstrated discordance in 2.9% to 19.7% of patients. (4) Conclusions: Our results can inform the implementation of risk assessment and risk-based screening among women undergoing screening mammography.
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Vegunta S, Kling JM, Patel BK. Supplemental Cancer Screening for Women With Dense Breasts: Guidance for Health Care Professionals. Mayo Clin Proc 2021; 96:2891-2904. [PMID: 34686363 DOI: 10.1016/j.mayocp.2021.06.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 05/20/2021] [Accepted: 06/08/2021] [Indexed: 11/16/2022]
Abstract
Mammography is the standard for breast cancer screening. The sensitivity of mammography in identifying breast cancer, however, is reduced for women with dense breasts. Thirty-eight states have passed laws requiring that all women be notified of breast tissue density results in their mammogram report. The notification includes a statement that differs by state, encouraging women to discuss supplemental screening options with their health care professionals (HCPs). Several supplemental screening tests are available for women with dense breast tissue, but no established guidelines exist to direct HCPs in their recommendation of preferred supplemental screening test. Tailored screening, which takes into consideration the patient's mammographic breast density and lifetime breast cancer risk, can guide breast cancer screening strategies that are more comprehensive. This review describes the benefits and limitations of the various available supplemental screening tests to guide HCPs and patients in choosing the appropriate breast cancer screening.
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Affiliation(s)
- Suneela Vegunta
- Division of Women's Health Internal Medicine, Mayo Clinic, Scottsdale, AZ.
| | - Juliana M Kling
- Division of Women's Health Internal Medicine, Mayo Clinic, Scottsdale, AZ
| | - Bhavika K Patel
- Division of Breast Imaging, Mayo Clinic Hospital, Phoenix, AZ
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25
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Vegunta S, Bhatt AA, Choudhery SA, Pruthi S, Kaur AS. Identifying women with increased risk of breast cancer and implementing risk-reducing strategies and supplemental imaging. Breast Cancer 2021; 29:19-29. [PMID: 34665436 DOI: 10.1007/s12282-021-01298-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Accepted: 09/16/2021] [Indexed: 10/20/2022]
Abstract
Breast cancer (BC) is the second most common cancer in women, affecting 1 in 8 women in the United States (12.5%) in their lifetime. However, some women have a higher lifetime risk of BC because of genetic and lifestyle factors, mammographic breast density, and reproductive and hormonal factors. Because BC risk is variable, screening and prevention strategies should be individualized after considering patient-specific risk factors. Thus, health care professionals need to be able to assess risk profiles, identify high-risk women, and individualize screening and prevention strategies through a shared decision-making process. In this article, we review the risk factors for BC, risk-assessment models that identify high-risk patients, and preventive medications and lifestyle modifications that may decrease risk. We also discuss the benefits and limitations of various supplemental screening methods.
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Affiliation(s)
- Suneela Vegunta
- Division of Women's Health Internal Medicine, Mayo Clinic, 13400 E Shea Blvd, Scottsdale, AZ, 85259, USA.
| | - Asha A Bhatt
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | | | - Sandhya Pruthi
- Division of General Internal Medicine, Breast Cancer Clinic, Mayo Clinic, Rochester, MN, USA
| | - Aparna S Kaur
- Division of General Internal Medicine, Breast Cancer Clinic, Mayo Clinic, Rochester, MN, USA
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26
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Bonnet E, Daures JP, Landais P. Determination of thresholds of risk in women at average risk of breast cancer to personalize the organized screening program. Sci Rep 2021; 11:19104. [PMID: 34580360 PMCID: PMC8476568 DOI: 10.1038/s41598-021-98604-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 09/06/2021] [Indexed: 11/08/2022] Open
Abstract
In France, more than 10 million women at "average" risk of breast cancer (BC), are included in the organized BC screening. Existing predictive models of BC risk are not adapted to the French population. Thus, we set up a new score in the French Hérault region and looked for subgroups at a graded level of risk in women at "average" risk. We recruited a retrospective cohort of women, aged 50 to 60, who underwent the organized BC screening, and included 2241 non-cancer women and 527 who developed a BC during a 12-year follow-up period (2006-2018). The risk factors identified were high breast density (ACR BI-RADS grading)(B vs A: HR = 1.41, 95%CI [1.05; 1.9], p = 0.023; C vs A: HR = 1.65 [1.2; 2.27], p = 0.02 ; D vs A: HR = 2.11 [1.25;3.58], p = 0.006), a history of maternal breast cancer (HR = 1.61 [1.24; 2.09], p < 0.001), and socioeconomic difficulties (HR 1.23 [1.09; 1.55], p = 0.003). While early menopause (HR = 0.36 [0.13; 0.99], p = 0.003) and an age at menarche after 12 years (HR = 0.77 [0.63; 0.95], p = 0.047) were protective factors. We identified 3 groups at risk: lower, average, and higher, respectively. A low threshold was characterized at 1.9% of 12-year risk and a high threshold at 4.5% 12-year risk. Mean 12-year risks in the 3 groups of risk were 1.37%, 2.68%, and 5.84%, respectively. Thus, 12% of women presented a level of risk different from the average risk group, corresponding to 600,000 women involved in the French organized BC screening, enabling to propose a new strategy to personalize the national BC screening. On one hand, for women at lower risk, we proposed to reduce the frequency of mammograms and on the other hand, for women at higher risk, we suggested intensifying surveillance.
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Affiliation(s)
- Emmanuel Bonnet
- Montpellier University, EA2415, Institut Universitaire de recherche clinique, 34093, Montpellier Cedex 5, France.
- Languedoc Mutualité, Nouvelles Technologies, AESIO, Montpellier, France.
| | - Jean-Pierre Daures
- Montpellier University, EA2415, Institut Universitaire de recherche clinique, 34093, Montpellier Cedex 5, France
- Languedoc Mutualité, Nouvelles Technologies, AESIO, Montpellier, France
| | - Paul Landais
- Montpellier University, EA2415, Institut Universitaire de recherche clinique, 34093, Montpellier Cedex 5, France
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27
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Prevalence of Breast Cancer in Female Physicians Performing Procedures With Significant Fluoroscopy Exposure: Survey. J Comput Assist Tomogr 2021; 45:704-710. [PMID: 34469902 DOI: 10.1097/rct.0000000000001186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVE The aim of this study was to assess self-reported breast cancer prevalence potentially associated with occupational radiation exposure from fluoroscopy-guided procedures in female physicians using current standard protection measures. METHODS An institutional review board-approved survey was shared as a link to self-identified female physicians. We compared self-reported prevalence of breast cancer among women physicians with longer than 10 years of postfellowship practice in specialties with heavy fluoroscopy exposure versus specialties with low fluoroscopy exposure. We compared the distribution of breast cancer risk factors and personal radiation safety measures. RESULTS A total of 303 women physicians participated in the survey. There were 8 (16%) of 49 from the first study group and 8 (18%) of 44 from the second study group who self-reported a diagnosis of breast cancer. There were no differences in the distribution of breast cancer risk factors between the 2 groups or prevalence of breast cancer (P = 0.81). CONCLUSIONS Self-reported breast cancer prevalence is similar between women physicians who are practicing fluoroscopically heavy and light medical specialties.
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28
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Choudhury A, Perumalla S. Detecting breast cancer using artificial intelligence: Convolutional neural network. Technol Health Care 2021; 29:33-43. [PMID: 32444590 DOI: 10.3233/thc-202226] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
BACKGROUND One of the most broadly founded approaches to envisage cancer treatment relies upon a pathologist's efficiency to visually inspect the appearances of bio-markers on the invasive tumor tissue section. Lately, deep learning techniques have radically enriched the ability of computers to identify objects in images fostering the prospect for fully automated computer-aided diagnosis. Given the noticeable role of nuclear structure in cancer detection, AI's pattern recognizing ability can expedite the diagnostic process. OBJECTIVE In this study, we propose and implement an image classification technique to identify breast cancer. METHODS We implement the convolutional neural network (CNN) on breast cancer image data set to identify invasive ductal carcinoma (IDC). RESULT The proposed CNN model after data augmentation yielded 78.4% classification accuracy. 16% of IDC (-) were predicted incorrectly (false negative) whereas 25% of IDC (+) were predicted incorrectly (false positive). CONCLUSION The results achieved by the proposed approach have shown that it is feasible to employ a convolutional neural network particularly for breast cancer classification tasks. However, a common problem in any artificial intelligence algorithm is its dependence on the data set. Therefore, the performance of the proposed model might not be generalized.
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Affiliation(s)
- Avishek Choudhury
- School of Systems and Entereprises, Stevens Institute of Technology, Hoboken, NJ, USA
| | - Sunanda Perumalla
- Clinical and Business Intelligence, Integris Health, Oklahoma City, OK, USA
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29
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Outcomes of Return to Routine Screening for BI-RADS 3 Lesions Detected at Supplemental Automated Whole-Breast Ultrasound in Women with Dense Breasts: A Prospective Study. AJR Am J Roentgenol 2021; 217:1313-1321. [PMID: 34259039 DOI: 10.2214/ajr.21.26180] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Background: Supplemental screening breast ultrasound (US) detects additional cancers in women with dense breasts but identifies many BI-RADS 3 lesions that result in short-term follow-ups and biopsies. Objective: To evaluate outcomes in patients recommended for return to routine screening for lesions assessed as BI-RADS 3 on supplemental automated whole-breast US (ABUS). Methods: This prospective study invited patients with BI-RADS 1 or 2 on screening mammography and breast density C or D to undergo supplemental ABUS. ABUS was interpreted as BI-RADS 1, 2, 3, or 0. Return to routine screening was recommended for ABUS BI-RADS 1, 2, or 3. ABUS BI-RADS 0 lesions underwent targeted hand-held US. Remaining patients were followed for 2 years. Malignancy rates were compared using Fisher's exact tests. Results: A total of 2257 women (mean age, 58.0±11.2 years) were included. Supplemental ABUS was scored as BI-RADS 1 in 1186 (52.5%), BI-RADS 2 in 591 (26.2%), BI-RADS 3 in 395 (17.5%), and BI-RADS 0 in 85 (3.8%). A total of 394 patients with ABUS BI-RADS 3 had 2-year follow-up, during which no cancer (0%, 95% CI 0.0%-0.9%) was diagnosed in the quadrant of the lesion. Among patients with 2-year follow-up, breast cancer was diagnosed in 4/1117 (0.4%) with ABUS BI-RADS 1, 2/556 (0.4%) with ABUS BI-RADS 2, and 2/394 (0.5%) with ABUS BI-RADS 3 (cancer in other quadrant than the lesion). Malignancy rates were not different between ABUS BI-RADS 1, 2, and 3 (p=.28). ABUS recall rate was 3.8% (85/2257; 95% CI 3.6%-4.0%). If short-term follow-up had been recommended for ABUS BI-RADS 3, ABUS recall rate would have been 21.3% (480/2257, 95% CI 19.6%-23.0%). Biopsy rate was 0.4% (12/2257; 95% CI 0.3%-0.9%); positive biopsy rate was 58.3% (7/12). One of 7 patients diagnosed with cancer by initial supplemental ABUS, and none of 8 patients diagnosed with cancer during subsequent follow-up, had node-positive cancer. Conclusions: Return to routine screening for ABUS BI-RADS 3 lesions results in a substantial decrease in recall rate, while being unlikely to result in adverse outcome. Clinical Impact: This prospective study supports a recommendation for routine annual follow-up for BI-RADS 3 lesions at supplemental ABUS.
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30
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Wang F, Duan XN, Ling R, Yu ZG. Clinical practice guidelines for risk assessment to identify women at high risk of breast cancer: Chinese Society of Breast Surgery (CSBrS) practice guidelines 2021. Chin Med J (Engl) 2021; 134:1655-1657. [PMID: 34116529 PMCID: PMC8318626 DOI: 10.1097/cm9.0000000000001502] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Indexed: 12/03/2022] Open
Affiliation(s)
- Fei Wang
- Department of Breast Surgery, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250033, China
| | - Xue-Ning Duan
- Breast Disease Center, Peking University First Hospital, Beijing 100034, China
| | - Rui Ling
- Department of Thyroid, Breast and Vascular Surgery, Xijing Hospital, The Fourth Military Medical University, Xi’an, Shaanxi 710032, China
| | - Zhi-Gang Yu
- Department of Breast Surgery, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250033, China
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31
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Han Y, Lv J, Yu C, Guo Y, Bian Z, Hu Y, Yang L, Chen Y, Du H, Zhao F, Wen W, Shu XO, Xiang Y, Gao YT, Zheng W, Guo H, Liang P, Chen J, Chen Z, Huo D, Li L. Development and external validation of a breast cancer absolute risk prediction model in Chinese population. Breast Cancer Res 2021; 23:62. [PMID: 34051827 PMCID: PMC8164768 DOI: 10.1186/s13058-021-01439-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Accepted: 05/17/2021] [Indexed: 01/06/2023] Open
Abstract
BACKGROUNDS In contrast to developed countries, breast cancer in China is characterized by a rapidly escalating incidence rate in the past two decades, lower survival rate, and vast geographic variation. However, there is no validated risk prediction model in China to aid early detection yet. METHODS A large nationwide prospective cohort, China Kadoorie Biobank (CKB), was used to evaluate relative and attributable risks of invasive breast cancer. A total of 300,824 women free of any prior cancer were recruited during 2004-2008 and followed up to Dec 31, 2016. Cox models were used to identify breast cancer risk factors and build a relative risk model. Absolute risks were calculated by incorporating national age- and residence-specific breast cancer incidence and non-breast cancer mortality rates. We used an independent large prospective cohort, Shanghai Women's Health Study (SWHS), with 73,203 women to externally validate the calibration and discriminating accuracy. RESULTS During a median of 10.2 years of follow-up in the CKB, 2287 cases were observed. The final model included age, residence area, education, BMI, height, family history of overall cancer, parity, and age at menarche. The model was well-calibrated in both the CKB and the SWHS, yielding expected/observed (E/O) ratios of 1.01 (95% confidence interval (CI), 0.94-1.09) and 0.94 (95% CI, 0.89-0.99), respectively. After eliminating the effect of age and residence, the model maintained moderate but comparable discriminating accuracy compared with those of some previous externally validated models. The adjusted areas under the receiver operating curve (AUC) were 0.634 (95% CI, 0.608-0.661) and 0.585 (95% CI, 0.564-0.605) in the CKB and the SWHS, respectively. CONCLUSIONS Based only on non-laboratory predictors, our model has a good calibration and moderate discriminating capacity. The model may serve as a useful tool to raise individuals' awareness and aid risk-stratified screening and prevention strategies.
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Affiliation(s)
- Yuting Han
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, 38 Xueyuan Road, Beijing, 100191 China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, 38 Xueyuan Road, Beijing, 100191 China
- Key Laboratory of Molecular Cardiovascular Sciences (Peking University), Ministry of Education, Beijing, China
- Peking University Institute of Environmental Medicine, Beijing, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, 38 Xueyuan Road, Beijing, 100191 China
| | - Yu Guo
- Chinese Academy of Medical Sciences, Beijing, China
| | - Zheng Bian
- Chinese Academy of Medical Sciences, Beijing, China
| | - Yizhen Hu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, 38 Xueyuan Road, Beijing, 100191 China
| | - Ling Yang
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Yiping Chen
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Huaidong Du
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Fangyuan Zhao
- Department of Public Health Sciences, The University of Chicago, 5841 S. Maryland Ave., MC2000, Chicago, IL 60637 USA
| | - Wanqing Wen
- Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN USA
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN USA
| | - Yongbing Xiang
- State Key Laboratory of Oncogene and Related Genes & Department of Epidemiology, Shanghai Cancer Institute, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Yu-Tang Gao
- State Key Laboratory of Oncogene and Related Genes & Department of Epidemiology, Shanghai Cancer Institute, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN USA
| | - Hong Guo
- Medical department, Liuyang Hospital of Traditional Chinese Medicine, Liuyang, China
| | - Peng Liang
- People’s Hospital of Liuyang, Liuyang, China
| | - Junshi Chen
- China National Center for Food Safety Risk Assessment, Beijing, China
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Dezheng Huo
- Department of Public Health Sciences, The University of Chicago, 5841 S. Maryland Ave., MC2000, Chicago, IL 60637 USA
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, 38 Xueyuan Road, Beijing, 100191 China
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32
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Andrahennadi S, Sami A, Manna M, Pauls M, Ahmed S. Current Landscape of Targeted Therapy in Hormone Receptor-Positive and HER2-Negative Breast Cancer. Curr Oncol 2021; 28:1803-1822. [PMID: 34064867 PMCID: PMC8161804 DOI: 10.3390/curroncol28030168] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 04/21/2021] [Accepted: 05/06/2021] [Indexed: 02/06/2023] Open
Abstract
Background: Hormone receptor-positive and HER2-negative breast cancer (HR + BC) is the most prevalent breast cancer. Endocrine therapy is the mainstay of treatment, however, due to the heterogeneous nature of the disease, resistance to endocrine therapy is not uncommon. Over the past decades, the emergence of novel targeted therapy in combination with endocrine therapy has shown improvement in outcomes of HR + BC. This paper reviews available data of targeted therapy and the results of pivotal clinical trials in the management of HR + BC. Methods: A literature search in PubMed and Google Scholar was performed using keywords related to HR + BC and targeted therapy. Major relevant studies that were presented in international cancer research conferences were also included. Results: Endocrine therapy with tamoxifen and aromatase inhibitors are backbone treatments for women with early-stage HR + BC leading to a significant reduction in mortality. They can also be used for primary prevention in women with a high risk of breast cancer. Preliminary data has shown the efficacy of adjuvant cyclin-dependent kinase (CDK) 4/6 inhibitor, abemaciclib, in high-risk disease in combination with aromatase inhibitors. For most women with advanced HR + BC, endocrine therapy is the primary treatment. Recent evidence has shown that the use of CKD 4/6 inhibitors, mTOR inhibitors, and PI3K inhibitors in combination with endocrine therapy has been associated with better outcomes and delays initiation of chemotherapy. Several novel agents are under study for HR + BC. Discussion: Targeted treatment options for HR + BC have evolved. The future of overcoming resistance to targeted therapy, novel compounds, and predictive markers are key to improving HR + BC outcomes.
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Affiliation(s)
- Samitha Andrahennadi
- College of Medicine, University of Saskatchewan, Saskatoon, SK S7N 5E5, Canada; (S.A.); (A.S.); (M.M.); (M.P.)
| | - Amer Sami
- College of Medicine, University of Saskatchewan, Saskatoon, SK S7N 5E5, Canada; (S.A.); (A.S.); (M.M.); (M.P.)
- Saskatoon Cancer Center, Saskatchewan Cancer Agency, University of Saskatchewan, 20 Campus Drive, Saskatoon, SK S7N 4H4, Canada
| | - Mita Manna
- College of Medicine, University of Saskatchewan, Saskatoon, SK S7N 5E5, Canada; (S.A.); (A.S.); (M.M.); (M.P.)
- Saskatoon Cancer Center, Saskatchewan Cancer Agency, University of Saskatchewan, 20 Campus Drive, Saskatoon, SK S7N 4H4, Canada
| | - Mehrnoosh Pauls
- College of Medicine, University of Saskatchewan, Saskatoon, SK S7N 5E5, Canada; (S.A.); (A.S.); (M.M.); (M.P.)
- Saskatoon Cancer Center, Saskatchewan Cancer Agency, University of Saskatchewan, 20 Campus Drive, Saskatoon, SK S7N 4H4, Canada
| | - Shahid Ahmed
- College of Medicine, University of Saskatchewan, Saskatoon, SK S7N 5E5, Canada; (S.A.); (A.S.); (M.M.); (M.P.)
- Saskatoon Cancer Center, Saskatchewan Cancer Agency, University of Saskatchewan, 20 Campus Drive, Saskatoon, SK S7N 4H4, Canada
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Jantzen R, Payette Y, de Malliard T, Labbé C, Noisel N, Broët P. Validation of breast cancer risk assessment tools on a French-Canadian population-based cohort. BMJ Open 2021; 11:e045078. [PMID: 33846154 PMCID: PMC8047995 DOI: 10.1136/bmjopen-2020-045078] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
OBJECTIVES Evaluate the accuracy of the Breast Cancer Risk Assessment Tool (BCRAT), International Breast Cancer Intervention Study risk evaluation tool (IBIS), Polygenic Risk Scores (PRS) and combined scores (BCRAT+PRS and IBIS +PRS) to predict the occurrence of invasive breast cancers at 5 years in a French-Canadian population. DESIGN Population-based cohort study. SETTING We used the population-based cohort CARTaGENE, composed of 43 037 Quebec residents aged between 40 and 69 years and broadly representative of the population recorded on the Quebec administrative health insurance registries. PARTICIPANTS 10 200 women recruited in 2009-2010 were included for validating BCRAT and IBIS and 4555 with genetic information for validating the PRS and combined scores. OUTCOME MEASURES We computed the absolute risks of breast cancer at 5 years using BCRAT, IBIS, four published PRS and combined models. We reported the overall calibration performance, goodness-of-fit test and discriminatory accuracy. RESULTS 131 (1.28%) women developed a breast cancer at 5 years for validating BCRAT and IBIS and 58 (1.27%) for validating PRS and combined scores. Median follow-up was 5 years. BCRAT and IBIS had an overall expected-to-observed ratio of 1.01 (0.85-1.19) and 1.02 (0.86-1.21) but with significant differences when partitioning by risk groups (p<0.05). IBIS' c-index was significantly higher than BCRAT (63.42 (59.35-67.49) vs 58.63 (54.05-63.21), p=0.013). PRS scores had a global calibration around 0.82, with a CI including one, and non-significant goodness-of-fit tests. PRS' c-indexes were non-significantly higher than BCRAT and IBIS, the highest being 64.43 (58.23-70.63). Combined models did not improve the results. CONCLUSIONS In this French-Canadian population-based cohort, BCRAT and IBIS have good mean calibration that could be improved for risk subgroups, and modest discriminatory accuracy. Despite this modest discriminatory power, these tools can be of interest for primary care physicians for delivering a personalised message to their high-risk patients, regarding screening and lifestyle counselling.
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Affiliation(s)
- Rodolphe Jantzen
- CARTaGENE, Research Center, CHU Sainte-Justine, Montreal, Quebec, Canada
- Université de Montréal, Montréal, Québec, Canada
| | - Yves Payette
- CARTaGENE, Research Center, CHU Sainte-Justine, Montreal, Quebec, Canada
| | | | - Catherine Labbé
- CARTaGENE, Research Center, CHU Sainte-Justine, Montreal, Quebec, Canada
| | - Nolwenn Noisel
- CARTaGENE, Research Center, CHU Sainte-Justine, Montreal, Quebec, Canada
- Université de Montréal, Montréal, Québec, Canada
| | - Philippe Broët
- CARTaGENE, Research Center, CHU Sainte-Justine, Montreal, Quebec, Canada
- Université de Montréal, Montréal, Québec, Canada
- CESP, INSERM, University Paris-Saclay, Villejuif, France
- Assistance Publique-Hôpitaux de Paris (AP-HP), Hôpitaux Universitaires Paris-Sud, Hôpital Paul Brousse, Villejuif, France
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Rostami S, Rafei A, Damghanian M, Khakbazan Z, Maleki F, Zendehdel K. Discriminatory Accuracy of the Gail Model for Breast Cancer Risk Assessment among Iranian Women. IRANIAN JOURNAL OF PUBLIC HEALTH 2021; 49:2205-2213. [PMID: 33708742 PMCID: PMC7917489 DOI: 10.18502/ijph.v49i11.4739] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Background: The Gail model is the most well-known tool for breast cancer risk assessment worldwide. Although it was validated in various Western populations, inconsistent results were reported from Asian populations. We used data from a large case-control study and evaluated the discriminatory accuracy of the Gail model for breast cancer risk assessment among the Iranian female population. Methods: We used data from 942 breast cancer patients and 975 healthy controls at the Cancer Institute of Iran, Tehran, Iran, in 2016. We refitted the Gail model to our case-control data (the IR-Gail model). We compared the discriminatory power of the IR-Gail with the original Gail model, using ROC curve analyses and estimation of the area under the ROC curve (AUC). Results: Except for the history of biopsies that showed an extremely high relative risk (OR=9.1), the observed ORs were similar to the estimates observed in Gail’s study. Incidence rates of breast cancer were extremely lower in Iran than in the USA, leading to a lower average absolute risk among the Iranian population (2.78, ±SD 2.45). The AUC was significantly improved after refitting the model, but it remained modest (0.636 vs. 0.627, ΔAUC = 0.009, bootstrapped P=0.008). We reported that the cut-point of 1.67 suggested in the Gail study did not discriminate between breast cancer patients and controls among the Iranian female population. Conclusion: Although the coefficients from the local study improved the discriminatory accuracy of the model, it remained modest. Cohort studies are warranted to evaluate the validity of the model for Iranian women.
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Affiliation(s)
- Sahar Rostami
- Department of Reproductive Health and Midwifery, School of Nursing and Midwifery, Tehran University of Medical Sciences, Tehran, Iran.,Cancer Research Center, Cancer Institute of Iran, Tehran University of Medical Sciences, Tehran, Iran
| | - Ali Rafei
- Cancer Research Center, Cancer Institute of Iran, Tehran University of Medical Sciences, Tehran, Iran
| | - Maryam Damghanian
- Nursing and Midwifery Care Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Zohreh Khakbazan
- Nursing and Midwifery Care Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Farzad Maleki
- Cancer Research Center, Cancer Institute of Iran, Tehran University of Medical Sciences, Tehran, Iran.,Social Determinants of Health Research Center, Urmia University of Medical Sciences, Urmia, Iran
| | - Kazem Zendehdel
- Cancer Research Center, Cancer Institute of Iran, Tehran University of Medical Sciences, Tehran, Iran.,Cancer Biology Research Center, Cancer Institute of Iran, Tehran University of Medical Sciences, Tehran, Iran.,Breast Disease Research Center, Cancer Institute of Iran, Tehran University of Medical Sciences, Tehran, Iran
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McCarthy AM, Guan Z, Welch M, Griffin ME, Sippo DA, Deng Z, Coopey SB, Acar A, Semine A, Parmigiani G, Braun D, Hughes KS. Performance of Breast Cancer Risk-Assessment Models in a Large Mammography Cohort. J Natl Cancer Inst 2021; 112:489-497. [PMID: 31556450 DOI: 10.1093/jnci/djz177] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2019] [Revised: 07/23/2019] [Accepted: 09/04/2019] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Several breast cancer risk-assessment models exist. Few studies have evaluated predictive accuracy of multiple models in large screening populations. METHODS We evaluated the performance of the BRCAPRO, Gail, Claus, Breast Cancer Surveillance Consortium (BCSC), and Tyrer-Cuzick models in predicting risk of breast cancer over 6 years among 35 921 women aged 40-84 years who underwent mammography screening at Newton-Wellesley Hospital from 2007 to 2009. We assessed model discrimination using the area under the receiver operating characteristic curve (AUC) and assessed calibration by comparing the ratio of observed-to-expected (O/E) cases. We calculated the square root of the Brier score and positive and negative predictive values of each model. RESULTS Our results confirmed the good calibration and comparable moderate discrimination of the BRCAPRO, Gail, Tyrer-Cuzick, and BCSC models. The Gail model had slightly better O/E ratio and AUC (O/E = 0.98, 95% confidence interval [CI] = 0.91 to 1.06, AUC = 0.64, 95% CI = 0.61 to 0.65) compared with BRCAPRO (O/E = 0.94, 95% CI = 0.88 to 1.02, AUC = 0.61, 95% CI = 0.59 to 0.63) and Tyrer-Cuzick (version 8, O/E = 0.84, 95% CI = 0.79 to 0.91, AUC = 0.62, 95% 0.60 to 0.64) in the full study population, and the BCSC model had the highest AUC among women with available breast density information (O/E = 0.97, 95% CI = 0.89 to 1.05, AUC = 0.64, 95% CI = 0.62 to 0.66). All models had poorer predictive accuracy for human epidermal growth factor receptor 2 positive and triple-negative breast cancers than hormone receptor positive human epidermal growth factor receptor 2 negative breast cancers. CONCLUSIONS In a large cohort of patients undergoing mammography screening, existing risk prediction models had similar, moderate predictive accuracy and good calibration overall. Models that incorporate additional genetic and nongenetic risk factors and estimate risk of tumor subtypes may further improve breast cancer risk prediction.
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Affiliation(s)
- Anne Marie McCarthy
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Zoe Guan
- Department of Biostatistics, Harvard University T H Chan School of Public Health, Boston, MA.,Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA
| | - Michaela Welch
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA
| | - Molly E Griffin
- Division of Surgical Oncology, Massachusetts General Hospital, Boston, MA
| | - Dorothy A Sippo
- Department of Radiology, Massachusetts General Hospital, Boston, MA
| | - Zhengyi Deng
- Division of Surgical Oncology, Massachusetts General Hospital, Boston, MA
| | - Suzanne B Coopey
- Division of Surgical Oncology, Massachusetts General Hospital, Boston, MA
| | - Ahmet Acar
- Istanbul School of Medicine, Istanbul University, Istanbul, Turkey
| | - Alan Semine
- Department of Radiology, Newton-Wellesley Hospital, Newton, MA
| | - Giovanni Parmigiani
- Department of Biostatistics, Harvard University T H Chan School of Public Health, Boston, MA.,Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA
| | - Danielle Braun
- Department of Biostatistics, Harvard University T H Chan School of Public Health, Boston, MA.,Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA
| | - Kevin S Hughes
- Division of Surgical Oncology, Massachusetts General Hospital, Boston, MA
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Martín-Payo R, Ferreras-Losilla L, González-Méndez X, Leirós-Díaz C, Martínez-Urquijo A, Fernández-Álvarez MDM. Apps for individuals diagnosed with breast cancer: a preliminary assessment of the content and quality of commercially available apps in Spanish. Mhealth 2021; 7:2. [PMID: 33634185 PMCID: PMC7882267 DOI: 10.21037/mhealth-19-191] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Accepted: 05/22/2020] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND many apps are directly related to health issues. Recent studies show that apps are widely accepted by the population and contribute to the modernization of the healthcare system. However, before recommendation, their contents and quality should be assessed, as well as the behavioral change techniques they include. In Spain, no study has been found to determine which apps are aimed at addressing any aspect of breast cancer. The objective of this study was to identify and describe the contents and analyze the quality and behavior change strategies of the free applications available in the online stores of Android and Apple whose main purpose is related to some aspect of breast cancer. METHODS Searches were conducted in the Apple App and Google Play stores in Spain, between October 2018 and February 2019, using an Apple iPad Pro and a Samsung Galaxy Tab A6. The Spanish search terms used were: "cáncer de mama" [breast cancer], "cáncer de pecho" [breast cancer], "cáncer de seno" [breast cancer], "tumor de mama" [breast tumor], "tumor de pecho" [breast tumor], "tumor de seno" [breast tumor], "neoplasia de mama" [breast neoplasm], "neoplasia de pecho" [breast neoplasm], and "neoplasia de seno" [breast neoplasm]. After screening, contents related to breast cancer, quality, and behavioral change were assessed. RESULTS The contents of the 6 selected apps were related to breast self-examination and to the signs and symptoms that may warn the woman of the presence of a breast tumor. The MARS objective and subjective quality scores were 4.11 (SD =0.59) and 3.07 (SD =0.91), respectively. The mean number of BCTs included in the apps was 2.83 (SD =3.040). The app with the highest number of BCTs was APP1, with a total of 9 techniques. CONCLUSIONS Few free apps are specifically designed for breast cancer in Spanish. Their content and quality, as well as the number of BCTs they include, should be improved.
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Affiliation(s)
- Rubén Martín-Payo
- Faculty of Medicine & Health Sciences, University of Oviedo, Asturias, Spain
- Precam Research Group, ISPA, Health Research Institute of Principado de Asturias, Spain
| | | | - Xana González-Méndez
- Faculty of Medicine & Health Sciences, University of Oviedo, Asturias, Spain
- Precam Research Group, ISPA, Health Research Institute of Principado de Asturias, Spain
- SESPA Public Health Service of the Principality of Asturias, Spain
| | - Claudia Leirós-Díaz
- Precam Research Group, ISPA, Health Research Institute of Principado de Asturias, Spain
- SESPA Public Health Service of the Principality of Asturias, Spain
| | - Andrea Martínez-Urquijo
- Faculty of Medicine & Health Sciences, University of Oviedo, Asturias, Spain
- Precam Research Group, ISPA, Health Research Institute of Principado de Asturias, Spain
| | - Maria del Mar Fernández-Álvarez
- Faculty of Medicine & Health Sciences, University of Oviedo, Asturias, Spain
- Precam Research Group, ISPA, Health Research Institute of Principado de Asturias, Spain
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An Update on Screening and Prevention for Breast and Gynecological Cancers in Average and High Risk Individuals. Am J Med Sci 2020; 360:489-510. [DOI: 10.1016/j.amjms.2020.06.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Revised: 05/22/2020] [Accepted: 06/03/2020] [Indexed: 11/21/2022]
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Choudhury A, Renjilian E, Asan O. Use of machine learning in geriatric clinical care for chronic diseases: a systematic literature review. JAMIA Open 2020; 3:459-471. [PMID: 33215079 PMCID: PMC7660963 DOI: 10.1093/jamiaopen/ooaa034] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 06/26/2020] [Accepted: 07/11/2020] [Indexed: 12/13/2022] Open
Abstract
Objectives Geriatric clinical care is a multidisciplinary assessment designed to evaluate older patients’ (age 65 years and above) functional ability, physical health, and cognitive well-being. The majority of these patients suffer from multiple chronic conditions and require special attention. Recently, hospitals utilize various artificial intelligence (AI) systems to improve care for elderly patients. The purpose of this systematic literature review is to understand the current use of AI systems, particularly machine learning (ML), in geriatric clinical care for chronic diseases. Materials and Methods We restricted our search to eight databases, namely PubMed, WorldCat, MEDLINE, ProQuest, ScienceDirect, SpringerLink, Wiley, and ERIC, to analyze research articles published in English between January 2010 and June 2019. We focused on studies that used ML algorithms in the care of geriatrics patients with chronic conditions. Results We identified 35 eligible studies and classified in three groups: psychological disorder (n = 22), eye diseases (n = 6), and others (n = 7). This review identified the lack of standardized ML evaluation metrics and the need for data governance specific to health care applications. Conclusion More studies and ML standardization tailored to health care applications are required to confirm whether ML could aid in improving geriatric clinical care.
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Affiliation(s)
- Avishek Choudhury
- School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, New Jersey, USA
| | - Emily Renjilian
- School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, New Jersey, USA
| | - Onur Asan
- School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, New Jersey, USA
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Kim DY, Park HL. Breast Cancer Risk Prediction in Korean Women: Review and Perspectives on Personalized Breast Cancer Screening. J Breast Cancer 2020; 23:331-342. [PMID: 32908785 PMCID: PMC7462811 DOI: 10.4048/jbc.2020.23.e40] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Accepted: 06/22/2020] [Indexed: 01/20/2023] Open
Abstract
Due to an increasing proportion of older individuals and the adoption of a westernized lifestyle, the incidence rate of breast cancer is expected to rapidly increase within the next 10 years in Korea. The National Cancer Screening Program (NCSP) of Korea recommends biennial breast cancer screening through mammography for women aged 40-69 years old and according to individual risk and preference for women above 70 years old. There is an ongoing debate on how to most effectively screen for breast cancer, with many proponents of personalized screening, or screening according to individual risk, for women under 70 years old as well. However, to accurately stratify women into risk categories, further study using more refined personalized characteristics, including potentially incorporating a polygenic risk score (PRS), may be needed. While most breast cancer risk prediction models were developed in Western countries, the Korean Breast Cancer Risk Assessment Tool (KoBCRAT) was developed in 2013, and several other risk models have been developed for Asian women specifically. This paper reviews these models compared to commonly used models developed using primarily Caucasian women, namely, the modified Gail, Breast Cancer Surveillance Consortium, Rosner and Colditz, and Tyrer-Cuzick models. In addition, this paper reviews studies in which PRS is included in risk prediction in Asian women. Finally, this paper discusses and explores strategies toward development and implementation of personalized screening for breast cancer in Korea.
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Affiliation(s)
- Do Yeun Kim
- Division of Medical Oncology, Department of Internal Medicine, Dongguk University Ilsan Hospital, Goyang, Korea
| | - Hannah Lui Park
- Department of Epidemiology, School of Medicine, University of California, Irvine, CA, USA
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Behravan H, Hartikainen JM, Tengström M, Kosma VM, Mannermaa A. Predicting breast cancer risk using interacting genetic and demographic factors and machine learning. Sci Rep 2020; 10:11044. [PMID: 32632202 PMCID: PMC7338351 DOI: 10.1038/s41598-020-66907-9] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2020] [Accepted: 06/01/2020] [Indexed: 12/21/2022] Open
Abstract
Breast cancer (BC) is a multifactorial disease and the most common cancer in women worldwide. We describe a machine learning approach to identify a combination of interacting genetic variants (SNPs) and demographic risk factors for BC, especially factors related to both familial history (Group 1) and oestrogen metabolism (Group 2), for predicting BC risk. This approach identifies the best combinations of interacting genetic and demographic risk factors that yield the highest BC risk prediction accuracy. In tests on the Kuopio Breast Cancer Project (KBCP) dataset, our approach achieves a mean average precision (mAP) of 77.78 in predicting BC risk by using interacting genetic and Group 1 features, which is better than the mAPs of 74.19 and 73.65 achieved using only Group 1 features and interacting SNPs, respectively. Similarly, using interacting genetic and Group 2 features yields a mAP of 78.00, which outperforms the system based on only Group 2 features, which has a mAP of 72.57. Furthermore, the gene interaction maps built from genes associated with SNPs that interact with demographic risk factors indicate important BC-related biological entities, such as angiogenesis, apoptosis and oestrogen-related networks. The results also show that demographic risk factors are individually more important than genetic variants in predicting BC risk.
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Affiliation(s)
- Hamid Behravan
- Institute of Clinical Medicine, Pathology and Forensic Medicine, and Translational Cancer Research Area, University of Eastern Finland, P.O. Box 1627, FI-70211, Kuopio, Finland.
| | - Jaana M Hartikainen
- Institute of Clinical Medicine, Pathology and Forensic Medicine, and Translational Cancer Research Area, University of Eastern Finland, P.O. Box 1627, FI-70211, Kuopio, Finland
| | - Maria Tengström
- Institute of Clinical Medicine, Oncology, University of Eastern Finland, P.O. Box 1627, FI-70211, Kuopio, Finland
- Cancer Center, Kuopio University Hospital, Kuopio, P.O. Box 100, FI-70029, Kuopio, Finland
| | - Veli-Matti Kosma
- Institute of Clinical Medicine, Pathology and Forensic Medicine, and Translational Cancer Research Area, University of Eastern Finland, P.O. Box 1627, FI-70211, Kuopio, Finland
- Biobank of Eastern Finland, Kuopio University Hospital, Kuopio, Finland
| | - Arto Mannermaa
- Institute of Clinical Medicine, Pathology and Forensic Medicine, and Translational Cancer Research Area, University of Eastern Finland, P.O. Box 1627, FI-70211, Kuopio, Finland
- Biobank of Eastern Finland, Kuopio University Hospital, Kuopio, Finland
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Machine learning-based lifetime breast cancer risk reclassification compared with the BOADICEA model: impact on screening recommendations. Br J Cancer 2020; 123:860-867. [PMID: 32565540 PMCID: PMC7463251 DOI: 10.1038/s41416-020-0937-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Revised: 05/13/2020] [Accepted: 05/29/2020] [Indexed: 12/17/2022] Open
Abstract
Background The clinical utility of machine-learning (ML) algorithms for breast cancer risk prediction and screening practices is unknown. We compared classification of lifetime breast cancer risk based on ML and the BOADICEA model. We explored the differences in risk classification and their clinical impact on screening practices. Methods We used three different ML algorithms and the BOADICEA model to estimate lifetime breast cancer risk in a sample of 112,587 individuals from 2481 families from the Oncogenetic Unit, Geneva University Hospitals. Performance of algorithms was evaluated using the area under the receiver operating characteristic (AU-ROC) curve. Risk reclassification was compared for 36,146 breast cancer-free women of ages 20–80. The impact on recommendations for mammography surveillance was based on the Swiss Surveillance Protocol. Results The predictive accuracy of ML-based algorithms (0.843 ≤ AU-ROC ≤ 0.889) was superior to BOADICEA (AU-ROC = 0.639) and reclassified 35.3% of women in different risk categories. The largest reclassification (20.8%) was observed in women characterised as ‘near population’ risk by BOADICEA. Reclassification had the largest impact on screening practices of women younger than 50. Conclusion ML-based reclassification of lifetime breast cancer risk occurred in approximately one in three women. Reclassification is important for younger women because it impacts clinical decision- making for the initiation of screening.
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Hou C, Zhong X, He P, Xu B, Diao S, Yi F, Zheng H, Li J. Predicting Breast Cancer in Chinese Women Using Machine Learning Techniques: Algorithm Development. JMIR Med Inform 2020; 8:e17364. [PMID: 32510459 PMCID: PMC7308891 DOI: 10.2196/17364] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Revised: 03/28/2020] [Accepted: 04/19/2020] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Risk-based breast cancer screening is a cost-effective intervention for controlling breast cancer in China, but the successful implementation of such intervention requires an accurate breast cancer prediction model for Chinese women. OBJECTIVE This study aimed to evaluate and compare the performance of four machine learning algorithms on predicting breast cancer among Chinese women using 10 breast cancer risk factors. METHODS A dataset consisting of 7127 breast cancer cases and 7127 matched healthy controls was used for model training and testing. We used repeated 5-fold cross-validation and calculated AUC, sensitivity, specificity, and accuracy as the measures of the model performance. RESULTS The three novel machine-learning algorithms (XGBoost, Random Forest and Deep Neural Network) all achieved significantly higher area under the receiver operating characteristic curves (AUCs), sensitivity, and accuracy than logistic regression. Among the three novel machine learning algorithms, XGBoost (AUC 0.742) outperformed deep neural network (AUC 0.728) and random forest (AUC 0.728). Main residence, number of live births, menopause status, age, and age at first birth were considered as top-ranked variables in the three novel machine learning algorithms. CONCLUSIONS The novel machine learning algorithms, especially XGBoost, can be used to develop breast cancer prediction models to help identify women at high risk for breast cancer in developing countries.
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Affiliation(s)
- Can Hou
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Xiaorong Zhong
- Department of Head, Neck and Mammary Gland Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.,Laboratory of Molecular Diagnosis of Cancer, Clinical Research Center for Breast, West China Hospital, Sichuan University, Chengdu, China
| | - Ping He
- Department of Head, Neck and Mammary Gland Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.,Laboratory of Molecular Diagnosis of Cancer, Clinical Research Center for Breast, West China Hospital, Sichuan University, Chengdu, China
| | - Bin Xu
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Sha Diao
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Fang Yi
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Hong Zheng
- Department of Head, Neck and Mammary Gland Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.,Laboratory of Molecular Diagnosis of Cancer, Clinical Research Center for Breast, West China Hospital, Sichuan University, Chengdu, China
| | - Jiayuan Li
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
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McClintock AH, Golob AL, Laya MB. Breast Cancer Risk Assessment: A Step-Wise Approach for Primary Care Providers on the Front Lines of Shared Decision Making. Mayo Clin Proc 2020; 95:1268-1275. [PMID: 32498779 DOI: 10.1016/j.mayocp.2020.04.017] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Revised: 03/23/2020] [Accepted: 04/01/2020] [Indexed: 12/31/2022]
Abstract
Breast cancer-screening guidelines increasingly recommend that clinicians perform a risk assessment for breast cancer to inform shared decision making for screening. Precision medicine is quickly becoming the preferred approach to cancer screening, with the aim of increased surveillance in high-risk women, while sparing lower-risk women the burden of unnecessary imaging. Risk assessment also informs clinical care by refining screening recommendations for younger women, identifying women who should be referred to genetic counseling, and identifying candidates for risk-reducing medications. Several breast cancer risk-assessment models are currently available to help clinicians categorize a woman's risk for breast cancer. However, choosing the appropriate model for a given patient requires a working knowledge of the strengths, weaknesses, and performance characteristics of each. The aim of this article is to provide a stepwise approach for clinicians to assess an individual woman's risk for breast cancer and describe the features, appropriate use, and performance characteristics of commonly encountered risk-prediction models. This approach will help primary care providers engage in shared decision making by efficiently generating an accurate risk assessment and make clear, evidence-based screening and prevention recommendations that are appropriately matched to a woman's risk for breast cancer.
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Affiliation(s)
- Adelaide H McClintock
- Division of General Internal Medicine, Department of Medicine, University of Washington, Seattle, Washington; Women's Health Care Center, Seattle, Washington.
| | - Anna L Golob
- Division of General Internal Medicine, Department of Medicine, University of Washington, Seattle, Washington; Seattle Veterans Affairs Medical Center, Seattle, Washington
| | - Mary B Laya
- Division of General Internal Medicine, Department of Medicine, University of Washington, Seattle, Washington; Women's Health Care Center, Seattle, Washington
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Bharucha PP, Chiu KE, François FM, Scott JL, Khorjekar GR, Tirada NP. Genetic Testing and Screening Recommendations for Patients with Hereditary Breast Cancer. Radiographics 2020; 40:913-936. [PMID: 32469631 DOI: 10.1148/rg.2020190181] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Professionals who specialize in breast imaging may be the first to initiate the conversation about genetic counseling with patients who have a diagnosis of premenopausal breast cancer or a strong family history of breast and ovarian cancer. Commercial genetic testing panels have gained popularity and have become more affordable in recent years. Therefore, it is imperative for radiologists to be able to provide counseling and to identify those patients who should be referred for genetic testing. The authors review the process of genetic counseling and the associated screening recommendations for patients at high and moderate risk. Ultimately, genetic test results enable appropriate patient-specific screening, which allows improvement of overall survival by early detection and timely treatment. The authors discuss pretest counseling, which involves the use of various breast cancer risk assessment tools such as the Gail and Tyrer-Cuzick models. The most common high- and moderate-risk gene mutations associated with breast cancer are also reviewed. In addition to BRCA1 and BRCA2, several high-risk genes, including TP53, PTEN, CDH1, and STK11, are discussed. Moderate-risk genes include ATM, CHEK2, and PALB2. The imaging appearances of breast cancer typically associated with each gene mutation, as well as the other associated cancers, are described. ©RSNA, 2020 See discussion on this article by Butler (pp 937-940).
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Affiliation(s)
- Puja P Bharucha
- From the Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, 22 S Greene St, Baltimore, MD 21201
| | - Kellie E Chiu
- From the Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, 22 S Greene St, Baltimore, MD 21201
| | - Fabienne M François
- From the Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, 22 S Greene St, Baltimore, MD 21201
| | - Jessica L Scott
- From the Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, 22 S Greene St, Baltimore, MD 21201
| | - Gauri R Khorjekar
- From the Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, 22 S Greene St, Baltimore, MD 21201
| | - Nikki P Tirada
- From the Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, 22 S Greene St, Baltimore, MD 21201
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Tanner LTA, Cheung KL. Correlation between breast cancer and lifestyle within the Gulf Cooperation Council countries: A systematic review. World J Clin Oncol 2020; 11:217-242. [PMID: 32355643 PMCID: PMC7186238 DOI: 10.5306/wjco.v11.i4.217] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 03/12/2020] [Accepted: 03/26/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND In the six Gulf Cooperation Council countries (GCCCs), Bahrain, Saudi Arabia, Kuwait, Oman, Qatar and the United Arab Emirates, breast cancer (BC) is the greatest cause of cancer incidence and mortality. Obesity and physical inactivity are established risk factors for BC globally and appear to be more of a problem in high income countries like the GCCCs.
AIM To determine whether obesity and physical inactivity are associated with BC incidence in the GCCCs using the United Kingdom as a comparator.
METHODS This systematic review was carried out according to PRISMA guidelines. A cancer registry and a statistical data search was done to identify the BC incidence over the past two decades and the prevalence of obesity and physical inactivity in the GCCCs. Additionally, a systematic search of the databases, MEDLINE, Web of Science, and PubMed between 1999 and 2019 was performed to determine whether obesity and physical inactivity are risk factors for BC in the GCCCs. All papers were critically appraised according to their research methods and were assessed for quality and risk of bias.
RESULTS BC was the top malignancy in each GCC country. Women tended to be diagnosed with BC at a younger age than women in the United Kingdom. The greatest 10-year increase in BC incidence was seen in Saudi Arabia (54.2%), approximately seven times the rate of increase seen in the United Kingdom (7.6%). The prevalence of obesity and physical inactivity was greater in all the GCCCs in comparison to the United Kingdom. A total of 155 full studies were reviewed of which 17 were included. Of those, eight looked at the prevalence of obesity and physical inactivity in the Gulf States and nine looked at these as risk factors for BC. Only one study found an association between BC and obesity (odds ratio = 2.29). No studies looked solely at the link between physical inactivity and BC.
CONCLUSION The prevalence of obesity and physical inactivity was high within the GCCCs, but the majority of the included studies found no positive correlation between obesity or physical inactivity and BC. A high proportion of women in this study were pre-menopausal which could contribute to the negative findings.
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Affiliation(s)
| | - Kwok Leung Cheung
- School of Medicine, University of Nottingham, Derby DE22 3DT, United Kingdom
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Ming C, Viassolo V, Probst-Hensch N, Chappuis PO, Dinov ID, Katapodi MC. Letter to the editor: Response to Giardiello D, Antoniou AC, Mariani L, Easton DF, Steyerberg EW. Breast Cancer Res 2020; 22:35. [PMID: 32276659 PMCID: PMC7146948 DOI: 10.1186/s13058-020-01274-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Accepted: 04/02/2020] [Indexed: 12/12/2022] Open
Affiliation(s)
- Chang Ming
- Department of Clinical Research, Faculty of Medicine, University of Basel, Missionstrasse 64, 2 OG - Room 007, 4055, Basel, Switzerland.
| | - Valeria Viassolo
- Oncogenetics and Cancer Prevention, Geneva University Hospitals, Geneva, Switzerland
| | - Nicole Probst-Hensch
- Swiss Tropical and Public Health Institute, University of Basel, Basel, Switzerland
| | - Pierre O Chappuis
- Oncogenetics and Cancer Prevention, Geneva University Hospitals, Geneva, Switzerland.,Genetic Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Ivo D Dinov
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.,Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI, USA.,Statistics Online Computational resource, University of Michigan, Ann Arbor, MI, USA.,University of Michigan School of Nursing, Ann Arbor, MI, USA
| | - Maria C Katapodi
- Department of Clinical Research, Faculty of Medicine, University of Basel, Missionstrasse 64, 2 OG - Room 007, 4055, Basel, Switzerland.,University of Michigan School of Nursing, Ann Arbor, MI, USA
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Meehan AJ, Latham RM, Arseneault L, Stahl D, Fisher HL, Danese A. Developing an individualized risk calculator for psychopathology among young people victimized during childhood: A population-representative cohort study. J Affect Disord 2020; 262:90-98. [PMID: 31715391 PMCID: PMC6916410 DOI: 10.1016/j.jad.2019.10.034] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2019] [Revised: 09/23/2019] [Accepted: 10/25/2019] [Indexed: 12/20/2022]
Abstract
BACKGROUND Victimized children are at greater risk for psychopathology than non-victimized peers. However, not all victimized children develop psychiatric disorders, and accurately identifying which victimized children are at greatest risk for psychopathology is important to provide targeted interventions. This study sought to develop and internally validate individualized risk prediction models for psychopathology among victimized children. METHODS Participants were members of the Environmental Risk (E-Risk) Longitudinal Twin Study, a nationally-representative British birth cohort of 2,232 twins born in 1994-1995. Victimization exposure was measured prospectively between ages 5 and 12 years, alongside a comprehensive range of individual-, family-, and community-level predictors of psychopathology. Structured psychiatric interviews took place at age-18 assessment. Logistic regression models were estimated with Least Absolute Shrinkage and Selection Operator (LASSO) regularization to avoid over-fitting to the current sample, and internally validated using 10-fold nested cross-validation. RESULTS 26.5% (n = 591) of E-Risk participants had been exposed to at least one form of severe childhood victimization, and 60.4% (n = 334) of victimized children met diagnostic criteria for any psychiatric disorder at age 18. Separate prediction models for any psychiatric disorder, internalizing disorders, and externalizing disorders selected parsimonious subsets of predictors. The three internally validated models showed adequate discrimination, based on area-under-the-curve estimates (range = =0.66-0.73), and good calibration. LIMITATIONS External validation in wholly-independent data is needed before clinical implementation. CONCLUSIONS Findings offer proof-of-principle evidence that prediction modeling can be useful in supporting identification of victimized children at greatest risk for psychopathology. This has the potential to inform targeted interventions and rational resource allocation.
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Affiliation(s)
- Alan J. Meehan
- Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Rachel M. Latham
- Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Louise Arseneault
- Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Daniel Stahl
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Helen L. Fisher
- Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Andrea Danese
- Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK; Department of Child & Adolescent Psychiatry, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK; National and Specialist CAMHS Trauma, Anxiety, and Depression Clinic, South London and Maudsley NHS Foundation Trust, London, UK.
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48
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Breast cancer risk assessment and early diagnosis using Principal Component Analysis and support vector machine techniques. INFORMATICS IN MEDICINE UNLOCKED 2020. [DOI: 10.1016/j.imu.2020.100459] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
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Latham RM, Meehan AJ, Arseneault L, Stahl D, Danese A, Fisher HL. Development of an individualized risk calculator for poor functioning in young people victimized during childhood: A longitudinal cohort study. CHILD ABUSE & NEGLECT 2019; 98:104188. [PMID: 31563702 PMCID: PMC6905153 DOI: 10.1016/j.chiabu.2019.104188] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Revised: 07/24/2019] [Accepted: 09/10/2019] [Indexed: 05/18/2023]
Abstract
BACKGROUND Childhood victimization elevates the average risk of developing functional impairment in adulthood. However, not all victimized children demonstrate poor outcomes. Although research has described factors that confer vulnerability or resilience, it is unknown if this knowledge can be translated to accurately identify the most vulnerable victimized children. OBJECTIVE To build and internally validate a risk calculator to identify those victimized children who are most at risk of functional impairment at age 18 years. PARTICIPANTS We utilized data from the Environmental Risk (E-Risk) Longitudinal Twin Study, a nationally-representative birth cohort of 2232 UK children born in 1994-95. METHODS Victimization exposure was assessed repeatedly between ages 5 and 12 years along with a range of individual-, family- and community-level predictors. Functional outcomes were assessed at age 18 years. We developed and evaluated a prediction model for psychosocial disadvantage and economic disadvantage using the Least Absolute Shrinkage and Selection Operator (LASSO) regularized regression with nested 10-fold cross-validation. RESULTS The model predicting psychosocial disadvantage following childhood victimization retained 12 of 22 predictors, had an area under the curve (AUC) of 0.65, and was well-calibrated within the range of 40-70% predicted risk. The model predicting economic disadvantage retained 10 of 22 predictors, achieved excellent discrimination (AUC = 0.80), and a high degree of calibration. CONCLUSIONS Prediction modelling techniques can be applied to estimate individual risk for poor functional outcomes in young adulthood following childhood victimization. Such risk prediction tools could potentially assist practitioners to target interventions, which is particularly useful in a context of scarce resources.
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Affiliation(s)
- Rachel M Latham
- King's College London, Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, London, UK
| | - Alan J Meehan
- King's College London, Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, London, UK
| | - Louise Arseneault
- King's College London, Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, London, UK
| | - Daniel Stahl
- King's College London, Department of Biostatistics, Institute of Psychiatry, Psychology & Neuroscience, London, UK
| | - Andrea Danese
- King's College London, Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, London, UK; King's College London, Department of Child & Adolescent Psychiatry, Institute of Psychiatry, Psychology & Neuroscience, London, UK; National and Specialist CAMHS Trauma, Anxiety, and Depression Clinic, South London and Maudsley NHS Foundation Trust, London, UK
| | - Helen L Fisher
- King's College London, Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, London, UK.
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Thorat MA, Balasubramanian R. Breast cancer prevention in high-risk women. Best Pract Res Clin Obstet Gynaecol 2019; 65:18-31. [PMID: 31862315 DOI: 10.1016/j.bpobgyn.2019.11.006] [Citation(s) in RCA: 80] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 11/10/2019] [Accepted: 11/11/2019] [Indexed: 12/24/2022]
Abstract
Women at high risk of developing breast cancer are a heterogeneous group of women including those with and without high-risk germline mutation/s. Prevention in these women requires a personalised and multidisciplinary approach. Preventive therapy with selective oestrogen receptor modulators (SERMs) like tamoxifen and aromatase inhibitors (AIs) substantially reduces breast cancer risk well beyond the active treatment period. The importance of benign breast disease as a marker of increased breast cancer risk remains underappreciated, and although the benefit of preventive therapy may be greater in such women, preventive therapy remains underutilised in these and other high-risk women. Bilateral Risk-Reducing Mastectomy (BRRM) reduces the risk of developing breast cancer by 90% in high-risk women such as carriers of BRCA mutations. It also improves breast cancer-specific survival in BRCA1 carriers. Bilateral risk-reducing salpingo-oophorectomy may also reduce risk in premenopausal BRCA2 carriers. Further research to improve risk models, to identify surrogate biomarkers of preventive therapy benefit and to develop newer preventive agents is needed.
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Affiliation(s)
- Mangesh A Thorat
- Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Queen Mary University of London, United Kingdom; School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences & Medicine, King's College London, United Kingdom; Breast Services, Guy's Hospital, Great Maze Pond, London, SE1 9RT, United Kingdom.
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