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Hubbard RA, Su YR, Bowles EJA, Ichikawa L, Kerlikowske K, Lowry KP, Miglioretti DL, Tosteson ANA, Wernli KJ, Lee JM. Predicting five-year interval second breast cancer risk in women with prior breast cancer. J Natl Cancer Inst 2024; 116:929-937. [PMID: 38466940 PMCID: PMC11160498 DOI: 10.1093/jnci/djae063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 02/22/2024] [Accepted: 03/07/2024] [Indexed: 03/13/2024] Open
Abstract
BACKGROUND Annual surveillance mammography is recommended for women with a personal history of breast cancer. Risk prediction models that estimate mammography failures such as interval second breast cancers could help to tailor surveillance imaging regimens to women's individual risk profiles. METHODS In a cohort of women with a history of breast cancer receiving surveillance mammography in the Breast Cancer Surveillance Consortium in 1996-2019, we used Least Absolute Shrinkage and Selection Operator (LASSO)-penalized regression to estimate the probability of an interval second cancer (invasive cancer or ductal carcinoma in situ) in the 1 year after a negative surveillance mammogram. Based on predicted risks from this one-year risk model, we generated cumulative risks of an interval second cancer for the five-year period after each mammogram. Model performance was evaluated using cross-validation in the overall cohort and within race and ethnicity strata. RESULTS In 173 290 surveillance mammograms, we observed 496 interval cancers. One-year risk models were well-calibrated (expected/observed ratio = 1.00) with good accuracy (area under the receiver operating characteristic curve = 0.64). Model performance was similar across race and ethnicity groups. The median five-year cumulative risk was 1.20% (interquartile range 0.93%-1.63%). Median five-year risks were highest in women who were under age 40 or pre- or perimenopausal at diagnosis and those with estrogen receptor-negative primary breast cancers. CONCLUSIONS Our risk model identified women at high risk of interval second breast cancers who may benefit from additional surveillance imaging modalities. Risk models should be evaluated to determine if risk-guided supplemental surveillance imaging improves early detection and decreases surveillance failures.
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Affiliation(s)
- Rebecca A Hubbard
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Yu-Ru Su
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA, USA
| | - Erin J A Bowles
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA, USA
| | - Laura Ichikawa
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA, USA
| | - Karla Kerlikowske
- Departments of Medicine and Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
- General Internal Medicine Section, Department of Veterans Affairs, University of California, San Francisco, CA, USA
| | - Kathryn P Lowry
- Department of Radiology, University of Washington and Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Diana L Miglioretti
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA, USA
- Division of Biostatistics, Department of Public Health Sciences, University of California Davis, Davis, CA, USA
| | - Anna N A Tosteson
- The Dartmouth Institute for Health Policy and Clinical Practice and Dartmouth Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Karen J Wernli
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA, USA
| | - Janie M Lee
- Department of Radiology, University of Washington and Fred Hutchinson Cancer Center, Seattle, WA, USA
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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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Breast Cancer Risk Assessment Tools for Stratifying Women into Risk Groups: A Systematic Review. Cancers (Basel) 2023; 15:cancers15041124. [PMID: 36831466 PMCID: PMC9953796 DOI: 10.3390/cancers15041124] [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: 11/01/2022] [Revised: 01/31/2023] [Accepted: 02/01/2023] [Indexed: 02/12/2023] Open
Abstract
BACKGROUND The benefits and harms of breast screening may be better balanced through a risk-stratified approach. We conducted a systematic review assessing the accuracy of questionnaire-based risk assessment tools for this purpose. METHODS Population: asymptomatic women aged ≥40 years; Intervention: questionnaire-based risk assessment tool (incorporating breast density and polygenic risk where available); Comparison: different tool applied to the same population; Primary outcome: breast cancer incidence; Scope: external validation studies identified from databases including Medline and Embase (period 1 January 2008-20 July 2021). We assessed calibration (goodness-of-fit) between expected and observed cancers and compared observed cancer rates by risk group. Risk of bias was assessed with PROBAST. RESULTS Of 5124 records, 13 were included examining 11 tools across 15 cohorts. The Gail tool was most represented (n = 11), followed by Tyrer-Cuzick (n = 5), BRCAPRO and iCARE-Lit (n = 3). No tool was consistently well-calibrated across multiple studies and breast density or polygenic risk scores did not improve calibration. Most tools identified a risk group with higher rates of observed cancers, but few tools identified lower-risk groups across different settings. All tools demonstrated a high risk of bias. CONCLUSION Some risk tools can identify groups of women at higher or lower breast cancer risk, but this is highly dependent on the setting and population.
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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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Factors Associated with Screening Mammogram Uptake among Women Attending an Urban University Primary Care Clinic in Malaysia. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19106103. [PMID: 35627637 PMCID: PMC9141597 DOI: 10.3390/ijerph19106103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 05/13/2022] [Accepted: 05/15/2022] [Indexed: 12/04/2022]
Abstract
Screening mammograms have resulted in a reduction in breast cancer mortality, yet the uptake in Malaysia was low. This study aimed to determine the prevalence and factors associated with screening mammogram uptake among women attending a Malaysian primary care clinic. A cross-sectional study was conducted among 200 women aged 40 to 74 attending the clinic. The data was collected using questionnaires assessing sociodemographic, clinical characteristics, knowledge and health beliefs. Multiple logistic regression was used to identify factors associated with mammogram uptake. The prevalence of screening mammograms was 46.0%. About 45.5% of women with high breast cancer risk had never undergone a mammogram. Older participants, aged 50 to 74 (OR = 2.57, 95% CI: 1.05, 6.29, p-value = 0.039) and those who received a physician’s recommendation (OR = 7.61, 95% CI: 3.81, 15.20, p-value < 0.001) were more likely to undergo screening mammography. Significant health beliefs associated with mammogram uptake were perceived barriers (OR = 0.81, 95% CI: 0.67, 0.97, p-value = 0.019) and cues to action (OR = 1.30, 95% CI: 1.06, 1.59, p-value = 0.012). Approximately half of the participants and those in the high-risk group had never undergone a mammogram. Older age, physician recommendation, perceived barriers and cues to action were significantly associated with mammogram uptake. Physicians need to play an active role in promoting breast cancer screening and addressing the barriers.
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Ho PJ, Ho WK, Khng AJ, Yeoh YS, Tan BKT, Tan EY, Lim GH, Tan SM, Tan VKM, Yip CH, Mohd-Taib NA, Wong FY, Lim EH, Ngeow J, Chay WY, Leong LCH, Yong WS, Seah CM, Tang SW, Ng CWQ, Yan Z, Lee JA, Rahmat K, Islam T, Hassan T, Tai MC, Khor CC, Yuan JM, Koh WP, Sim X, Dunning AM, Bolla MK, Antoniou AC, Teo SH, Li J, Hartman M. Overlap of high-risk individuals predicted by family history, and genetic and non-genetic breast cancer risk prediction models: implications for risk stratification. BMC Med 2022; 20:150. [PMID: 35468796 PMCID: PMC9040206 DOI: 10.1186/s12916-022-02334-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 03/14/2022] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND Family history, and genetic and non-genetic risk factors can stratify women according to their individual risk of developing breast cancer. The extent of overlap between these risk predictors is not clear. METHODS In this case-only analysis involving 7600 Asian breast cancer patients diagnosed between age 30 and 75 years, we examined identification of high-risk patients based on positive family history, the Gail model 5-year absolute risk [5yAR] above 1.3%, breast cancer predisposition genes (protein-truncating variants [PTV] in ATM, BRCA1, BRCA2, CHEK2, PALB2, BARD1, RAD51C, RAD51D, or TP53), and polygenic risk score (PRS) 5yAR above 1.3%. RESULTS Correlation between 5yAR (at age of diagnosis) predicted by PRS and the Gail model was low (r=0.27). Fifty-three percent of breast cancer patients (n=4041) were considered high risk by one or more classification criteria. Positive family history, PTV carriership, PRS, or the Gail model identified 1247 (16%), 385 (5%), 2774 (36%), and 1592 (21%) patients who were considered at high risk, respectively. In a subset of 3227 women aged below 50 years, the four models studied identified 470 (15%), 213 (7%), 769 (24%), and 325 (10%) unique patients who were considered at high risk, respectively. For younger women, PRS and PTVs together identified 745 (59% of 1276) high-risk individuals who were not identified by the Gail model or family history. CONCLUSIONS Family history and genetic and non-genetic risk stratification tools have the potential to complement one another to identify women at high risk.
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Affiliation(s)
- Peh Joo Ho
- Genome Institute of Singapore, Human Genetics, Singapore, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Weang Kee Ho
- Cancer Research Malaysia, 1 Jalan SS12/1A, 47500 Subang Jaya, Selangor Malaysia
- School of Mathematical Sciences, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, 43500 Semenyih, Selangor Malaysia
| | - Alexis J. Khng
- Genome Institute of Singapore, Human Genetics, Singapore, Singapore
| | - Yen Shing Yeoh
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Benita Kiat-Tee Tan
- Department of General Surgery, Sengkang General Hospital, Singapore, Singapore
- Department of Breast Surgery, Singapore General Hospital, Singapore, Singapore
- Division of Surgery and Surgical Oncology, National Cancer Centre Singapore, Singapore, Singapore
| | - Ern Yu Tan
- Department of General Surgery, Tan Tock Seng Hospital, Singapore, 308433 Singapore
- Lee Kong Chian School of Medicine, Nanyang Technology University, Singapore, Singapore
- Institute of Molecular and Cell Biology, Singapore, Singapore
| | - Geok Hoon Lim
- KK Breast Department, KK Women’s and Children’s Hospital, Singapore, 229899 Singapore
| | - Su-Ming Tan
- Division of Breast Surgery, Changi General Hospital, Singapore, Singapore
| | - Veronique Kiak Mien Tan
- Department of Breast Surgery, Singapore General Hospital, Singapore, Singapore
- Division of Surgery and Surgical Oncology, National Cancer Centre Singapore, Singapore, Singapore
| | - Cheng-Har Yip
- Subang Jaya Medical Centre, Subang Jaya, Selangor Malaysia
| | - Nur-Aishah Mohd-Taib
- Department of Surgery, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
- Universiti Malaya Cancer Research Institute, Kuala Lumpur, Malaysia
| | - Fuh Yong Wong
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore
| | - Elaine Hsuen Lim
- Division of Medical Oncology, National Cancer Centre Singapore, Singapore, Singapore
| | - Joanne Ngeow
- Lee Kong Chian School of Medicine, Nanyang Technology University, Singapore, Singapore
- Division of Medical Oncology, National Cancer Centre Singapore, Singapore, Singapore
- Cancer Genetics Service, National Cancer Centre Singapore, Singapore, Singapore
| | - Wen Yee Chay
- Division of Medical Oncology, National Cancer Centre Singapore, Singapore, Singapore
| | - Lester Chee Hao Leong
- Department of Diagnostic Radiology, Singapore General Hospital, Singapore, Singapore
| | - Wei Sean Yong
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore
| | - Chin Mui Seah
- Division of Breast Surgery, Changi General Hospital, Singapore, Singapore
| | - Siau Wei Tang
- Department of Surgery, University Surgical Cluster, National University Hospital, Singapore, Singapore
| | - Celene Wei Qi Ng
- Department of Surgery, University Surgical Cluster, National University Hospital, Singapore, Singapore
| | - Zhiyan Yan
- KK Breast Department, KK Women’s and Children’s Hospital, Singapore, 229899 Singapore
| | - Jung Ah Lee
- KK Breast Department, KK Women’s and Children’s Hospital, Singapore, 229899 Singapore
| | - Kartini Rahmat
- Healthy Longevity Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Tania Islam
- Department of Surgery, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
- Universiti Malaya Cancer Research Institute, Kuala Lumpur, Malaysia
| | - Tiara Hassan
- Cancer Research Malaysia, 1 Jalan SS12/1A, 47500 Subang Jaya, Selangor Malaysia
| | - Mei-Chee Tai
- Cancer Research Malaysia, 1 Jalan SS12/1A, 47500 Subang Jaya, Selangor Malaysia
| | - Chiea Chuen Khor
- Genome Institute of Singapore, Human Genetics, Singapore, Singapore
| | - Jian-Min Yuan
- UPMC Hillman Cancer Center, Pittsburgh, PA USA
- Department of Epidemiology, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA USA
| | - Woon-Puay Koh
- Healthy Longevity Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Singapore Institute for Clinical Sciences, Agency for Science Technology and Research (A*STAR), Singapore, 117609 Singapore
| | - Xueling Sim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Alison M. Dunning
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Manjeet K. Bolla
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Antonis C. Antoniou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Soo-Hwang Teo
- Cancer Research Malaysia, 1 Jalan SS12/1A, 47500 Subang Jaya, Selangor Malaysia
- Department of Surgery, Faculty of Medicine, University of Malaya, Jalan Universiti, 50630 Kuala Lumpur, Malaysia
| | - Jingmei Li
- Genome Institute of Singapore, Human Genetics, Singapore, Singapore
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Mikael Hartman
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
- Department of Surgery, University Surgical Cluster, National University Hospital, Singapore, Singapore
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Ho PJ, Wong FY, Chay WY, Lim EH, Lim ZL, Chia KS, Hartman M, Li J. Breast cancer risk stratification for mammographic screening: A nation-wide screening cohort of 24,431 women in Singapore. Cancer Med 2021; 10:8182-8191. [PMID: 34708579 PMCID: PMC8607242 DOI: 10.1002/cam4.4297] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 08/10/2021] [Accepted: 08/26/2021] [Indexed: 12/19/2022] Open
Abstract
Background Breast cancer incidence is increasing in Asia. However, few women in Singapore attend routine mammography screening. We aim to identify women at high risk of breast cancer who will benefit most from regular screening using the Gail model and information from their first screen (recall status and mammographic density). Methods In 24,431 Asian women (50–69 years) who attended screening between 1994 and 1997, 117 developed breast cancer within 5 years of screening. Cox proportional hazard models were used to study the associations between risk classifiers (Gail model 5‐year absolute risk, recall status, mammographic density), and breast cancer occurrence. The efficacy of risk stratification was evaluated by considering sensitivity, specificity, and the proportion of cancers identified. Results Adjusting for information from first screen attenuated the hazard ratios (HR) associated with 5‐year absolute risk (continuous, unadjusted HR [95% confidence interval]: 2.3 [1.8–3.1], adjusted HR: 1.9 [1.4–2.6]), but improved the discriminatory ability of the model (unadjusted AUC: 0.615 [0.559–0.670], adjusted AUC: 0.703 [0.653–0.753]). The sensitivity and specificity of the adjusted model were 0.709 and 0.622, respectively. Thirty‐eight percent of all breast cancers were detected in 12% of the study population considered high risk (top five percentile of the Gail model 5‐year absolute risk [absolute risk ≥1.43%], were recalled, and/or mammographic density ≥50%). Conclusion The Gail model is able to stratify women based on their individual breast cancer risk in this population. Including information from the first screen can improve prediction in the 5 years after screening. Risk stratification has the potential to pick up more cancers.
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Affiliation(s)
- Peh Joo Ho
- Genome Institute of Singapore, Singapore, Singapore.,Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Fuh Yong Wong
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore
| | - Wen Yee Chay
- Division of Medical Oncology, National Cancer Centre Singapore, Singapore, Singapore
| | - Elaine Hsuen Lim
- Division of Medical Oncology, National Cancer Centre Singapore, Singapore, Singapore
| | - Zi Lin Lim
- Genome Institute of Singapore, Singapore, Singapore
| | - Kee Seng Chia
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Mikael Hartman
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore.,Department of Surgery, Yong Loo Lin School of Medicine National University of Singapore, Singapore, Singapore
| | - Jingmei Li
- Genome Institute of Singapore, Singapore, Singapore.,Department of Surgery, Yong Loo Lin School of Medicine National University of Singapore, Singapore, Singapore
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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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9
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Hajian-Tilaki K, Nikpour M. Accuracy of self-perceived risk perception of breast cancer development in Iranian women. BMC WOMENS HEALTH 2021; 21:93. [PMID: 33663481 PMCID: PMC7934235 DOI: 10.1186/s12905-021-01238-z] [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] [Received: 07/12/2020] [Accepted: 02/22/2021] [Indexed: 01/02/2023]
Abstract
BACKGROUND The accuracy of subjective risk perception is a matter of concern in breast cancer development. The objective of this study was to evaluate the accuracy of self-perceived risk assessment of breast cancer development and compared to actual risk in Iranian women. METHODS The demographic, clinical, and reproductive characteristics of 800 women aged 35-85 years were collected with an in-person interview. The self-perceived risk and the actual risk were assessed using the visual analog scale (VAS) and he Gail model respectively. Gail's cutoff of 1.66% risk was used to categorize the estimated 5-year actual risk as low/average risk (< 1.66%) and high risk (≥ 1.66). In low/average risk, if the self-perceived risk > actual risk, then individuals were considered as overestimating. Similarly, in high-risk women, if the perceived risk < actual risk, then, the subjects were labeled as under-estimate; otherwise, it was labeled as accurate. The Kappa statistics were used to determine the agreement between self-perceived risk and actual risk. ROC analysis was applied to determine the accuracy of self-perceived risk in the prediction of actual risk. RESULTS The perceived risk was significantly higher than actual risk (p = 0.001, 0.01 for 5-year and lifetime risk respectively). Both in low and high-risk groups about half of the women over-estimate and underestimate the risk by subjective risk perception. For a 5-year risk assessment, there was no agreement between perceived risk and actual risk (Kappa = 0.00, p = 0.98) but a very low agreement between them in lifetime risk assessment (Kappa = 0.09, p = 0.005). The performance of accuracy of risk perception versus actual risk was very low (AUC = 0.53, 95% CI 0.44-0.61 and AUC = 0.58, 95% CI 0.54-0.62 for the 5-year risk and lifetime risk respectively). CONCLUSION The clinical performance of risk perception based on VAS is very poor. Thus, the efforts of the public health education program should focus on the correct perception of breast cancer risk among Iranian women.
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Affiliation(s)
- Karimollah Hajian-Tilaki
- Department of Biostatistics and Epidemiology, Babol University of Medical Sciences, Babol, Iran.,Social Determinants of Health Research Center, Health Research Institute, Babol University of Medical Sciences, Babol, Iran
| | - Maryam Nikpour
- Non-Communicable Disease Research Center, Health Research Institute, Babol University of Medical Sciences, Babol, Iran.
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10
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Solikhah S, Nurdjannah S. Assessment of the risk of developing breast cancer using the Gail model in Asian females: A systematic review. Heliyon 2020; 6:e03794. [PMID: 32346636 PMCID: PMC7182726 DOI: 10.1016/j.heliyon.2020.e03794] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Revised: 02/25/2020] [Accepted: 04/14/2020] [Indexed: 12/13/2022] Open
Abstract
Introduction Currently, the Breast Cancer Risk Assessment Tool (BCRAT), also known as the Gail model (GM) has been widely recognized and adapted for to study disparity in racial and ethnic groups in America including Asian and Pacific Islander American females. However, its applicability outside America remains uncertain due to diversity in epidemiology and risk factors of breast cancer in populations especially in Asian females. We sought to evaluate the performance of the GM to predict breast cancer risk in Asian countries. Material and methods This study identified articles published from 2010 by searching PubMed, MEDLINE, Scopus, Web of Science, Google Scholar and gray literature. The initial search terms were breast cancer, mammary, carcinoma, tumor, neoplasm, risk assessment tool, BCRAT, breast cancer prediction, Gail model, Asia, and Asian. Results The search yielded 20 articles, with 7 articles addressing the AUC and/or the expected (E) to observed (O) ratio of predicted breast cancer risk, representing the accuracy of the GM in the Asian population. One publication reported the sensitivity and specificity but no AUC. None of the studies were accepted as the standard for reporting prognostic models. Several studies reported good prognostic testing and likely developed a new model modifying the items in the instrument. Conclusion The results are not strong enough to develop breast cancer risk in the setting of Asian countries. Involving the breast cancer risk of the Asian population in developing a prognostic model with good statistical understanding is particularly important and can reduce flawed or biased models. Identifying the best methods to achieve well-suited prognostic models in the Asian population should be a priority.
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Affiliation(s)
- Solikhah Solikhah
- Faculty of Public Health, Universitas Ahmad Dahlan, Yogyakarta, 55166, Indonesia.,Dynamic Social Study Center, Universitas Ahmad Dahlan, Yogyakarta, 55166, Indonesia
| | - Sitti Nurdjannah
- Faculty of Public Health, Universitas Ahmad Dahlan, Yogyakarta, 55166, Indonesia
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11
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Nikpour M, Hajian-Tilaki K, Bakhtiari A. Risk Assessment for Breast Cancer Development and Its Clinical Impact on Screening Performance in Iranian Women. Cancer Manag Res 2019; 11:10073-10082. [PMID: 31819640 PMCID: PMC6890170 DOI: 10.2147/cmar.s229585] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Accepted: 11/16/2019] [Indexed: 01/28/2023] Open
Abstract
Introduction The aim of this study is to estimate the objective and subjective risk and to examine their associations with three forms of breast cancer screening. Methods This cross-sectional study was conducted with a sample of 800 women aged 35–85 years from the community setting and outpatient clinic in Babol, the north of Iran. The demographic, socio-economic characteristics and the risk factor profiles were collected through in-person interview. The health belief model (HBM) and visual analog scales were used to assess the women’s perceived risk of breast cancer. The practice of women regarding breast self-examination (BSE), breast clinical examination (BCE), and mammography were measured. We used the Gail model in estimating 5-year and lifetime risk. The logistic regression model was applied to determine the relationship of calculated and perceived risk on screening behaviors. Results The mean of estimated 5-year and lifetime risk were 0.89 ±0.89 and 8.87 ±3.84 percent respectively while the perceived personal risk on visual scale perception was much greater than the calculated risk. The high 5-year calculated risk was a predictor of mammography practice but not BSE and BCE; however, after adjusting the subscales of HBM and socio-demographic characteristics, its effect remained significant (adjusted OR=1.97(95% CI: 1.02–3.08)). The perceived risk from HBM in particular self-efficacy (p=0.001) remained positively significant on all forms of screening practice. Conclusion While the perceived risk from HBM scale was meaningful in screening performance, the calculated risk from the Gail model had a clinical impact on mammography behaviors independent of HBM scales.
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Affiliation(s)
- Maryam Nikpour
- Student Research Committee, Health Research Institute, Babol University of Medical Sciences, Babol, Iran
| | - Karimollah Hajian-Tilaki
- Department of Biostatistics and Epidemiology, School of Medicine, Babol University of Medical Sciences, Babol, Iran
| | - Afsaneh Bakhtiari
- Department of Midwifery, School of Medicine, Babol University of Medical Sciences, Babol, Iran
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12
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Sa-Nguanraksa D, Sasanakietkul T, O-Charoenrat C, Kulprom A, O-Charoenrat P. Gail Model Underestimates Breast Cancer Risk in Thai Population. Asian Pac J Cancer Prev 2019; 20:2385-2389. [PMID: 31450910 PMCID: PMC6852814 DOI: 10.31557/apjcp.2019.20.8.2385] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Indexed: 11/25/2022] Open
Abstract
Background: The Gail model is the most widely used method for breast cancer risk estimation. This model has
been studied and verified for its validity in many groups but there has yet to be a study to validate the Gail model in a
Thai population. This study aims to evaluate whether the Gail model can accurately calculate the risk of breast cancer
among Thai women. Methods: The subjects were recruited from the Division of Head, Neck, and Breast Surgery,
Department of Surgery, Siriraj Hospital. The patients attending the division were asked to enroll in the study and
complete questionnaires. Gail model scores were then calculated. Relationships between parameters were examined
using the Pearson’s chi-square test, Fisher’s exact test, and independent-samples t-test. Results: There were 514
women recruited. Age, parity, age at first-live birth, and history of atypical ductal hyperplasia (ADH) were significant
risk factors for breast cancer. The 5-year and lifetime risk score for breast cancer calculated by the Gail model were
not significantly different between the patient and the control subjects. The proportions of the subjects with lifetime
risk ≥20% were significantly higher in breast cancer patients (p=0.049). Conclusion: The Gail model underestimated
the risk of breast cancer in Thai women. Calibration of the model is still required before adoption in Thai population.
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Affiliation(s)
- Doonyapat Sa-Nguanraksa
- Division of Head Neck and Breast Surgery, Department of Surgery, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand.
| | - Thanyawat Sasanakietkul
- Division of Head Neck and Breast Surgery, Department of Surgery, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand. ,Minimally Invasive and Endocrine Surgery Division, Department of Surgery, Police General Hospital, Bangkok, Thailand
| | - Chayanuch O-Charoenrat
- Division of Head Neck and Breast Surgery, Department of Surgery, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand.
| | - Anchalee Kulprom
- Division of Head Neck and Breast Surgery, Department of Surgery, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand.
| | - Pornchai O-Charoenrat
- Division of Head Neck and Breast Surgery, Department of Surgery, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand.
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13
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Yang J, Lin W, Chen Y. Off-label use of tamoxifen in a Chinese tertiary care hospital. Int J Clin Pharm 2019; 41:555-562. [PMID: 30680514 DOI: 10.1007/s11096-019-00788-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2018] [Accepted: 01/09/2019] [Indexed: 10/27/2022]
Abstract
Background Tamoxifen is an estrogen receptor modulator used for the treatment of breast cancer; however, currently, it is used in many off-label indications. Objective To investigate the prevalence of tamoxifen off-label prescribing and explore available scientific evidence that supports those uses in outpatients. Setting Xiamen maternity and child health care hospital in Xiamen city of China. Method All the prescriptions of outpatients receiving tamoxifen were exported from an electronic prescribing system during a 1-year period. Tamoxifen use was then classified as either on- or off-label according to the criteria we established previously, and the details of the off-label prescriptions were collected. Logistic regression was applied to explore predictive variables. Evidence search was limited to Up-To-Date, the Micromedex database and PubMed. Main outcome measure The rate of off-label use, risk factors identified by logistic regression and evidence exhibition. Results A total of 75% of all the prescriptions available were classified as off-label use. Hyperplasia of the breast was the most frequently prescribed off-label indication. According to the analysis of logistic regression, male patients, patients less than 34 years old, and physicians with a higher professional title were more likely associated with off-label prescribing. After a search in Up-To-Date, the Micromedex database and PubMed, only male infertility, atypical hyperplasia, mastodynia, peripheral precocious puberty and gynecomastia were found to have strong evidence supporting the use of tamoxifen off-label (22.75%). Conclusion Although the off-label use of tamoxifen was common in our hospital, there was a relative shortage of evidence available supporting those uses.
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Affiliation(s)
- Jianhui Yang
- Department of Pharmacy, Xiamen Maternity and Child Health Care Hospital, No. 10 Zhenhai Road, Xiamen, 361001, Fujian Province, China.
| | - Wubin Lin
- Department of Pharmacy, Xiamen Maternity and Child Health Care Hospital, No. 10 Zhenhai Road, Xiamen, 361001, Fujian Province, China
| | - Yao Chen
- Department of Pharmacy, Xiamen Maternity and Child Health Care Hospital, No. 10 Zhenhai Road, Xiamen, 361001, Fujian Province, China
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14
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Ahmed AE, McClish DK, Alghamdi T, Alshehri A, Aljahdali Y, Aburayah K, Almaymoni A, Albaijan M, Al-Jahdali H, Jazieh AR. Modeling risk assessment for breast cancer in symptomatic women: a Saudi Arabian study. Cancer Manag Res 2019; 11:1125-1132. [PMID: 30787637 PMCID: PMC6366356 DOI: 10.2147/cmar.s189883] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Background Despite the continuing increase in the breast cancer incidence rate among Saudi Arabian women, no breast cancer risk-prediction model is available in this population. The aim of this research was to develop a risk-assessment tool to distinguish between high risk and low risk of breast cancer in a sample of Saudi women who were screened for breast cancer. Methods A retrospective chart review was conducted on symptomatic women who underwent breast mass biopsies between September 8, 2015 and November 8, 2017 at King Abdulaziz Medical City, Riyadh, Saudi Arabia. Results A total of 404 (63.8%) malignant breast biopsies and 229 (36.2%) benign breast biopsies were analyzed. Women ≥40 years old (aOR: 6.202, CI 3.497–11.001, P=0.001), hormone-replacement therapy (aOR 24.365, 95% CI 8.606–68.987, P=0.001), postmenopausal (aOR 3.058, 95% CI 1.861–5.024, P=0.001), and with a family history of breast cancer (aOR 2.307, 95% CI 1.142–4.658, P=0.020) were independently associated with an increased risk of breast cancer. This model showed an acceptable fit and had area under the receiver-operating characteristic curve of 0.877 (95% CI 0.851–0.903), with optimism-corrected area under the curve of 0.865. Conclusion The prediction model developed in this study has a high ability in predicting increased breast cancer risk in our facility. Combining information on age, use of hormone therapy, postmenopausal status, and family history of breast cancer improved the degree of discriminatory accuracy of breast cancer prediction. Our risk model may assist in initiating population-screening programs and prompt clinical decision making to manage cases and prevent unfavorable outcomes.
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Affiliation(s)
- Anwar E Ahmed
- King Abdullah International Medical Research Center (KAIMRC), Riyadh, Saudi Arabia, .,College of Public Health and Health Informatics, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia,
| | - Donna K McClish
- Department of Biostatistics, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA
| | - Thamer Alghamdi
- College of Medicine, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
| | - Abdulmajeed Alshehri
- College of Medicine, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
| | - Yasser Aljahdali
- College of Medicine, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
| | - Khalid Aburayah
- College of Medicine, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
| | - Abdulrahman Almaymoni
- College of Medicine, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
| | - Monirah Albaijan
- King Abdullah International Medical Research Center (KAIMRC), Riyadh, Saudi Arabia,
| | - Hamdan Al-Jahdali
- King Abdullah International Medical Research Center (KAIMRC), Riyadh, Saudi Arabia, .,College of Medicine, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia.,King Abdulaziz Medical City, Riyadh, Saudi Arabia.,Ministry of the National Guard - Health Affairs, Riyadh, Saudi Arabia
| | - Abdul Rahman Jazieh
- College of Medicine, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia.,King Abdulaziz Medical City, Riyadh, Saudi Arabia.,Ministry of the National Guard - Health Affairs, Riyadh, Saudi Arabia
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15
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Nickson C, Procopio P, Velentzis LS, Carr S, Devereux L, Mann GB, James P, Lee G, Wellard C, Campbell I. Prospective validation of the NCI Breast Cancer Risk Assessment Tool (Gail Model) on 40,000 Australian women. Breast Cancer Res 2018; 20:155. [PMID: 30572910 PMCID: PMC6302513 DOI: 10.1186/s13058-018-1084-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2018] [Accepted: 11/25/2018] [Indexed: 01/24/2023] Open
Abstract
Background There is a growing interest in delivering more personalised, risk-based breast cancer screening protocols. This requires population-level validation of practical models that can stratify women into breast cancer risk groups. Few studies have evaluated the Gail model (NCI Breast Cancer Risk Assessment Tool) in a population screening setting; we validated this tool in a large, screened population. Methods We used data from 40,158 women aged 50–69 years (via the lifepool cohort) participating in Australia’s BreastScreen programme. We investigated the association between Gail scores and future invasive breast cancer, comparing observed and expected outcomes by Gail score ranked groups. We also used machine learning to rank Gail model input variables by importance and then assessed the incremental benefit in risk prediction obtained by adding variables in order of diminishing importance. Results Over a median of 4.3 years, the Gail model predicted 612 invasive breast cancers compared with 564 observed cancers (expected/observed (E/O) = 1.09, 95% confidence interval (CI) 1.00–1.18). There was good agreement across decile groups of Gail scores (χ2 = 7.1, p = 0.6) although there was some overestimation of cancer risk in the top decile of our study group (E/O = 1.65, 95% CI 1.33–2.07). Women in the highest quintile (Q5) of Gail scores had a 2.28-fold increased risk of breast cancer (95% CI 1.73–3.02, p < 0.0001) compared with the lowest quintile (Q1). Compared with the median quintile, women in Q5 had a 34% increased risk (95% CI 1.06–1.70, p = 0.014) and those in Q1 had a 41% reduced risk (95% CI 0.44–0.79, p < 0.0001). Similar patterns were observed separately for women aged 50–59 and 60–69 years. The model’s overall discrimination was modest (area under the curve (AUC) 0.59, 95% CI 0.56–0.61). A reduced Gail model excluding information on ethnicity and hyperplasia was comparable to the full Gail model in terms of correctly stratifying women into risk groups. Conclusions This study confirms that the Gail model (or a reduced model excluding information on hyperplasia and ethnicity) can effectively stratify a screened population aged 50–69 years according to the risk of future invasive breast cancer. This information has the potential to enable more personalised, risk-based screening strategies that aim to improve the balance of the benefits and harms of screening. Electronic supplementary material The online version of this article (10.1186/s13058-018-1084-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Carolyn Nickson
- Melbourne School of Population and Global Health, University of Melbourne, Carlton, Victoria, 3010, Australia. .,Cancer Research Division, Cancer Council NSW, Woolloomooloo, NSW, 2011, Australia.
| | - Pietro Procopio
- Melbourne School of Population and Global Health, University of Melbourne, Carlton, Victoria, 3010, Australia.,Cancer Research Division, Cancer Council NSW, Woolloomooloo, NSW, 2011, Australia
| | - Louiza S Velentzis
- Melbourne School of Population and Global Health, University of Melbourne, Carlton, Victoria, 3010, Australia.,Cancer Research Division, Cancer Council NSW, Woolloomooloo, NSW, 2011, Australia
| | - Sarah Carr
- Melbourne School of Population and Global Health, University of Melbourne, Carlton, Victoria, 3010, Australia
| | - Lisa Devereux
- Lifepool Study, Peter MacCallum Cancer Centre, Melbourne, Victoria, 3000, Australia
| | - Gregory Bruce Mann
- Breast Service, Royal Women's and Royal Melbourne Hospital, Parkville, Victoria, 3050, Australia.,Department of Surgery, The University of Melbourne, Parkville, 3010, Australia
| | - Paul James
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, 3000, Australia.,Familial Cancer Centre, Peter MacCallum Cancer Centre and Royal Melbourne Hospital, Parkville, Victoria, 3052, Australia
| | - Grant Lee
- Melbourne School of Population and Global Health, University of Melbourne, Carlton, Victoria, 3010, Australia
| | - Cameron Wellard
- Melbourne School of Population and Global Health, University of Melbourne, Carlton, Victoria, 3010, Australia
| | - Ian Campbell
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, 3000, Australia.,Cancer Genetics Laboratory, Peter MacCallum Cancer Centre, Melbourne, Victoria, 3000, Australia
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16
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Chowdhury M, Euhus D, Arun B, Umbricht C, Biswas S, Choudhary P. Validation of a personalized risk prediction model for contralateral breast cancer. Breast Cancer Res Treat 2018; 170:415-423. [PMID: 29574637 DOI: 10.1007/s10549-018-4763-5] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2018] [Accepted: 03/17/2018] [Indexed: 01/02/2023]
Abstract
PURPOSE Women diagnosed with unilateral breast cancer are increasingly choosing to remove their other unaffected breast through contralateral prophylactic mastectomy (CPM) to reduce the risk of contralateral breast cancer (CBC). Yet a large proportion of CPMs are believed to be medically unnecessary. Thus, there is a pressing need to educate patients effectively on their CBC risk. We had earlier developed a CBC risk prediction model called CBCRisk based on eight personal risk factors. METHODS In this study, we validate CBCRisk on independent clinical data from the Johns Hopkins University (JH) and MD Anderson Cancer Center (MDA). Women whose first breast cancer diagnosis was either invasive and/or ductal carcinoma in situ and whose age at first diagnosis was between 18 and 88 years were included in the cohorts because CBCRisk was developed specifically for these women. A woman who develops CBC is called a case whereas a woman who does not is called a control. The cohort sizes are 6035 (with 117 CBC cases) for JH and 5185 (with 111 CBC cases) for MDA. We computed the relevant calibration and validation measures for 3- and 5-year risk predictions. RESULTS We found that the model performs reasonably well for both cohorts. In particular, area under the receiver-operating characteristic curve for the two cohorts range from 0.61 to 0.65. CONCLUSIONS With this independent validation, CBCRisk can be used confidently in clinical settings for counseling BC patients by providing their individualized CBC risk. In turn, this may potentially help alleviate the rate of medically unnecessary CPMs.
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Affiliation(s)
- Marzana Chowdhury
- Department of Mathematical Sciences, University of Texas at Dallas, 800 W Campbell Rd., FO 35, Richardson, TX, 75080, USA
| | - David Euhus
- Division of Surgical Oncology, Johns Hopkins University, Baltimore, USA
| | - Banu Arun
- Department of Breast Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, USA
| | - Chris Umbricht
- Division of Surgical Oncology, Johns Hopkins University, Baltimore, USA
| | - Swati Biswas
- Department of Mathematical Sciences, University of Texas at Dallas, 800 W Campbell Rd., FO 35, Richardson, TX, 75080, USA.
| | - Pankaj Choudhary
- Department of Mathematical Sciences, University of Texas at Dallas, 800 W Campbell Rd., FO 35, Richardson, TX, 75080, USA.
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17
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Wang X, Huang Y, Li L, Dai H, Song F, Chen K. Assessment of performance of the Gail model for predicting breast cancer risk: a systematic review and meta-analysis with trial sequential analysis. Breast Cancer Res 2018; 20:18. [PMID: 29534738 PMCID: PMC5850919 DOI: 10.1186/s13058-018-0947-5] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2017] [Accepted: 02/26/2018] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND The Gail model has been widely used and validated with conflicting results. The current study aims to evaluate the performance of different versions of the Gail model by means of systematic review and meta-analysis with trial sequential analysis (TSA). METHODS Three systematic review and meta-analyses were conducted. Pooled expected-to-observed (E/O) ratio and pooled area under the curve (AUC) were calculated using the DerSimonian and Laird random-effects model. Pooled sensitivity, specificity and diagnostic odds ratio were evaluated by bivariate mixed-effects model. TSA was also conducted to determine whether the evidence was sufficient and conclusive. RESULTS Gail model 1 accurately predicted breast cancer risk in American women (pooled E/O = 1.03; 95% CI 0.76-1.40). The pooled E/O ratios of Caucasian-American Gail model 2 in American, European and Asian women were 0.98 (95% CI 0.91-1.06), 1.07 (95% CI 0.66-1.74) and 2.29 (95% CI 1.95-2.68), respectively. Additionally, Asian-American Gail model 2 overestimated the risk for Asian women about two times (pooled E/O = 1.82; 95% CI 1.31-2.51). TSA showed that evidence in Asian women was sufficient; nonetheless, the results in American and European women need further verification. The pooled AUCs for Gail model 1 in American and European women and Asian females were 0.55 (95% CI 0.53-0.56) and 0.75 (95% CI 0.63-0.88), respectively, and the pooled AUCs of Caucasian-American Gail model 2 for American, Asian and European females were 0.61 (95% CI 0.59-0.63), 0.55 (95% CI 0.52-0.58) and 0.58 (95% CI 0.55-0.62), respectively. The pooled sensitivity, specificity and diagnostic odds ratio of Gail model 1 were 0.63 (95% CI 0.27-0.89), 0.91 (95% CI 0.87-0.94) and 17.38 (95% CI 2.66-113.70), respectively, and the corresponding indexes of Gail model 2 were 0.35 (95% CI 0.17-0.59), 0.86 (95% CI 0.76-0.92) and 3.38 (95% CI 1.40-8.17), respectively. CONCLUSIONS The Gail model was more accurate in predicting the incidence of breast cancer in American and European females, while far less useful for individual-level risk prediction. Moreover, the Gail model may overestimate the risk in Asian women and the results were further validated by TSA, which is an addition to the three previous systematic review and meta-analyses. TRIAL REGISTRATION PROSPERO CRD42016047215 .
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Affiliation(s)
- Xin Wang
- Department of Epidemiology and Biostatistics, Key Laboratory of Cancer Prevention and Therapy, Tianjin Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Huanhu Xi Road, Tiyuan Bei, Hexi District, Tianjin, 300060 People’s Republic of China
| | - Yubei Huang
- Department of Epidemiology and Biostatistics, Key Laboratory of Cancer Prevention and Therapy, Tianjin Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Huanhu Xi Road, Tiyuan Bei, Hexi District, Tianjin, 300060 People’s Republic of China
| | - Lian Li
- Department of Epidemiology and Biostatistics, Key Laboratory of Cancer Prevention and Therapy, Tianjin Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Huanhu Xi Road, Tiyuan Bei, Hexi District, Tianjin, 300060 People’s Republic of China
| | - Hongji Dai
- Department of Epidemiology and Biostatistics, Key Laboratory of Cancer Prevention and Therapy, Tianjin Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Huanhu Xi Road, Tiyuan Bei, Hexi District, Tianjin, 300060 People’s Republic of China
| | - Fengju Song
- Department of Epidemiology and Biostatistics, Key Laboratory of Cancer Prevention and Therapy, Tianjin Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Huanhu Xi Road, Tiyuan Bei, Hexi District, Tianjin, 300060 People’s Republic of China
| | - Kexin Chen
- Department of Epidemiology and Biostatistics, Key Laboratory of Cancer Prevention and Therapy, Tianjin Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Huanhu Xi Road, Tiyuan Bei, Hexi District, Tianjin, 300060 People’s Republic of China
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Epidemiology and survival outcome of breast cancer in a nationwide study. Oncotarget 2017; 8:16939-16950. [PMID: 28199975 PMCID: PMC5370012 DOI: 10.18632/oncotarget.15207] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2016] [Accepted: 01/24/2017] [Indexed: 12/17/2022] Open
Abstract
Breast cancer is among the most prevalent cancers in Taiwan. The National Health Insurance database was used to identify patients with breast cancer and estimate the yearly prevalence and incidence of breast cancer between 1997 and 2013. Joinpoint regression analysis was used for the annual percentage change of incidence, prevalence, and survival outcome. Among 12,181,919 female beneficiaries in 2013, the prevalence was 834.37 per 100,000 persons (95% confidence interval, 829.28–839.45) and the incidence was 93.00 per 100,000 person-year (95% confidence interval, 91.27–94.73). The average annual percentage change of the age-standardized breast cancer incidence was 3.5 per 100,000 person-years (3.1–3.8; P < 0.05), suggesting an increase in breast cancer incidence over the study period. The 5-year mortality rate was 4.5% in 1997 and 4.4% in 2008. The 5-year mortality rate among patients with Charlson comorbidity index > 1 was 39.1% (19.2%–59.1%) in 1997 and 21.1% (15.7%-32.0%) in 2008, with an annual percentage change of –0.8 (–1.3 to 2.9), suggesting that the mortality rate was gradually decreasing in patients with comorbidities. In conclusion, 1 in 120 women in Taiwan has breast cancer and the incidence is rising, while the annual percentage change of breast cancer prevalence is decreasing. The mortality rate of breast cancer was essentially stable, but the 1-year, 2-year, and 5-year mortality rates in people with Charlson comorbidity index > 1 were declined.
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Cintolo-Gonzalez JA, Braun D, Blackford AL, Mazzola E, Acar A, Plichta JK, Griffin M, Hughes KS. Breast cancer risk models: a comprehensive overview of existing models, validation, and clinical applications. Breast Cancer Res Treat 2017; 164:263-284. [DOI: 10.1007/s10549-017-4247-z] [Citation(s) in RCA: 75] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2017] [Accepted: 04/12/2017] [Indexed: 01/01/2023]
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Béroud C, Letovsky SI, Braastad CD, Caputo SM, Beaudoux O, Bignon YJ, Bressac-De Paillerets B, Bronner M, Buell CM, Collod-Béroud G, Coulet F, Derive N, Divincenzo C, Elzinga CD, Garrec C, Houdayer C, Karbassi I, Lizard S, Love A, Muller D, Nagan N, Nery CR, Rai G, Revillion F, Salgado D, Sévenet N, Sinilnikova O, Sobol H, Stoppa-Lyonnet D, Toulas C, Trautman E, Vaur D, Vilquin P, Weymouth KS, Willis A, Eisenberg M, Strom CM. BRCA Share: A Collection of Clinical BRCA Gene Variants. Hum Mutat 2016; 37:1318-1328. [DOI: 10.1002/humu.23113] [Citation(s) in RCA: 51] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2016] [Accepted: 09/02/2016] [Indexed: 12/12/2022]
Affiliation(s)
- Christophe Béroud
- Aix Marseille Univ; INSERM, GMGF Marseille France
- APHM; Hôpital TIMONE Enfants; Laboratoire de Génétique Moléculaire; Marseille France
| | | | | | - Sandrine M. Caputo
- Service de Génétique; Department de Biologie des Tumeurs; Institut Curie; Paris France
| | | | | | | | | | | | | | - Florence Coulet
- Groupe hospitalier Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Laboratoire d'Oncogénétique et Angiogénétique moléculaire; Université Pierre et Marie Curie; Paris France
| | - Nicolas Derive
- Service de Génétique; Department de Biologie des Tumeurs; Institut Curie; Paris France
| | | | | | | | - Claude Houdayer
- Service de Génétique; Department de Biologie des Tumeurs; Institut Curie; Paris France
- Université Paris Descartes; Paris France
| | | | - Sarab Lizard
- CHU de Dijon; Hôpital d'Enfants; Service de Génétique Médicale Dijon France
| | - Angela Love
- Quest Diagnostics; Marlborough Massachusetts
| | | | | | | | - Ghadi Rai
- Aix Marseille Univ; INSERM, GMGF Marseille France
| | | | | | | | | | | | - Dominique Stoppa-Lyonnet
- Service de Génétique; Department de Biologie des Tumeurs; Institut Curie; Paris France
- Université Paris Descartes; Paris France
| | | | - Edwin Trautman
- Laboratory Corporation of America; Westborough Massachusetts
| | - Dominique Vaur
- Laboratoire de biologie et de génétique du cancer; CLCC François Baclesse; INSERM 1079 Centre Normand de Génomique et de Médecine Personnalisée; Caen France
| | - Paul Vilquin
- Laboratoire de Biologie Cellulaire et Hormonale (CHU Arnaud de Villeneuve); Montpellier France
| | | | - Alecia Willis
- Laboratory Corporation of America; Research Triangle Park North Carolina
| | - Marcia Eisenberg
- Laboratory Corporation of America; Research Triangle Park North Carolina
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Lee CPL, Choi H, Soo KC, Tan MH, Chay WY, Chia KS, Liu J, Li J, Hartman M. Mammographic Breast Density and Common Genetic Variants in Breast Cancer Risk Prediction. PLoS One 2015; 10:e0136650. [PMID: 26401662 PMCID: PMC4581713 DOI: 10.1371/journal.pone.0136650] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2014] [Accepted: 08/06/2015] [Indexed: 01/25/2023] Open
Abstract
Introduction Known prediction models for breast cancer can potentially by improved by the addition of mammographic density and common genetic variants identified in genome-wide associations studies known to be associated with risk of the disease. We evaluated the benefit of including mammographic density and the cumulative effect of genetic variants in breast cancer risk prediction among women in a Singapore population. Methods We estimated the risk of breast cancer using a prospective cohort of 24,161 women aged 50 to 64 from Singapore with available mammograms and known risk factors for breast cancer who were recruited between 1994 and 1997. We measured mammographic density using the medio-lateral oblique views of both breasts. Each woman’s genotype for 75 SNPs was simulated based on the genotype frequency obtained from the Breast Cancer Association Consortium data and the cumulative effect was summarized by a genetic risk score (GRS). Any improvement in the performance of our proposed prediction model versus one containing only variables from the Gail model was assessed by changes in receiver-operating characteristic and predictive values. Results During 17 years of follow-up, 680 breast cancer cases were diagnosed. The multivariate-adjusted hazard ratios (95% confidence intervals) were 1.60 (1.22–2.10), 2.20 (1.65–2.92), 2.33 (1.71–3.20), 2.12 (1.43–3.14), and 3.27 (2.24–4.76) for the corresponding mammographic density categories: 11-20cm2, 21-30cm2, 31-40cm2, 41-50cm2, 51-60cm2, and 1.10 (1.03–1.16) for GRS. At the predicted absolute 10-year risk thresholds of 2.5% and 3.0%, a model with mammographic density and GRS could correctly identify 0.9% and 0.5% more women who would develop the disease compared to a model using only the Gail variables, respectively. Conclusion Mammographic density and common genetic variants can improve the discriminatory power of an established breast cancer risk prediction model among females in Singapore.
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Affiliation(s)
- Charmaine Pei Ling Lee
- NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Hyungwon Choi
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | | | - Min-Han Tan
- National Cancer Centre, Singapore, Singapore
| | | | - Kee Seng Chia
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Jenny Liu
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Jingmei Li
- Human Genetics, Genome Institute of Singapore, Singapore, Singapore
| | - Mikael Hartman
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
- Department of Surgery, National University Hospital, Singapore, Singapore
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- * E-mail:
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Reproductive factors, adiposity, breastfeeding and their associations with ovarian cancer in an Asian cohort. Cancer Causes Control 2015; 26:1561-73. [PMID: 26342607 DOI: 10.1007/s10552-015-0649-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2015] [Accepted: 07/30/2015] [Indexed: 12/19/2022]
Abstract
PURPOSE The aim of this study was to assess associations of breastfeeding, adiposity and reproductive risk factors with ovarian cancer risk in a Singaporean population. In addition to the main analysis, interaction effects of parity on other risk factors were examined. METHODS A retrospective cohort consisting of 28,201 women with 107 incident ovarian cancers in up to 17 years of follow-up from the Singapore Breast Cancer Screening Project (1994-1997) was studied. Hazard ratios (HRs) for risk factors were estimated using Cox proportional hazards models. RESULTS Body mass index and breastfeeding were found to have no statistical significant association with ovarian cancer risk. Gravidity was inversely associated with ovarian cancer risk [each pregnancy, adjusted HR 0.89, 95% confidence interval (CI) 0.81, 0.97], while results for parity were very similar (per delivery, HR 0.89, 95% CI 0.81, 0.98). Each additional year of ovulatory period was found to increase ovarian cancer risk by 2% (HR 1.02, 95% CI 1.00, 1.04). Each year increase in total duration of oral contraceptive use reduced ovarian cancer risk by 6% (HR 0.94, 95% CI 0.85, 1.02). CONCLUSIONS Parity, gravidity and shorter ovulatory period were associated with lower ovarian cancer risk. Breastfeeding and body mass index were not associated with ovarian cancer risk, while increased duration of oral contraceptive use resulted in borderline risk reduction. No significant evidence was found to suggest that parity had an interaction effect on any risk factor.
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Mohammadbeigi A, Mohammadsalehi N, Valizadeh R, Momtaheni Z, Mokhtari M, Ansari H. Lifetime and 5 years risk of breast cancer and attributable risk factor according to Gail model in Iranian women. J Pharm Bioallied Sci 2015; 7:207-11. [PMID: 26229355 PMCID: PMC4517323 DOI: 10.4103/0975-7406.160020] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2015] [Revised: 05/16/2015] [Accepted: 05/21/2015] [Indexed: 11/08/2022] Open
Abstract
Introduction: Breast cancer is the most commonly diagnosed cancers in women worldwide and in Iran. It is expected to account for 29% of all new cancers in women at 2015. This study aimed to assess the 5 years and lifetime risk of breast cancer according to Gail model, and to evaluate the effect of other additional risk factors on the Gail risk. Materials and Methods: A cross sectional study conducted on 296 women aged more than 34-year-old in Qom, Center of Iran. Breast Cancer Risk Assessment Tool calculated the Gail risk for each subject. Data were analyzed by paired t-test, independent t-test, and analysis of variance in bivariate approach to evaluate the effect of each factor on Gail risk. Multiple linear regression models with stepwise method were used to predict the effect of each variable on the Gail risk. Results: The mean age of the participants was 47.8 ± 8.8-year-old and 47% have Fars ethnicity. The 5 years and lifetime risk was 0.37 ± 0.18 and 4.48 ± 0.925%, respectively. It was lower than the average risk in same race and age women (P < 0.001). Being single, positive family history of breast cancer, positive history of biopsy, and radiotherapy as well as using nonhormonal contraceptives were related to higher lifetime risk (P < 0.05). Moreover, a significant direct correlation observed between lifetime risk and body mass index, age of first live birth, and menarche age. While an inversely correlation observed between lifetimes risk of breast cancer and total month of breast feeding duration and age. Conclusion: Based on our results, the 5 years and lifetime risk of breast cancer according to Gail model was lower than the same race and age. Moreover, by comparison with national epidemiologic indicators about morbidity and mortality of breast cancer, it seems that the Gail model overestimate the risk of breast cancer in Iranian women.
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Affiliation(s)
- Abolfazl Mohammadbeigi
- Department of Epidemiology and Biostatistics, School of Health, Health Policy and Promotion Research Center, Qom University of Medical Sciences, Qom, Iran
| | - Narges Mohammadsalehi
- Department of Research Vice-chancellor, Qom University of Medical Sciences, Qom, Iran
| | - Razieh Valizadeh
- Student Research Committee, Qom University of Medical Sciences, Qom, Iran
| | - Zeinab Momtaheni
- Student Research Committee, Qom University of Medical Sciences, Qom, Iran
| | - Mohsen Mokhtari
- Department Health Vice-Chancellor, Arak University of Medical Sciences, Arak, Iran
| | - Hossein Ansari
- Health Promotion Research Center, Department of Epidemiology and biostatistics, Zahedan University of Medical Sciences, Zahedan, Iran
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Min JW, Chang MC, Lee HK, Hur MH, Noh DY, Yoon JH, Jung Y, Yang JH. Validation of risk assessment models for predicting the incidence of breast cancer in korean women. J Breast Cancer 2014; 17:226-35. [PMID: 25320620 PMCID: PMC4197352 DOI: 10.4048/jbc.2014.17.3.226] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2013] [Accepted: 06/23/2014] [Indexed: 11/30/2022] Open
Abstract
Purpose The Gail model is one of the most widely used tools to assess the risk of breast cancer. However, it is known to overestimate breast cancer risk for Asian women. Here, we validate the Gail model and the Korean model using Korean data, and subsequently update and revalidate the Korean model using recent data. Methods We validated the modified Gail model (model 2), Asian American Gail model, and a previous Korean model using screening patient data collected between January 1999 and July 2004. The occurrence of breast cancer was confirmed by matching the resident registration number with data from the Korean Breast Cancer Registration Program. The expected-to-observed (E/O) ratio was used to validate the reliability of the program, and receiver operating characteristics curve analysis was used to evaluate the program's discriminatory power. There has been a rapid increase in the incidence of breast cancer in Korea, and we updated and revalidated the Korean model using incidence and mortality rate data from recent years. Results Among 40,229 patients who were included in the validation, 161 patients were confirmed to have developed breast cancer within 5 years of screening. The E/O ratios and 95% confidence intervals (CI) were 2.46 (2.10-2.87) for the modified Gail model and 1.29 (1.11-1.51) for the Asian American Gail model. The E/O ratio and 95% CI for the Korean model was 0.50 (0.43-0.59). For the updated Korean model, the E/O ratio and 95% CI were 0.85 (0.73-1.00). In the discriminatory power, the area under curve and 95% CI of the modified Gail model, Asian American Gail model, Korean model and updated Korean model were 0.547 (0.500-0.594), 0.543 (0.495-0.590), 0.509 (0.463-0.556), and 0.558 (0.511-0.605), respectively. Conclusion The updated Korean model shows a better performance than the other three models. It is hoped that this study can provide the basis for a clinical risk assessment program and a future prospective study of breast cancer prevention.
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Affiliation(s)
- Jun Won Min
- Department of Surgery, Dankook University College of Medicine, Cheonan, Korea
| | - Myung-Chul Chang
- Department of Surgery, Dankook University College of Medicine, Cheonan, Korea
| | - Hae Kyung Lee
- Department of Surgery, Cheil General Hospital and Women's Healthcare Center, Kwandong University College of Medicine, Seoul, Korea
| | - Min Hee Hur
- Department of Surgery, Cheil General Hospital and Women's Healthcare Center, Kwandong University College of Medicine, Seoul, Korea
| | - Dong-Young Noh
- Department of Surgery, Seoul National University College of Medicine, Seoul, Korea
| | - Jung Han Yoon
- Department of Surgery, Chonnam National University Hwasun Hospital, Hwasun, Korea
| | - Yongsik Jung
- Department of Surgery, Ajou University School of Medicine, Suwon, Korea
| | - Jung-Hyun Yang
- Department of Surgery, Konkuk University School of Medicine, Seoul, Korea
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Lee JY, Klimberg S, Bondurant KL, Phillips MM, Kadlubar SA. Cross-sectional study to assess the association of population density with predicted breast cancer risk. Breast J 2014; 20:615-21. [PMID: 25200109 DOI: 10.1111/tbj.12330] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
The Gail and CARE models estimate breast cancer risk for white and African-American (AA) women, respectively. The aims of this study were to compare metropolitan and nonmetropolitan women with respect to predicted breast cancer risks based on known risk factors, and to determine if population density was an independent risk factor for breast cancer risk. A cross-sectional survey was completed by 15,582 women between 35 and 85 years of age with no history of breast cancer. Metropolitan and nonmetropolitan women were compared with respect to risk factors, and breast cancer risk estimates, using general linear models adjusted for age. For both white and AA women, tisk factors used to estimate breast cancer risk included age at menarche, history of breast biopsies, and family history. For white women, age at first childbirth was an additional risk factor. In comparison to their nonmetropolitan counterparts, metropolitan white women were more likely to report having a breast biopsy, have family history of breast cancer, and delay childbirth. Among white metropolitan and nonmetropolitan women, mean estimated 5-year risks were 1.44% and 1.32% (p < 0.001), and lifetime risks of breast cancer were 10.81% and 10.01% (p < 0.001), respectively. AA metropolitan residents were more likely than those from nonmetropolitan areas to have had a breast biopsy. Among AA metropolitan and nonmetropolitan women, mean estimated 5-year risks were 1.16% and 1.12% (p = 0.039) and lifetime risks were 8.94%, and 8.85% (p = 0.344). Metropolitan residence was associated with higher predicted breast cancer risks for white women. Among AA women, metropolitan residence was associated with a higher predicted breast cancer risk at 5 years, but not over a lifetime. Population density was not an independent risk factor for breast cancer.
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Affiliation(s)
- Jeannette Y Lee
- Department of Biostatistics, University of Arkansas for Medical Sciences, Little Rock, Arkansas
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Development of a risk assessment tool for projecting individualized probabilities of developing breast cancer for Chinese women. Tumour Biol 2014; 35:10861-9. [PMID: 25085581 DOI: 10.1007/s13277-014-1967-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2014] [Accepted: 04/11/2014] [Indexed: 01/13/2023] Open
Abstract
The optimal approach regarding breast cancer screening for Chinese women is unclear due to the relative low incidence rate. A risk assessment tool may be useful for selection of high-risk subsets of population for mammography screening in low-incidence and resource-limited developing country. The odd ratios for six main risk factors of breast cancer were pooled by review manager after a systematic research of literature. Health risk appraisal (HRA) model was developed to predict an individual's risk of developing breast cancer in the next 5 years from current age. The performance of this HRA model was assessed based on a first-round screening database. Estimated risk of breast cancer increased with age. Increases in the 5-year risk of developing breast cancer were found with the existence of any of included risk factors. When individuals who had risk above median risk (3.3‰) were selected from the validation database, the sensitivity is 60.0% and the specificity is 47.8%. The unweighted area under the curve (AUC) was 0.64 (95% CI = 0.50-0.78). The risk-prediction model reported in this article is based on a combination of risk factors and shows good overall predictive power, but it is still weak at predicting which particular women will develop the disease. It would be very helpful for the improvement of a current model if more population-based prospective follow-up studies were used for the validation.
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Cancer prevention in Asia: resource-stratified guidelines from the Asian Oncology Summit 2013. Lancet Oncol 2013; 14:e497-507. [DOI: 10.1016/s1470-2045(13)70350-4] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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Challa VR, Swamyvelu K, Shetty N. Assessment of the clinical utility of the Gail model in estimating the risk of breast cancer in women from the Indian population. Ecancermedicalscience 2013; 7:363. [PMID: 24171047 PMCID: PMC3797657 DOI: 10.3332/ecancer.2013.363] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2013] [Indexed: 01/15/2023] Open
Abstract
Introduction Breast cancer screening programmes are based on various risk models to assess the risk of breast cancer in the general population. The aim of the present study is to predict the efficacy of the Gail model (GM) in the Indian population. We did a retrospective calculation of the Gail score from the hospital records of patients with breast cancer and benign breast disease. Materials and methods The Gail score was calculated in three groups. The three groups were made up of 104 patients with confirmed breast cancer (Group A), 100 patients with confirmed benign breast diseases (Group B), and 100 patient attendants (Group C). Statistical analysis The data analysis was done using SPSS 15.0, Medcal 9.0.1. Results The median Gail score in the three groups of patients was 7.5±3.04 in patients with breast cancer, 8.2±1.4 in patients with benign breast diseases, and 7.8±1.7 in normal people. The median Gail score was lower in patients with breast cancer when compared with normal people. Conclusion The GM is not useful in identifying the risk of breast cancer in Indian women. There is a need for further studies to evaluate other genetic and environmental factors to create an appropriate model for the Indian population.
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Affiliation(s)
- Vasu Reddy Challa
- Kidwai Memorial Institute of Oncology, Bengaluru, Karnataka 560029, India
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Pastor-Barriuso R, Ascunce N, Ederra M, Erdozáin N, Murillo A, Alés-Martínez JE, Pollán M. Recalibration of the Gail model for predicting invasive breast cancer risk in Spanish women: a population-based cohort study. Breast Cancer Res Treat 2013; 138:249-59. [PMID: 23378108 PMCID: PMC3586062 DOI: 10.1007/s10549-013-2428-y] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2012] [Accepted: 01/21/2013] [Indexed: 01/10/2023]
Abstract
The Gail model for predicting the absolute risk of invasive breast cancer has been validated extensively in US populations, but its performance in the international setting remains uncertain. We evaluated the predictive accuracy of the Gail model in 54,649 Spanish women aged 45-68 years who were free of breast cancer at the 1996-1998 baseline mammographic examination in the population-based Navarre Breast Cancer Screening Program. Incident cases of invasive breast cancer and competing deaths were ascertained until the end of 2005 (average follow-up of 7.7 years) through linkage with population-based cancer and mortality registries. The Gail model was tested for calibration and discrimination in its original form and after recalibration to the lower breast cancer incidence and risk factor prevalence in the study cohort, and compared through cross-validation with a Navarre model fully developed from this cohort. The original Gail model overpredicted significantly the 835 cases of invasive breast cancer observed in the cohort (ratio of expected to observed cases 1.46, 95 % CI 1.36-1.56). The recalibrated Gail model was well calibrated overall (expected-to-observed ratio 1.00, 95 % CI 0.94-1.07), but it tended to underestimate risk for women in low-risk quintiles and to overestimate risk in high-risk quintiles (P = 0.01). The Navarre model showed good cross-validated calibration overall (expected-to-observed ratio 0.98, 95 % CI 0.92-1.05) and in different cohort subsets. The Navarre and Gail models had modest cross-validated discrimination indexes of 0.542 (95 % CI 0.521-0.564) and 0.544 (95 % CI 0.523-0.565), respectively. Although the original Gail model cannot be applied directly to populations with different underlying rates of invasive breast cancer, it can readily be recalibrated to provide unbiased estimates of absolute risk in such populations. Nevertheless, its limited discrimination ability at the individual level highlights the need to develop extended models with additional strong risk factors.
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Affiliation(s)
- Roberto Pastor-Barriuso
- National Center for Epidemiology, Carlos III Institute of Health, Monforte de Lemos 5, 28029 Madrid, Spain.
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Tan MH. Recognising the Many Faces of Cancer. ANNALS OF THE ACADEMY OF MEDICINE, SINGAPORE 2012. [DOI: 10.47102/annals-acadmedsg.v41n2p47] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- Min Han Tan
- Institute of Bioengineering and Nanotechnology, Singapore
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