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Pruitt WR, Samuels B, Cunningham S. The Gail Model and Its Use in Preventive Screening: A Comparison of the Corbelli Study. Cureus 2024; 16:e56290. [PMID: 38501027 PMCID: PMC10945157 DOI: 10.7759/cureus.56290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/15/2024] [Indexed: 03/20/2024] Open
Abstract
Background This study aims to determine the usage of the Gail model in screening for breast cancer during physical examinations of women by sampling primary care physicians in two regions of Texas - Hidalgo County and Johnson County. A Gail score of 1.66% or higher indicates increased breast cancer risk. Three specialties are surveyed: internal medicine (IM), family medicine (FM), and gynecology (GYN). The null hypothesis for this study is that primary care physicians do not use the Gail model in screening for breast cancer during physical examinations of women. Methods A survey was distributed to 100 physicians with specialties in IM, FM, and GYN from May 2022 to July 2022. The survey assessed the physician's frequency of use of the Gail model and chemoprevention. Data were collected by distributing survey questionnaires to physicians in person. Descriptive statistics were used for response distributions. Fisher's exact probability test was used for comparisons across specialties. Results The response rate was 34% (34/100). Thirty-eight percent of the physicians surveyed reported using the Gail model in their practice (IM 46%, FM 23%, and GYN 31%). All 13 of the physicians using the Gail model were open to using chemoprevention. Conclusions Only 38% of the physicians surveyed responded that they use the Gail model in their practice. The study concluded that a minority of primary care physicians used the Gail model to decrease breast cancer risk. Further research would help to define better the Gail model and its use in preventing breast cancer in women. The Gail model appears to be beneficial to breast cancer risk reduction; however, risk reduction medication side effects need to be minimized.
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Affiliation(s)
| | - Beryl Samuels
- Neurosciences, Johns Hopkins University, Baltimore, USA
| | - Scott Cunningham
- Obstetrics and Gynecology, All American Institute of Medical Sciences, Black River, JAM
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Zirpoli GR, Pfeiffer RM, Bertrand KA, Huo D, Lunetta KL, Palmer JR. Addition of polygenic risk score to a risk calculator for prediction of breast cancer in US Black women. Breast Cancer Res 2024; 26:2. [PMID: 38167144 PMCID: PMC10763003 DOI: 10.1186/s13058-023-01748-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 11/30/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Previous work in European ancestry populations has shown that adding a polygenic risk score (PRS) to breast cancer risk prediction models based on epidemiologic factors results in better discriminatory performance as measured by the AUC (area under the curve). Following publication of the first PRS to perform well in women of African ancestry (AA-PRS), we conducted an external validation of the AA-PRS and then evaluated the addition of the AA-PRS to a risk calculator for incident breast cancer in Black women based on epidemiologic factors (BWHS model). METHODS Data from the Black Women's Health Study, an ongoing prospective cohort study of 59,000 US Black women followed by biennial questionnaire since 1995, were used to calculate AUCs and 95% confidence intervals (CIs) for discriminatory accuracy of the BWHS model, the AA-PRS alone, and a new model that combined them. Analyses were based on data from 922 women with invasive breast cancer and 1844 age-matched controls. RESULTS AUCs were 0.577 (95% CI 0.556-0.598) for the BWHS model and 0.584 (95% CI 0.563-0.605) for the AA-PRS. For a model that combined estimates from the questionnaire-based BWHS model with the PRS, the AUC increased to 0.623 (95% CI 0.603-0.644). CONCLUSIONS This combined model represents a step forward for personalized breast cancer preventive care for US Black women, as its performance metrics are similar to those from models in other populations. Use of this new model may mitigate exacerbation of breast cancer disparities if and when it becomes feasible to include a PRS in routine health care decision-making.
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Affiliation(s)
- Gary R Zirpoli
- Slone Epidemiology Center at Boston University, Boston, MA, USA
| | - Ruth M Pfeiffer
- Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
- Division of Cancer Epidemiology and Biostatistics, National Cancer Institute, Bethesda, USA.
| | - Kimberly A Bertrand
- Slone Epidemiology Center at Boston University, Boston, MA, USA
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Dezheng Huo
- Department of Public Health Sciences, The University of Chicago, Chicago, IL, USA
- Center for Clinical Cancer Genetics & Global Health, The University of Chicago, Chicago, IL, USA
| | - Kathryn L Lunetta
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Julie R Palmer
- Slone Epidemiology Center at Boston University, Boston, MA, USA.
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
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3
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del Valle Peña Colmenares J, García CC, Velásquez YJV, Pino LAC, Rodríguez ÁG, Rodríguez WJV, Vargas DJG, Herrera DJA. Is using the Gail model to calculate the risk of breast cancer in the Venezuelan population justified? Ecancermedicalscience 2023; 17:1590. [PMID: 37799948 PMCID: PMC10550297 DOI: 10.3332/ecancer.2023.1590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Indexed: 10/07/2023] Open
Abstract
Objective To evaluate the accuracy of the Gail model (GM) in women who already have a diagnosis of breast cancer (BC) from the Breast Pathology Service, Hospital Oncology Department of the Venezuelan Social Security Institute (SOH-IVSS) in the period 2004-2014. To compare the accuracy of the GM in women aged above and below 40 years with a diagnosis of BC. Method Descriptive, retrospective, cross-sectional, 830 records of patients diagnosed with BC were reviewed between 2004 and 2014. Results The mean age for diagnosis of the disease was 46 ± 13 years; menarche age was 13 years ± 2; age at first birth 22 ± 5 years, with a history of biopsy 32 ± 11, the percentage of relatives with a primary history of BC reported (PHBC) 9.3%. Only 41% of women with a diagnosis of BC reported Gail >1.67 (positive Gail). In the dichotomous logistic regression that related positive Gail with the independent variables, it was observed: greater probability of positive Gail if menarche age <11 years (p < 0.036), PHBC (p = 0.005), previous biopsy (p = 0.007), age at first birth 25-29 years (p = 0.019). When stratifying by age, unlike the bivariate analysis, women over 40 years of age are more likely to have a positive Gail in menarche age <11 years (p = 0.008), PHBC (p = 0.001), previous biopsy (p = 0.025) when compared with younger women, the age at first birth between 25 and 29 years was statistically significant for both groups; however, the probability was higher in younger women (p = 0.008). Conclusion There is no conclusive evidence to consider that the GM is applicable to Venezuelan women due to its low precision since it only identified 41% of the patients who had BC as high risk; however, when the factors are analysed separately, we found a higher probability of a positive Gail with statistical significance in EM <11 years, PHBC, previous biopsy and age at first birth 25-29 years; When stratifying by age, we observed that the age at first birth 25-29 years in women aged 40 or less increases the probability of a positive Gail. It is necessary to develop new risk assessment models that are adapted to our female population.
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Affiliation(s)
- Josepmilly del Valle Peña Colmenares
- Servicio Patología Mamaria del Servicio Oncológico Hospitalario (SOH), Instituto Venezolano del Seguro Social (IVSS), Caracas 1040, Venezuela
- https://orcid.org/0000-0002-1114-6289
| | - Carmen Cristina García
- Cátedra de Patología General y Fisiopatología, Escuela Luis Razetti, Facultad de Medicina, Caracas 1050, Venezuela
- https://orcid.org/0000-0002-7889-9445
| | - Yazmin José Velásquez Velásquez
- Servicio Patología Mamaria del Servicio Oncológico Hospitalario (SOH), Instituto Venezolano del Seguro Social (IVSS), Caracas 1040, Venezuela
- https://orcid.org/0000-0003-3307-2564
| | - Leider Arelis Campos Pino
- Servicio Patología Mamaria del Servicio Oncológico Hospitalario (SOH), Instituto Venezolano del Seguro Social (IVSS), Caracas 1040, Venezuela
- https://orcid.org/0000-0002-0907-8467
| | - Álvaro Gómez Rodríguez
- Servicio Patología Mamaria del Servicio Oncológico Hospitalario (SOH), Instituto Venezolano del Seguro Social (IVSS), Caracas 1040, Venezuela
- https://orcid.org/0000-0003-3740-0238
| | - Wladimir José Villegas Rodríguez
- Servicio Patología Mamaria del Servicio Oncológico Hospitalario (SOH), Instituto Venezolano del Seguro Social (IVSS), Caracas 1040, Venezuela
- https://orcid.org/0000-0001-8999-9751
| | - David José González Vargas
- Servicio Oncológico Hospitalario (SOH), Instituto Venezolano del Seguro Social (IVSS), Caracas 1040, Venezuela
- https://orcid.org/0000-0001-8071-3139
| | - Douglas José Angulo Herrera
- Escuela de Estadística y Ciencias Actuariales, Universidad Central de Venezuela, Caracas 1050, Venezuela
- https://orcid.org/0009-0003-5506-0297
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Okunola HL, Shuryak I, Repin M, Wu HC, Santella RM, Terry MB, Turner HC, Brenner DJ. Improved prediction of breast cancer risk based on phenotypic DNA damage repair capacity in peripheral blood B cells. RESEARCH SQUARE 2023:rs.3.rs-3093360. [PMID: 37461559 PMCID: PMC10350237 DOI: 10.21203/rs.3.rs-3093360/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
Background Standard Breast Cancer (BC) risk prediction models based only on epidemiologic factors generally have quite poor performance, and there have been a number of risk scores proposed to improve them, such as AI-based mammographic information, polygenic risk scores and pathogenic variants. Even with these additions BC risk prediction performance is still at best moderate. In that decreased DNA repair capacity (DRC) is a major risk factor for development of cancer, we investigated the potential to improve BC risk prediction models by including a measured phenotypic DRC assay. Methods Using blood samples from the Breast Cancer Family Registry we assessed the performance of phenotypic markers of DRC in 46 matched pairs of individuals, one from each pair with BC (with blood drawn before BC diagnosis) and the other from controls matched by age and time since blood draw. We assessed DRC in thawed cryopreserved peripheral blood mononuclear cells (PBMCs) by measuring γ-H2AX yields (a marker for DNA double-strand breaks) at multiple times from 1 to 20 hrs after a radiation challenge. The studies were performed using surface markers to discriminate between different PBMC subtypes. Results The parameter F res , the residual damage signal in PBMC B cells at 20 hrs post challenge, was the strongest predictor of breast cancer with an AUC (Area Under receiver-operator Curve) of 0.89 [95% Confidence Interval: 0.84-0.93] and a BC status prediction accuracy of 0.80. To illustrate the combined use of a phenotypic predictor with standard BC predictors, we combined F res in B cells with age at blood draw, and found that the combination resulted in significantly greater BC predictive power (AUC of 0.97 [95% CI: 0.94-0.99]), an increase of 13 percentage points over age alone. Conclusions If replicated in larger studies, these results suggest that inclusion of a fingerstick-based phenotypic DRC blood test has the potential to markedly improve BC risk prediction.
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Affiliation(s)
| | | | | | - Hui-Chen Wu
- Columbia University Mailman School of Public Health
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5
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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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Lawson-Michod KA, Watt MH, Grieshober L, Green SE, Karabegovic L, Derzon S, Owens M, McCarty RD, Doherty JA, Barnard ME. Pathways to ovarian cancer diagnosis: a qualitative study. BMC Womens Health 2022; 22:430. [PMCID: PMC9636716 DOI: 10.1186/s12905-022-02016-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 10/14/2022] [Indexed: 11/06/2022] Open
Abstract
Abstract
Background
Ovarian cancer is often diagnosed at a late stage, when survival is poor. Qualitative narratives of patients’ pathways to ovarian cancer diagnoses may identify opportunities for earlier cancer detection and, consequently, earlier stage at diagnosis.
Methods
We conducted semi-structured interviews of ovarian cancer patients and survivors (n = 14) and healthcare providers (n = 11) between 10/2019 and 10/2021. Interviews focused on the time leading up to an ovarian cancer diagnosis. Thematic analysis was conducted by two independent reviewers using a two-phase deductive and inductive coding approach. Deductive coding used a priori time intervals from the validated Model of Pathways to Treatment (MPT), including self-appraisal and management of symptoms, medical help-seeking, diagnosis, and pre-treatment. Inductive coding identified common themes within each stage of the MPT across patient and provider interviews.
Results
The median age at ovarian cancer diagnosis was 61.5 years (range, 29–78 years), and the majority of participants (11/14) were diagnosed with advanced-stage disease. The median time from first symptom to initiation of treatment was 2.8 months (range, 19 days to 4.7 years). The appraisal and help-seeking intervals contributed the greatest delays in time-to-diagnosis for ovarian cancer. Nonspecific symptoms, perceptions of health and aging, avoidant coping strategies, symptom embarrassment, and concerns about potential judgment from providers prolonged the appraisal and help-seeking intervals. Patients and providers also emphasized access to care, including financial access, as critical to a timely diagnosis.
Conclusion
Interventions are urgently needed to reduce ovarian cancer morbidity and mortality. Population-level screening remains unlikely to improve ovarian cancer survival, but findings from our study suggest that developing interventions to improve self-appraisal of symptoms and reduce barriers to help-seeking could reduce time-to-diagnosis for ovarian cancer. Affordability of care and insurance may be particularly important for ovarian cancer patients diagnosed in the United States.
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Vegunta S, Kling JM, Patel BK. Supplemental Cancer Screening for Women With Dense Breasts: Guidance for Health Care Professionals. Mayo Clin Proc 2021; 96:2891-2904. [PMID: 34686363 DOI: 10.1016/j.mayocp.2021.06.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 05/20/2021] [Accepted: 06/08/2021] [Indexed: 11/16/2022]
Abstract
Mammography is the standard for breast cancer screening. The sensitivity of mammography in identifying breast cancer, however, is reduced for women with dense breasts. Thirty-eight states have passed laws requiring that all women be notified of breast tissue density results in their mammogram report. The notification includes a statement that differs by state, encouraging women to discuss supplemental screening options with their health care professionals (HCPs). Several supplemental screening tests are available for women with dense breast tissue, but no established guidelines exist to direct HCPs in their recommendation of preferred supplemental screening test. Tailored screening, which takes into consideration the patient's mammographic breast density and lifetime breast cancer risk, can guide breast cancer screening strategies that are more comprehensive. This review describes the benefits and limitations of the various available supplemental screening tests to guide HCPs and patients in choosing the appropriate breast cancer screening.
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Affiliation(s)
- Suneela Vegunta
- Division of Women's Health Internal Medicine, Mayo Clinic, Scottsdale, AZ.
| | - Juliana M Kling
- Division of Women's Health Internal Medicine, Mayo Clinic, Scottsdale, AZ
| | - Bhavika K Patel
- Division of Breast Imaging, Mayo Clinic Hospital, Phoenix, AZ
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8
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Saleh B, Elhawary MA, Mohamed ME, Ali IN, El Zayat MS, Mohamed H. Gail model utilization in predicting breast cancer risk in Egyptian women: a cross-sectional study. Breast Cancer Res Treat 2021; 188:749-758. [PMID: 33852122 DOI: 10.1007/s10549-021-06200-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 03/16/2021] [Indexed: 12/18/2022]
Abstract
PURPOSE Herein, our purpose was to calculate the 5-year and lifetime risk of breast cancer and to assess new breast cancer potential contributors among Egyptian women utilizing the modified Gail model, while presenting a global comparison of risk assessment. METHODS This study included 7009 women from both urban and rural areas scattered across 40% of the Egyptian provinces. The 5-year risk categories were defined as low risk (≤ 1.66%) or high risk (> 1.66%), whereas the lifetime risk categories were defined as low risk (≤ 20%) or high risk (> 20%). Pearson's Chi-squared test was performed to determine the association between participants' characteristics and distinct risk categories. Binary logistic regression was carried out for correlation analysis. RESULTS The mean estimated risk for developing invasive breast cancer over 5 years was 0.86 (± 0.67), whereas the mean lifetime breast cancer risk score was 11.26 (± 5.7). Accordingly, only 614 (8.75%) and 470 (6.7%) women were categorized as individuals with high risk of breast cancer incidence in 5-year and lifetime, respectively. Only 192 participants (2.7%) conferred both high 5-year and high lifetime risk scores. Marital status, method of feeding, physical activity behavior, contraceptive use, menopause and number of children were found to have a statistically significant association with both 5-year and lifetime breast cancer risk categories. CONCLUSION We revealed that modified Gail model had a well-fitting and discrimination accuracy in Egyptian women when compared with other countries.
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Affiliation(s)
- Basem Saleh
- Medical Oncology Department, Tanta Cancer Center, Ministry of Health, Tanta, Gharbiah, Egypt.,Medical Oncology Department, Aswan Oncology Center, Ministry of Health, Aswân, Egypt
| | - Mohamed A Elhawary
- International Society of Pharmacovigilance - Egypt Chapter, Cairo, Egypt.,Faculty of Pharmacy, Ain Shams University, Cairo, Egypt
| | - Moataz E Mohamed
- Department of Pharmacy Practice, Faculty of Pharmacy, Helwan University, Cairo, Egypt
| | - Islam N Ali
- Faculty of Pharmacy, Ain Shams University, Cairo, Egypt.,University of Glasgow, Glasgow, Scotland, UK
| | - Menna S El Zayat
- Diagnostic Radiology Department, Al Helal Hospital - Specialized Medical Centers, Cairo, Egypt
| | - Hadeer Mohamed
- Oncology Department, Faculty of Medicine, Ain Shams University, Cairo, Egypt. .,Department of Clinical Oncology, Ain Shams University Hospitals, Cairo, Egypt.
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Jantzen R, Payette Y, de Malliard T, Labbé C, Noisel N, Broët P. Validation of breast cancer risk assessment tools on a French-Canadian population-based cohort. BMJ Open 2021; 11:e045078. [PMID: 33846154 PMCID: PMC8047995 DOI: 10.1136/bmjopen-2020-045078] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
OBJECTIVES Evaluate the accuracy of the Breast Cancer Risk Assessment Tool (BCRAT), International Breast Cancer Intervention Study risk evaluation tool (IBIS), Polygenic Risk Scores (PRS) and combined scores (BCRAT+PRS and IBIS +PRS) to predict the occurrence of invasive breast cancers at 5 years in a French-Canadian population. DESIGN Population-based cohort study. SETTING We used the population-based cohort CARTaGENE, composed of 43 037 Quebec residents aged between 40 and 69 years and broadly representative of the population recorded on the Quebec administrative health insurance registries. PARTICIPANTS 10 200 women recruited in 2009-2010 were included for validating BCRAT and IBIS and 4555 with genetic information for validating the PRS and combined scores. OUTCOME MEASURES We computed the absolute risks of breast cancer at 5 years using BCRAT, IBIS, four published PRS and combined models. We reported the overall calibration performance, goodness-of-fit test and discriminatory accuracy. RESULTS 131 (1.28%) women developed a breast cancer at 5 years for validating BCRAT and IBIS and 58 (1.27%) for validating PRS and combined scores. Median follow-up was 5 years. BCRAT and IBIS had an overall expected-to-observed ratio of 1.01 (0.85-1.19) and 1.02 (0.86-1.21) but with significant differences when partitioning by risk groups (p<0.05). IBIS' c-index was significantly higher than BCRAT (63.42 (59.35-67.49) vs 58.63 (54.05-63.21), p=0.013). PRS scores had a global calibration around 0.82, with a CI including one, and non-significant goodness-of-fit tests. PRS' c-indexes were non-significantly higher than BCRAT and IBIS, the highest being 64.43 (58.23-70.63). Combined models did not improve the results. CONCLUSIONS In this French-Canadian population-based cohort, BCRAT and IBIS have good mean calibration that could be improved for risk subgroups, and modest discriminatory accuracy. Despite this modest discriminatory power, these tools can be of interest for primary care physicians for delivering a personalised message to their high-risk patients, regarding screening and lifestyle counselling.
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Affiliation(s)
- Rodolphe Jantzen
- CARTaGENE, Research Center, CHU Sainte-Justine, Montreal, Quebec, Canada
- Université de Montréal, Montréal, Québec, Canada
| | - Yves Payette
- CARTaGENE, Research Center, CHU Sainte-Justine, Montreal, Quebec, Canada
| | | | - Catherine Labbé
- CARTaGENE, Research Center, CHU Sainte-Justine, Montreal, Quebec, Canada
| | - Nolwenn Noisel
- CARTaGENE, Research Center, CHU Sainte-Justine, Montreal, Quebec, Canada
- Université de Montréal, Montréal, Québec, Canada
| | - Philippe Broët
- CARTaGENE, Research Center, CHU Sainte-Justine, Montreal, Quebec, Canada
- Université de Montréal, Montréal, Québec, Canada
- CESP, INSERM, University Paris-Saclay, Villejuif, France
- Assistance Publique-Hôpitaux de Paris (AP-HP), Hôpitaux Universitaires Paris-Sud, Hôpital Paul Brousse, Villejuif, France
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Tanner JJ, Johnson AJ, Terry EL, Cardoso J, Garvan C, Staud R, Deutsch G, Deshpande H, Lai S, Addison A, Redden D, Goodin BR, Price CC, Fillingim RB, Sibille KT. Resilience, pain, and the brain: Relationships differ by sociodemographics. J Neurosci Res 2021; 99:1207-1235. [PMID: 33606287 DOI: 10.1002/jnr.24790] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 11/23/2020] [Accepted: 01/03/2021] [Indexed: 12/22/2022]
Abstract
Chronic musculoskeletal (MSK) pain is disabling to individuals and burdensome to society. A relationship between telomere length and resilience was reported in individuals with consideration for chronic pain intensity. While chronic pain associates with brain changes, little is known regarding the neurobiological interface of resilience. In a group of individuals with chronic MSK pain, we examined the relationships between a previously investigated resilience index, clinical pain and functioning measures, and pain-related brain structures, with consideration for sex and ethnicity/race. A cross-sectional analysis of 166 non-Hispanic Black and non-Hispanic White adults, 45-85 years of age with pain ≥ 1 body site (s) over the past 3 months was completed. Measures of clinical pain and functioning, biobehavioral and psychosocial resilience, and structural MRI were completed. Our findings indicate higher levels of resilience associate with lower levels of clinical pain and functional limitations. Significant associations between resilience, ethnicity/race, and/or sex, and pain-related brain gray matter structure were demonstrated in the right amygdaloid complex, bilateral thalamus, and postcentral gyrus. Our findings provide compelling evidence that in order to decipher the neurobiological code of chronic pain and related protective factors, it will be important to improve how chronic pain is phenotyped; to include an equal representation of females in studies including analyses stratifying by sex, and to consider other sociodemographic factors.
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Affiliation(s)
- Jared J Tanner
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
| | - Alisa J Johnson
- Pain Research & Intervention Center of Excellence, University of Florida, Gainesville, FL, USA.,Department of Community Dentistry and Behavioral Science, University of Florida, Gainesville, FL, USA
| | - Ellen L Terry
- Pain Research & Intervention Center of Excellence, University of Florida, Gainesville, FL, USA.,Department of Biobehavioral Nursing Science, College of Nursing, University of Florida, Gainesville, FL, USA
| | - Josue Cardoso
- Pain Research & Intervention Center of Excellence, University of Florida, Gainesville, FL, USA
| | - Cynthia Garvan
- Department of Anesthesiology, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Roland Staud
- Department of Medicine, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Georg Deutsch
- Department of Radiology, University of Alabama, Birmingham Medical Center, Birmingham, AL, USA
| | - Hrishikesh Deshpande
- Department of Radiology, University of Alabama, Birmingham Medical Center, Birmingham, AL, USA.,Department of Anesthesiology, University of Alabama, Birmingham Medical Center, Birmingham, AL, USA
| | - Song Lai
- Department of Radiation Oncology & CTSI Human Imaging Core, University of Florida, Gainesville, FL, USA
| | - Adriana Addison
- Department of Anesthesiology, University of Alabama, Birmingham Medical Center, Birmingham, AL, USA.,Department of Psychology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - David Redden
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Burel R Goodin
- Department of Anesthesiology, University of Alabama, Birmingham Medical Center, Birmingham, AL, USA.,Department of Psychology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Catherine C Price
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA.,Department of Anesthesiology, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Roger B Fillingim
- Pain Research & Intervention Center of Excellence, University of Florida, Gainesville, FL, USA.,Department of Community Dentistry and Behavioral Science, University of Florida, Gainesville, FL, USA
| | - Kimberly T Sibille
- Pain Research & Intervention Center of Excellence, University of Florida, Gainesville, FL, USA.,Department of Anesthesiology, College of Medicine, University of Florida, Gainesville, FL, USA.,Department of Aging and Geriatric Research, College of Medicine, UF Pain Research & Intervention Center of Excellence, University of Florida, Gainesville, FL, USA
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11
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Incorporating cortisol into the NAPLS2 individualized risk calculator for prediction of psychosis. Schizophr Res 2021; 227:95-100. [PMID: 33046334 PMCID: PMC8287972 DOI: 10.1016/j.schres.2020.09.022] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 08/10/2020] [Accepted: 09/24/2020] [Indexed: 11/21/2022]
Abstract
BACKGROUND Risk calculators are useful tools that can help clinicians and researchers better understand an individual's risk of conversion to psychosis. The North American Prodrome Longitudinal Study (NAPLS2) Individualized Risk Calculator has good predictive accuracy but could be potentially improved by the inclusion of a biomarker. Baseline cortisol, a measure of hypothalamic-pituitary-adrenal (HPA) axis functioning that is impacted by biological vulnerability to stress and exposure to environmental stressors, has been shown to be higher among individuals at clinical high-risk for psychosis (CHRP) who eventually convert to psychosis than those who do not. We sought to determine whether the addition of baseline cortisol to the NAPLS2 risk calculator improved the performance of the risk calculator. METHODS Participants were drawn from the NAPLS2 study. A subset of NAPLS2 participants provided salivary cortisol samples. A multivariate Cox proportional hazards regression evaluated the likelihood of an individual's eventual conversion to psychosis based on demographic and clinical variables in addition to baseline cortisol levels. RESULTS A total of 417 NAPLS2 participants provided salivary cortisol and were included in the analysis. Higher levels of cortisol were predictive of conversion to psychosis in a univariate model (C-index = 0.59, HR = 21.5, p-value = 0.004). The inclusion of cortisol in the risk calculator model resulted in a statistically significant improvement in performance from the original risk calculator model (C-index = 0.78, SE = 0.028). CONCLUSIONS Salivary cortisol is an inexpensive and non-invasive biomarker that could improve individual predictions about conversion to psychosis and treatment decisions for CHR-P individuals.
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Pal Choudhury P, Wilcox AN, Brook MN, Zhang Y, Ahearn T, Orr N, Coulson P, Schoemaker MJ, Jones ME, Gail MH, Swerdlow AJ, Chatterjee N, Garcia-Closas M. Comparative Validation of Breast Cancer Risk Prediction Models and Projections for Future Risk Stratification. J Natl Cancer Inst 2020; 112:278-285. [PMID: 31165158 DOI: 10.1093/jnci/djz113] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Revised: 01/31/2019] [Accepted: 05/29/2019] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND External validation of risk models is critical for risk-stratified breast cancer prevention. We used the Individualized Coherent Absolute Risk Estimation (iCARE) as a flexible tool for risk model development and comparative model validation and to make projections for population risk stratification. METHODS Performance of two recently developed models, one based on the Breast and Prostate Cancer Cohort Consortium analysis (iCARE-BPC3) and another based on a literature review (iCARE-Lit), were compared with two established models (Breast Cancer Risk Assessment Tool and International Breast Cancer Intervention Study Model) based on classical risk factors in a UK-based cohort of 64 874 white non-Hispanic women (863 patients) age 35-74 years. Risk projections in a target population of US white non-Hispanic women age 50-70 years assessed potential improvements in risk stratification by adding mammographic breast density (MD) and polygenic risk score (PRS). RESULTS The best calibrated models were iCARE-Lit (expected to observed number of cases [E/O] = 0.98, 95% confidence interval [CI] = 0.87 to 1.11) for women younger than 50 years, and iCARE-BPC3 (E/O = 1.00, 95% CI = 0.93 to 1.09) for women 50 years or older. Risk projections using iCARE-BPC3 indicated classical risk factors can identify approximately 500 000 women at moderate to high risk (>3% 5-year risk) in the target population. Addition of MD and a 313-variant PRS is expected to increase this number to approximately 3.5 million women, and among them, approximately 153 000 are expected to develop invasive breast cancer within 5 years. CONCLUSIONS iCARE models based on classical risk factors perform similarly to or better than BCRAT or IBIS in white non-Hispanic women. Addition of MD and PRS can lead to substantial improvements in risk stratification. However, these integrated models require independent prospective validation before broad clinical applications.
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Affiliation(s)
| | - Amber N Wilcox
- Johns Hopkins University, Baltimore, MD.,Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda
| | | | - Yan Zhang
- Department of Biostatistics, Bloomberg School of Public Health
| | - Thomas Ahearn
- Johns Hopkins University, Baltimore, MD.,Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda
| | - Nick Orr
- Department of Biostatistics, Bloomberg School of Public Health.,Department of Oncology, School of Medicine.,Division of Breast Cancer Research, The Institute of Cancer Research, London, UK.,Centre for Cancer Research and Cell Biology, Queen's University Belfast, Belfast, UK
| | | | | | | | - Mitchell H Gail
- Johns Hopkins University, Baltimore, MD.,Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda
| | - Anthony J Swerdlow
- Division of Genetics and Epidemiology.,Division of Breast Cancer Research, The Institute of Cancer Research, London, UK
| | | | - Montserrat Garcia-Closas
- Johns Hopkins University, Baltimore, MD.,Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda
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Vilmun BM, Vejborg I, Lynge E, Lillholm M, Nielsen M, Nielsen MB, Carlsen JF. Impact of adding breast density to breast cancer risk models: A systematic review. Eur J Radiol 2020; 127:109019. [DOI: 10.1016/j.ejrad.2020.109019] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Revised: 04/10/2020] [Accepted: 04/13/2020] [Indexed: 01/19/2023]
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Assessing breast cancer risk within the general screening population: developing a breast cancer risk model to identify higher risk women at mammographic screening. Eur Radiol 2020; 30:5417-5426. [PMID: 32358648 DOI: 10.1007/s00330-020-06901-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Revised: 04/07/2020] [Accepted: 04/17/2020] [Indexed: 12/19/2022]
Abstract
OBJECTIVES To develop a breast cancer risk model to identify women at mammographic screening who are at higher risk of breast cancer within the general screening population. METHODS This retrospective nested case-control study used data from a population-based breast screening program (2009-2015). All women aged 40-75 diagnosed with screen-detected or interval breast cancer (n = 1882) were frequency-matched 3:1 on age and screen-year with women without screen-detected breast cancer (n = 5888). Image-derived risk factors from the screening mammogram (percent mammographic density [PMD], breast volume, age) were combined with core biopsy history, first-degree family history, and other clinical risk factors in risk models. Model performance was assessed using the area under the receiver operating characteristic curve (AUC). Classifiers assigning women to low- versus high-risk deciles were derived from risk models. Agreement between classifiers was assessed using a weighted kappa. RESULTS The AUC was 0.597 for a risk model including only image-derived risk factors. The successive addition of core biopsy and family history significantly improved performance (AUC = 0.660, p < 0.001 and AUC = 0.664, p = 0.04, respectively). Adding the three remaining risk factors did not further improve performance (AUC = 0.665, p = 0.45). There was almost perfect agreement (kappa = 0.97) between risk assessments based on a classifier derived from image-derived risk factors, core biopsy, and family history compared with those derived from a model including all available risk factors. CONCLUSIONS Women in the general screening population can be risk-stratified at time of screen using a simple model based on age, PMD, breast volume, and biopsy and family history. KEY POINTS • A breast cancer risk model based on three image-derived risk factors as well as core biopsy and first-degree family history can provide current risk estimates at time of screen. • Risk estimates generated from a combination of image-derived risk factors, core biopsy history, and first-degree family history may be more valid than risk estimates that rely on extensive self-reported risk factors. • A simple breast cancer risk model can avoid extensive clinical risk factor data collection.
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15
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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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16
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Validation of two US breast cancer risk prediction models in German women. Cancer Causes Control 2020; 31:525-536. [PMID: 32253639 DOI: 10.1007/s10552-020-01272-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2019] [Accepted: 01/31/2020] [Indexed: 12/17/2022]
Abstract
PURPOSE There are no models for German women that predict absolute risk of invasive breast cancer (BC), i.e., the probability of developing BC over a prespecified time period, given a woman's age and characteristics, while accounting for competing risks. We thus validated two absolute BC risk models (BCRAT, BCRmod) developed for US women in German women. BCRAT uses a woman's medical, reproductive, and BC family history; BCRmod adds modifiable risk factors (body mass index, hormone replacement therapy and alcohol use). METHODS We assessed model calibration by comparing observed BC numbers (O) to expected numbers (E) computed from BCRmod/BCRAT for German women enrolled in the prospective European Prospective Investigation into Cancer and Nutrition (EPIC), and after updating the models with German BC incidence/competing mortality rates. We also compared 1-year BC risk predicted for all German women using the German Health Interview and Examination Survey for Adults (DEGS) with overall German BC incidence. Discriminatory performance was quantified by the area under the receiver operator characteristics curve (AUC). RESULTS Among 22,098 EPIC-Germany women aged 40+ years, 745 BCs occurred (median follow-up: 11.9 years). Both models had good calibration for total follow-up, EBCRmod/O = 1.08 (95% confidence interval: 0.95-1.21), and EBCRAT/O = 0.99(0.87-1.11), and over 5 years. Compared to German BC incidence rates, both models somewhat overestimated 1-year risk for women aged 55+ and 70+ years. For total follow-up, AUCBCRmod = 0.61(0.58-0.63) and AUCBCRAT = 0.58(0.56-0.61), with similar values for 5-year follow-up. CONCLUSION US BC risk models showed adequate calibration in German women. Discriminatory performance was comparable to that in US women. These models thus could be applied for risk prediction in German women.
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An Expanded Agenda for the Primary Prevention of Breast Cancer: Charting a Course for the Future. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17030714. [PMID: 31979073 PMCID: PMC7036784 DOI: 10.3390/ijerph17030714] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Revised: 01/05/2020] [Accepted: 01/09/2020] [Indexed: 02/07/2023]
Abstract
Advances in breast cancer science, early detection, and treatment have resulted in improvements in breast cancer survival but not in breast cancer incidence. After skin cancer, breast cancer is the most common cancer diagnosis in the United States. Each year, nearly a quarter million U.S. women receive a breast cancer diagnosis, and the number continues to rise each year with the growth in the population of older women. Although much remains to be understood about breast cancer origins and prevention, action can be taken on the existing scientific knowledge to address the systemic factors that drive breast cancer risk at the population level. The California Breast Cancer Research Program funded a team at Breast Cancer Prevention Partners (BCPP) to convene leaders in advocacy, policy, and research related to breast cancer prevention from across the state of California. The objective was the development of a strategic plan to direct collective efforts toward specific and measurable objectives to reduce the incidence of breast cancer. The structured, innovative approach used by BCPP to integrate scientific evidence with community perspectives provides a model for other states to consider, to potentially change the future trajectory of breast cancer incidence in the United States.
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Dench E, Bond-Smith D, Darcey E, Lee G, Aung YK, Chan A, Cuzick J, Ding ZY, Evans CF, Harvey J, Highnam R, Hsieh MK, Kontos D, Li S, Mariapun S, Nickson C, Nguyen TL, Pertuz S, Procopio P, Rajaram N, Repich K, Tan M, Teo SH, Trinh NH, Ursin G, Wang C, Dos-Santos-Silva I, McCormack V, Nielsen M, Shepherd J, Hopper JL, Stone J. Measurement challenge: protocol for international case-control comparison of mammographic measures that predict breast cancer risk. BMJ Open 2019; 9:e031041. [PMID: 31892647 PMCID: PMC6955467 DOI: 10.1136/bmjopen-2019-031041] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Revised: 11/29/2019] [Accepted: 12/04/2019] [Indexed: 11/12/2022] Open
Abstract
INTRODUCTION For women of the same age and body mass index, increased mammographic density is one of the strongest predictors of breast cancer risk. There are multiple methods of measuring mammographic density and other features in a mammogram that could potentially be used in a screening setting to identify and target women at high risk of developing breast cancer. However, it is unclear which measurement method provides the strongest predictor of breast cancer risk. METHODS AND ANALYSIS The measurement challenge has been established as an international resource to offer a common set of anonymised mammogram images for measurement and analysis. To date, full field digital mammogram images and core data from 1650 cases and 1929 controls from five countries have been collated. The measurement challenge is an ongoing collaboration and we are continuing to expand the resource to include additional image sets across different populations (from contributors) and to compare additional measurement methods (by challengers). The intended use of the measurement challenge resource is for refinement and validation of new and existing mammographic measurement methods. The measurement challenge resource provides a standardised dataset of mammographic images and core data that enables investigators to directly compare methods of measuring mammographic density or other mammographic features in case/control sets of both raw and processed images, for the purposes of the comparing their predictions of breast cancer risk. ETHICS AND DISSEMINATION Challengers and contributors are required to enter a Research Collaboration Agreement with the University of Melbourne prior to participation in the measurement challenge. The Challenge database of collated data and images are stored in a secure data repository at the University of Melbourne. Ethics approval for the measurement challenge is held at University of Melbourne (HREC ID 0931343.3).
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Affiliation(s)
- Evenda Dench
- Centre for Genetic Origins of Health and Disease, University of Western Australia, Crawley, Western Australia, Australia
| | - Daniela Bond-Smith
- School of Population and Global Health, University of Western Australia, Crawley, Western Australia, Australia
| | - Ellie Darcey
- Centre for Genetic Origins of Health and Disease, University of Western Australia, Crawley, Western Australia, Australia
| | - Grant Lee
- Centre for Epidemiology & Biostatistics, The University of Melbourne School of Population and Global Health, Melbourne, Victoria, Australia
| | - Ye K Aung
- Centre for Epidemiology & Biostatistics, The University of Melbourne School of Population and Global Health, Melbourne, Victoria, Australia
| | - Ariane Chan
- Science and Technology, Volpara Health Technologies, Wellington, New Zealand
| | - Jack Cuzick
- Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Queen Mary University of London, London, UK
| | - Ze Y Ding
- Electrical and Computer Systems Engineering, School of Engineering, Monash University - Malaysia Campus, Bandar Sunway, Selangor, Malaysia
| | - Chris F Evans
- Centre for Epidemiology & Biostatistics, The University of Melbourne School of Population and Global Health, Melbourne, Victoria, Australia
| | - Jennifer Harvey
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia, USA
| | - Ralph Highnam
- Science and Technology, Volpara Health Technologies, Wellington, New Zealand
| | - Meng-Kang Hsieh
- Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Despina Kontos
- Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Shuai Li
- Centre for Epidemiology & Biostatistics, The University of Melbourne School of Population and Global Health, Melbourne, Victoria, Australia
| | - Shivaani Mariapun
- Cancer Research Malaysia, Subang Jaya, Selangor, Malaysia
- Department of Applied Mathematics, Faculty of Engineering, University of Nottingham - Malaysia Campus, Semenyih, Selangor, Malaysia
| | - Carolyn Nickson
- Centre for Epidemiology & Biostatistics, The University of Melbourne School of Population and Global Health, Melbourne, Victoria, Australia
- Cancer Research Division, Cancer Council New South Wales, Sydney, New South Wales, Austalia
| | - Tuong L Nguyen
- Centre for Epidemiology & Biostatistics, The University of Melbourne School of Population and Global Health, Melbourne, Victoria, Australia
| | - Said Pertuz
- Laboratory of Signal Processing, Tampere University of Technology, Tampere, Pirkanmaa, Finland
- Connectivity and Signal Processing group, Universidad Industrial de Santander, Bucaramanga, Colombia
| | - Pietro Procopio
- Centre for Epidemiology & Biostatistics, The University of Melbourne School of Population and Global Health, Melbourne, Victoria, Australia
- Cancer Research Division, Cancer Council New South Wales, Sydney, New South Wales, Austalia
| | - Nadia Rajaram
- Cancer Research Malaysia, Subang Jaya, Selangor, Malaysia
- Department of Applied Mathematics, Faculty of Engineering, University of Nottingham - Malaysia Campus, Semenyih, Selangor, Malaysia
| | - Kathy Repich
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia, USA
| | - Maxine Tan
- Electrical and Computer Systems Engineering, School of Engineering, Monash University - Malaysia Campus, Bandar Sunway, Selangor, Malaysia
- School of Electrical and Computer Engineering, University of Oklahoma Norman Campus, Norman, Oklahoma, USA
| | - Soo-Hwang Teo
- Cancer Research Malaysia, Subang Jaya, Selangor, Malaysia
| | - Nhut Ho Trinh
- Centre for Epidemiology & Biostatistics, The University of Melbourne School of Population and Global Health, Melbourne, Victoria, Australia
| | | | - Chao Wang
- Faculty of Health, Social Care and Education, Kingston University and St George's, University of London, Kingston-Upon-Thames, London, UK
| | - Isabel Dos-Santos-Silva
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, London, UK
| | - Valerie McCormack
- Section of Environment and Radiation, International Agency for Research on Cancer, IARC, Lyon, France
| | | | - John Shepherd
- University of Hawai'i Cancer Center, Honolulu, Hawaii, USA
| | - John L Hopper
- Centre for Epidemiology & Biostatistics, The University of Melbourne School of Population and Global Health, Melbourne, Victoria, Australia
| | - Jennifer Stone
- Centre for Genetic Origins of Health and Disease, University of Western Australia, Crawley, Western Australia, Australia
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