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Klassen CL, Viers LD, Ghosh K. Following the High-Risk Patient: Breast Cancer Risk-Based Screening. Ann Surg Oncol 2024; 31:3154-3159. [PMID: 38302622 DOI: 10.1245/s10434-024-14957-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 01/11/2024] [Indexed: 02/03/2024]
Abstract
Breast cancer (BC) is the most common cancer occurring in women in the USA today, and accounts for more than 40,000 deaths annually (Giaquinto in CA Cancer J Clin 72: 524-541, 2022). While breast cancer survival has improved over the past decades, incidence has increased, and diagnoses are being made at younger ages. This emphasizes the importance of risk evaluation, accurate prediction, and effective mitigation and risk reduction strategies. Enhanced screening can help detect cancers at an earlier stage, thus improving morbidity and mortality. This review addresses the recognition of women at high-risk for BC and monitoring strategies for those at high risk.
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Affiliation(s)
- Christine L Klassen
- Mayo School of Graduate Medical Education, Mayo Clinic- Rochester, Rochester, MN, USA.
| | - Lyndsay D Viers
- Mayo School of Graduate Medical Education, Mayo Clinic- Rochester, Rochester, MN, USA
| | - Karthik Ghosh
- Mayo School of Graduate Medical Education, Mayo Clinic- Rochester, Rochester, MN, USA
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Mertens E, Barrenechea-Pulache A, Sagastume D, Vasquez MS, Vandevijvere S, Peñalvo JL. Understanding the contribution of lifestyle in breast cancer risk prediction: a systematic review of models applicable to Europe. BMC Cancer 2023; 23:687. [PMID: 37480028 PMCID: PMC10360320 DOI: 10.1186/s12885-023-11174-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 07/12/2023] [Indexed: 07/23/2023] Open
Abstract
BACKGROUND Breast cancer (BC) is a significant health concern among European women, with the highest prevalence rates among all cancers. Existing BC prediction models account for major risks such as hereditary, hormonal and reproductive factors, but research suggests that adherence to a healthy lifestyle can reduce the risk of developing BC to some extent. Understanding the influence and predictive role of lifestyle variables in current risk prediction models could help identify actionable, modifiable, targets among high-risk population groups. PURPOSE To systematically review population-based BC risk prediction models applicable to European populations and identify lifestyle predictors and their corresponding parameter values for a better understanding of their relative contribution to the prediction of incident BC. METHODS A systematic review was conducted in PubMed, Embase and Web of Science from January 2000 to August 2021. Risk prediction models were included if (i) developed and/or validated in adult cancer-free women in Europe, (ii) based on easily ascertained information, and (iii) reported models' final predictors. To investigate further the comparability of lifestyle predictors across models, estimates were standardised into risk ratios and visualised using forest plots. RESULTS From a total of 49 studies, 33 models were developed and 22 different existing models, mostly from Gail (22 studies) and Tyrer-Cuzick and co-workers (12 studies) were validated or modified for European populations. Family history of BC was the most frequently included predictor (31 models), while body mass index (BMI) and alcohol consumption (26 and 21 models, respectively) were the lifestyle predictors most often included, followed by smoking and physical activity (7 and 6 models respectively). Overall, for lifestyle predictors, their modest predictive contribution was greater for riskier lifestyle levels, though highly variable model estimates across different models. CONCLUSIONS Given the increasing BC incidence rates in Europe, risk models utilising readily available risk factors could greatly aid in widening the population coverage of screening efforts, while the addition of lifestyle factors could help improving model performance and serve as intervention targets of prevention programmes.
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Affiliation(s)
- Elly Mertens
- Unit of Non-Communicable Diseases, Department of Public Health, Institute of Tropical Medicine, Nationalestraat 155, 2000, Antwerp, Belgium.
| | - Antonio Barrenechea-Pulache
- Unit of Non-Communicable Diseases, Department of Public Health, Institute of Tropical Medicine, Nationalestraat 155, 2000, Antwerp, Belgium
| | - Diana Sagastume
- Unit of Non-Communicable Diseases, Department of Public Health, Institute of Tropical Medicine, Nationalestraat 155, 2000, Antwerp, Belgium
| | - Maria Salve Vasquez
- Health Information, Scientific Institute of Public Health (Sciensano), Brussels, Belgium
| | - Stefanie Vandevijvere
- Health Information, Scientific Institute of Public Health (Sciensano), Brussels, Belgium
| | - José L Peñalvo
- Unit of Non-Communicable Diseases, Department of Public Health, Institute of Tropical Medicine, Nationalestraat 155, 2000, Antwerp, Belgium
- Global Health Institute, University of Antwerp, Antwerp, Belgium
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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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Shvetsov YB, Wilkens LR, White KK, Chong M, Buyum A, Badowski G, Leon Guerrero RT, Novotny R. Prediction of breast cancer risk among women of the Mariana Islands: the BRISK retrospective case-control study. BMJ Open 2022; 12:e061205. [PMID: 36600333 PMCID: PMC9743286 DOI: 10.1136/bmjopen-2022-061205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVES To develop a breast cancer risk prediction model for Chamorro and Filipino women of the Mariana Islands and compare its performance to that of the Breast Cancer Risk Assessment Tool (BCRAT). DESIGN Case-control study. SETTING Clinics/facilities and other community-based settings on Guam and Saipan (Northern Mariana Islands). PARTICIPANTS 245 women (87 breast cancer cases and 158 controls) of Chamorro or Filipino ethnicity, age 25-80 years, with no prior history of cancer (other than skin cancer), residing on Guam or Saipan for at least 5 years. PRIMARY AND SECONDARY OUTCOME MEASURES Breast cancer risk models were constructed using combinations of exposures previously identified to affect breast cancer risk in this population, population breast cancer incidence rates and all-cause mortality rates for Guam. RESULTS Models using ethnic-specific relative risks performed better than those with relative risks estimated from all women. The model with the best performance among both ethnicities (the Breast Cancer Risk Model (BRISK) model; area under the receiver operating characteristic curve (AUC): 0.64 and 0.67 among Chamorros and Filipinos, respectively) included age at menarche, age at first live birth, number of relatives with breast cancer and waist circumference. The 10-year breast cancer risk predicted by the BRISK model was 1.28% for Chamorros and 0.89% for Filipinos. Performance of the BCRAT was modest among both Chamorros (AUC: 0.60) and Filipinos (AUC: 0.55), possibly due to incomplete information on BCRAT risk factors. CONCLUSIONS The ability to develop breast cancer risk models for Mariana Islands women is constrained by the small population size and limited availability of health services and data. Nonetheless, we have demonstrated that breast cancer risk prediction models with adequate discriminatory performance can be built for small populations such as in the Mariana Islands. Anthropometry, in particular waist circumference, was important for estimating breast cancer risk in this population.
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Affiliation(s)
- Yurii B Shvetsov
- Cancer Center, University of Hawai'i at Mānoa, Honolulu, Hawaii, USA
| | - Lynne R Wilkens
- Cancer Center, University of Hawai'i at Mānoa, Honolulu, Hawaii, USA
| | - Kami K White
- Cancer Center, University of Hawai'i at Mānoa, Honolulu, Hawaii, USA
| | - Marie Chong
- Cancer Center, University of Hawai'i at Mānoa, Honolulu, Hawaii, USA
| | - Arielle Buyum
- AB Consulting, LLC, Saipan, Northern Mariana Islands
| | - Grazyna Badowski
- College of Natural and Applied Sciences, University of Guam, Mangilao, Guam
| | | | - Rachel Novotny
- College of Tropical Agriculture and Human Resources, University of Hawai'i at Manoa, Honolulu, Hawaii, USA
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Smith CDL, McMahon AD, Ross A, Inman GJ, Conway DI. Risk prediction models for head and neck cancer: A rapid review. Laryngoscope Investig Otolaryngol 2022; 7:1893-1908. [PMID: 36544947 PMCID: PMC9764804 DOI: 10.1002/lio2.982] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 10/26/2022] [Accepted: 11/11/2022] [Indexed: 11/29/2022] Open
Abstract
Background Cancer risk assessment models are used to support prevention and early detection. However, few models have been developed for head and neck cancer (HNC). Methods A rapid review of Embase and MEDLINE identified n = 3045 articles. Following dual screening, n = 14 studies were included. Quality appraisal using the PROBAST (risk of bias) instrument was conducted, and a narrative synthesis was performed to identify the best performing models in terms of risk factors and designs. Results Six of the 14 models were assessed as "high" quality. Of these, three had high predictive performance achieving area under curve values over 0.8 (0.87-0.89). The common features of these models were their inclusion of predictors carefully tailored to the target population/anatomical subsite and development with external validation. Conclusions Some existing models do possess the potential to identify and stratify those at risk of HNC but there is scope for improvement.
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Affiliation(s)
- Craig D. L. Smith
- School of Medicine, Dentistry, and NursingUniversity of GlasgowGlasgowUK
- Institute of Cancer SciencesUniversity of GlasgowGlasgowUK
| | - Alex D. McMahon
- School of Medicine, Dentistry, and NursingUniversity of GlasgowGlasgowUK
| | - Alastair Ross
- School of Medicine, Dentistry, and NursingUniversity of GlasgowGlasgowUK
| | - Gareth J. Inman
- Institute of Cancer SciencesUniversity of GlasgowGlasgowUK
- Cancer Research UK Beatson InstituteGlasgowUK
| | - David I. Conway
- School of Medicine, Dentistry, and NursingUniversity of GlasgowGlasgowUK
- Institute of Cancer SciencesUniversity of GlasgowGlasgowUK
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Nadeau M, Best LG, Klug MG, Wise K. Exploring Clinical Risk Factors for Breast Cancer Among American Indian Women. Front Public Health 2022; 10:840280. [PMID: 35784224 PMCID: PMC9247609 DOI: 10.3389/fpubh.2022.840280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 05/16/2022] [Indexed: 11/16/2022] Open
Abstract
Objective Very little is known about the breast cancer risk profile among American Indian women. Previous research shows that the proportion of American Indian/Alaska Native women with baseline characteristics (commonly known breast cancer risk factors) differs from other ethnicities. This retrospective case control study was designed to the explore the association of these factors among American Indian women with and without breast cancer. Methods Cases and controls were retrospectively selected from the medical records of American Indian women who obtained their health care from Quentin N. Burdick Memorial Health Care Facility (Indian Health Service) in Belcourt, ND. For each woman with breast cancer (n = 141), two controls were selected when possible (n = 278). Risk factors examined included woman's age, age at first live birth, age of menarche, the number of previous benign breast biopsies, the total number of first-degree relatives with breast cancer, body mass index and parity. Odds ratios and 95% confidence intervals were calculated using logistic regression. Results Many of the associations found among American Indian women who obtained their health care from Quentin N. Burdick Memorial Health Care Facility (Indian Health Service) in Belcourt, ND, between risk factors commonly identified in other populations and breast cancer were weakly positive. Nulliparity was the only risk factor to consistently show a positive significant association (OR = 2.87, 95% CI 1.16–0.7.12). Conclusion Disparities in breast cancer incidence, mortality and screening among Northern Plains American Indian emphasize the need to better understand the risk factors associated with breast cancer in this population. Based on the results of this study, the value of current risk prediction models in American Indian communities is uncertain and clinicians should be cautious in using these models to inform American Indian patients of their risk for breast cancer.
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Affiliation(s)
- Melanie Nadeau
- Department of Indigenous Health, School of Medicine and Health Sciences, University of North Dakota, Grand Forks, ND, United States
- *Correspondence: Melanie Nadeau
| | - Lyle G. Best
- Turtle Mountain Community College, Belcourt, ND, United States
| | - Marilyn G. Klug
- Department of Population Health, School of Medicine and Health Sciences, University of North Dakota, Grand Forks, ND, United States
| | - Kathryn Wise
- Department of Population Health, School of Medicine and Health Sciences, University of North Dakota, Grand Forks, ND, United States
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Gu J, Chen R, Wang SM, Li M, Fan Z, Li X, Zhou J, Sun K, Wei W. Prediction models for gastric cancer risk in the general population: a systematic review. Cancer Prev Res (Phila) 2022; 15:309-318. [PMID: 35017181 DOI: 10.1158/1940-6207.capr-21-0426] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 11/15/2021] [Accepted: 01/07/2022] [Indexed: 11/16/2022]
Abstract
Risk prediction models for gastric cancer (GC) could identify high-risk individuals in the general population. The objective of this study was to systematically review the available evidence about the construction and verification of GC predictive models. We searched PubMed, Embase, and Cochrane Library databases for articles that developed or validated GC risk prediction models up to November 2021. Data extracted included study characteristics, predictor selection, missing data, and evaluation metrics. Risk of bias (ROB) was assessed using the Prediction model study Risk Of Bias Assessment Tool (PROBAST). We identified a total of 12 original risk prediction models that fulfilled the criteria for analysis. The area under the receiver operating characteristic curve ranged from 0.73 to 0.93 in derivation sets (n=6), 0.68 to 0.90 in internal validation sets (n=5), 0.71 to 0.92 in external validation sets (n=7). The higher-performing models usually include age, salt preference, Helicobacter pylori, smoking, BMI, family history, pepsinogen and sex. According to PROBAST, at least one domain with a high ROB was present in all studies mainly due to methodologic limitations in the analysis domain. In conclusion, although some risk prediction models including similar predictors have displayed sufficient discriminative abilities, many have a high ROB due to methodological limitations and are not externally validated efficiently. Future prediction models should adherence to well-established standards and guidelines to benefit GC screening.
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Affiliation(s)
- Jianhua Gu
- National Central Cancer Registry, National Cancer Center/ National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College
| | - Ru Chen
- National Central Cancer Registry, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College
| | - Shao-Ming Wang
- National Central Cancer Registry Office, National Cancer Center/National Clinical Research Center for Cancer/ Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College
| | - Minjuan Li
- National Central Cancer Registry, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College
| | - Zhiyuan Fan
- National Cancer Registry Office, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College
| | - Xinqing Li
- 1. Office of National Central Cancer Registry, Cancer Institute/Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Jiachen Zhou
- Department of Epidemiology and Biostatistics, School of Public Health, Xi'an Jiaotong University Health Science Center
| | - Kexin Sun
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Science and Peking Union Medical College
| | - Wenqiang Wei
- National Central Cancer Registry, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College
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Lenkinski RE. Improving the Accuracy of Screening Dense Breasted Women for Breast Cancer By Combining Clinically Based Risk Assessment Models with Ultrasound Imaging. Acad Radiol 2022; 29 Suppl 1:S8-S9. [PMID: 34702674 DOI: 10.1016/j.acra.2021.09.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 09/23/2021] [Indexed: 11/25/2022]
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McCarthy AM, Liu Y, Ehsan S, Guan Z, Liang J, Huang T, Hughes K, Semine A, Kontos D, Conant E, Lehman C, Armstrong K, Braun D, Parmigiani G, Chen J. Validation of Breast Cancer Risk Models by Race/Ethnicity, Family History and Molecular Subtypes. Cancers (Basel) 2021; 14:45. [PMID: 35008209 PMCID: PMC8750569 DOI: 10.3390/cancers14010045] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 12/09/2021] [Accepted: 12/20/2021] [Indexed: 12/28/2022] Open
Abstract
(1) Background: The purpose of this study is to compare the performance of four breast cancer risk prediction models by race, molecular subtype, family history of breast cancer, age, and BMI. (2) Methods: Using a cohort of women aged 40-84 without prior history of breast cancer who underwent screening mammography from 2006 to 2015, we generated breast cancer risk estimates using the Breast Cancer Risk Assessment tool (BCRAT), BRCAPRO, Breast Cancer Surveillance Consortium (BCSC) and combined BRCAPRO+BCRAT models. Model calibration and discrimination were compared using observed-to-expected ratios (O/E) and the area under the receiver operator curve (AUC) among patients with at least five years of follow-up. (3) Results: We observed comparable discrimination and calibration across models. There was no significant difference in model performance between Black and White women. Model discrimination was poorer for HER2+ and triple-negative subtypes compared with ER/PR+HER2-. The BRCAPRO+BCRAT model displayed improved calibration and discrimination compared to BRCAPRO among women with a family history of breast cancer. Across models, discriminatory accuracy was greater among obese than non-obese women. When defining high risk as a 5-year risk of 1.67% or greater, models demonstrated discordance in 2.9% to 19.7% of patients. (4) Conclusions: Our results can inform the implementation of risk assessment and risk-based screening among women undergoing screening mammography.
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Affiliation(s)
- Anne Marie McCarthy
- Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA; (Y.L.); (S.E.); (J.C.)
| | - Yi Liu
- Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA; (Y.L.); (S.E.); (J.C.)
| | - Sarah Ehsan
- Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA; (Y.L.); (S.E.); (J.C.)
| | - Zoe Guan
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Harvard University, Boston, MA 02115, USA; (Z.G.); (J.L.); (T.H.); (D.B.); (G.P.)
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Jane Liang
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Harvard University, Boston, MA 02115, USA; (Z.G.); (J.L.); (T.H.); (D.B.); (G.P.)
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Theodore Huang
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Harvard University, Boston, MA 02115, USA; (Z.G.); (J.L.); (T.H.); (D.B.); (G.P.)
| | - Kevin Hughes
- Massachusetts General Hospital, Boston, MA 02114, USA;
| | - Alan Semine
- Newton Wellesley Hospital, Newton, MA 02462, USA; (A.S.); (C.L.); (K.A.)
| | - Despina Kontos
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA; (D.K.); (E.C.)
| | - Emily Conant
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA; (D.K.); (E.C.)
| | - Constance Lehman
- Newton Wellesley Hospital, Newton, MA 02462, USA; (A.S.); (C.L.); (K.A.)
| | - Katrina Armstrong
- Newton Wellesley Hospital, Newton, MA 02462, USA; (A.S.); (C.L.); (K.A.)
| | - Danielle Braun
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Harvard University, Boston, MA 02115, USA; (Z.G.); (J.L.); (T.H.); (D.B.); (G.P.)
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Giovanni Parmigiani
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Harvard University, Boston, MA 02115, USA; (Z.G.); (J.L.); (T.H.); (D.B.); (G.P.)
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Jinbo Chen
- Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA; (Y.L.); (S.E.); (J.C.)
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Chlebowski RT, Aragaki AK, Pan K. Breast Cancer Prevention: Time for Change. JCO Oncol Pract 2021; 17:709-716. [PMID: 34319769 PMCID: PMC8677965 DOI: 10.1200/op.21.00343] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 06/24/2021] [Accepted: 07/06/2021] [Indexed: 11/20/2022] Open
Abstract
Agency breast cancer prevention guidelines for other than hereditary cancers have not materially changed in 20 years; endocrine-targeted agents (then, tamoxifen; now, adding raloxifene and aromatase inhibitors) reduce good prognosis estrogen receptor (ER)-positive, progesterone receptor (PR)-positive cancers without reducing deaths from breast cancer. Across three tamoxifen placebo-controlled prevention trials (N = 23,360) begun almost 30 years ago, although there were 226 fewer breast cancer cases, there were nine more deaths from breast cancer in the tamoxifen groups. Following clinical advances, currently more than half of breast cancer cases are solved problems with extremely low risk of death. As endocrine-targeted agents commonly prevent these cancers, widespread implementation of current prevention strategies may not reduce deaths from breast cancer. Compared with other breast cancers, ER-positive, PR-negative cancers and triple-negative cancers have inferior survival (90.6% v 83.8% v 78.1%, respectively; P < .001). Against this background, in the Women's Health Initiative Dietary Modification randomized trial (N = 48,835), ER-positive, PR-negative cancers were statistically significantly reduced in the intervention group (hazard ratio, 0.77; 95% CI, 0.64 to 0.94) and deaths from breast cancer were reduced 21% (P = .02). In the Women's Health Initiative randomized, placebo-controlled trial evaluating conjugated equine estrogen (N = 10,739), ER-positive, PR-negative cancers were statistically significantly reduced in the intervention group (hazard ratio, 0.44; 95% CI, 0.27 to 0.74) and deaths from breast cancer were reduced 40% (P = .04). These findings suggest that reexamination of breast cancer risk reduction strategies and clinical practice is needed.
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Affiliation(s)
- Rowan T. Chlebowski
- Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA
| | | | - Kathy Pan
- Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA
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Chen R, Zheng R, Zhou J, Li M, Shao D, Li X, Wang S, Wei W. Risk Prediction Model for Esophageal Cancer Among General Population: A Systematic Review. Front Public Health 2021; 9:680967. [PMID: 34926362 PMCID: PMC8671165 DOI: 10.3389/fpubh.2021.680967] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 10/29/2021] [Indexed: 12/23/2022] Open
Abstract
Objective: The risk prediction model is an effective tool for risk stratification and is expected to play an important role in the early detection and prevention of esophageal cancer. This study sought to summarize the available evidence of esophageal cancer risk predictions models and provide references for their development, validation, and application. Methods: We searched PubMed, EMBASE, and Cochrane Library databases for original articles published in English up to October 22, 2021. Studies that developed or validated a risk prediction model of esophageal cancer and its precancerous lesions were included. Two reviewers independently extracted study characteristics including predictors, model performance and methodology, and assessed risk of bias and applicability with PROBAST (Prediction model Risk Of Bias Assessment Tool). Results: A total of 20 studies including 30 original models were identified. The median area under the receiver operating characteristic curve of risk prediction models was 0.78, ranging from 0.68 to 0.94. Age, smoking, body mass index, sex, upper gastrointestinal symptoms, and family history were the most commonly included predictors. None of the models were assessed as low risk of bias based on PROBST. The major methodological deficiencies were inappropriate date sources, inconsistent definition of predictors and outcomes, and the insufficient number of participants with the outcome. Conclusions: This study systematically reviewed available evidence on risk prediction models for esophageal cancer in general populations. The findings indicate a high risk of bias due to several methodological pitfalls in model development and validation, which limit their application in practice.
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Affiliation(s)
- Ru Chen
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Rongshou Zheng
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jiachen Zhou
- Department of Epidemiology and Biostatistics, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Minjuan Li
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Dantong Shao
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xinqing Li
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Shengfeng Wang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Wenqiang Wei
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Waters EA, Colditz GA, Davis KL. Essentialism and Exclusion: Racism in Cancer Risk Prediction Models. J Natl Cancer Inst 2021; 113:1620-1624. [PMID: 33905490 PMCID: PMC8634398 DOI: 10.1093/jnci/djab074] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 03/10/2021] [Accepted: 04/25/2021] [Indexed: 12/15/2022] Open
Abstract
Cancer risk prediction models have the potential to revolutionize the science and practice of cancer prevention and control by identifying the likelihood that a patient will develop cancer at some point in the future, likely experience more benefit than harm from a given intervention, and survive their cancer for a certain number of years. The ability of risk prediction models to produce estimates that are valid and reliable for people from diverse socio-demographic backgrounds-and consequently their utility for broadening the reach of precision medicine to marginalized populations-depends on ensuring that the risk factors included in the model are represented as thoroughly and as accurately as possible. However, cancer risk prediction models created in the United States have a critical limitation, the origins of which stem from the country's earliest days: they either erroneously treat the social construct of race as an immutable biological factor (ie, they "essentialize" race), or they exclude from the model those socio-contextual factors that are associated with both race and health outcomes. Models that essentialize race and/or exclude socio-contextual factors sometimes incorporate "race corrections" that adjust a patient's risk estimate up or down based on their race. This commentary discusses the origins of race corrections, potential flaws with such corrections, and strategies for developing cohorts for developing risk prediction models that do not essentialize race or exclude key socio-contextual factors. Such models will help move the science of cancer prevention and control towards its goal of eliminating cancer disparities and achieving health equity.
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Affiliation(s)
- Erika A Waters
- Washington University School of Medicine, St Louis, MO, USA
| | | | - Kia L Davis
- Washington University School of Medicine, St Louis, MO, USA
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Moskowitz CS, Ronckers CM, Chou JF, Smith SA, Friedman DN, Barnea D, Kok JL, de Vries S, Wolden SL, Henderson TO, van der Pal HJH, Kremer LCM, Neglia JP, Turcotte LM, Howell RM, Arnold MA, Schaapveld M, Aleman B, Janus C, Versluys B, Leisenring W, Sklar CA, Begg CB, Pike MC, Armstrong GT, Robison LL, van Leeuwen FE, Oeffinger KC. Development and Validation of a Breast Cancer Risk Prediction Model for Childhood Cancer Survivors Treated With Chest Radiation: A Report From the Childhood Cancer Survivor Study and the Dutch Hodgkin Late Effects and LATER Cohorts. J Clin Oncol 2021; 39:3012-3021. [PMID: 34048292 DOI: 10.1200/jco.20.02244] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
PURPOSE Women treated with chest radiation for childhood cancer have one of the highest risks of breast cancer. Models producing personalized breast cancer risk estimates applicable to this population do not exist. We sought to develop and validate a breast cancer risk prediction model for childhood cancer survivors treated with chest radiation incorporating treatment-related factors, family history, and reproductive factors. METHODS Analyses were based on multinational cohorts of female 5-year survivors of cancer diagnosed younger than age 21 years and treated with chest radiation. Model derivation was based on 1,120 participants in the Childhood Cancer Survivor Study diagnosed between 1970 and 1986, with median attained age 42 years (range 20-64) and 242 with breast cancer. Model validation included 1,027 participants from three cohorts, with median age 32 years (range 20-66) and 105 with breast cancer. RESULTS The model included current age, chest radiation field, whether chest radiation was delivered within 1 year of menarche, anthracycline exposure, age at menopause, and history of a first-degree relative with breast cancer. Ten-year risk estimates ranged from 2% to 23% for 30-year-old women (area under the curve, 0.63; 95% CI, 0.50 to 0.73) and from 5% to 34% for 40-year-old women (area under the curve, 0.67; 95% CI, 0.54 to 0.84). The highest risks were among premenopausal women older than age 40 years treated with mantle field radiation within a year of menarche who had a first-degree relative with breast cancer. It showed good calibration with an expected-to-observed ratio of the number of breast cancers of 0.92 (95% CI, 0.74 to 1.16). CONCLUSION Breast cancer risk varies among childhood cancer survivors treated with chest radiation. Accurate risk prediction may aid in refining surveillance, counseling, and preventive strategies in this population.
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Affiliation(s)
| | - Cécile M Ronckers
- Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands.,Brandenburg Medical School Theodor Fontane, Neuruppin, Germany
| | - Joanne F Chou
- Memorial Sloan Kettering Cancer Center, New York, NY
| | - Susan A Smith
- The University of Texas MD Anderson Cancer Center, Houston, TX
| | | | - Dana Barnea
- Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Judith L Kok
- Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands
| | | | | | - Tara O Henderson
- University of Chicago Medicine Comer Children's Hospital, Chicago, IL
| | | | | | - Joseph P Neglia
- University of Minnesota Masonic Cancer Center, Minneapolis, MN
| | | | | | | | | | - Berthe Aleman
- Netherlands Cancer Institute, Amsterdam, the Netherlands
| | | | - Birgitta Versluys
- Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands
| | | | | | - Colin B Begg
- Memorial Sloan Kettering Cancer Center, New York, NY
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Macaulay BO, Aribisala BS, Akande SA, Akinnuwesi BA, Olabanjo OA. Breast cancer risk prediction in African women using Random Forest Classifier. Cancer Treat Res Commun 2021; 28:100396. [PMID: 34049004 DOI: 10.1016/j.ctarc.2021.100396] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Revised: 05/07/2021] [Accepted: 05/08/2021] [Indexed: 11/18/2022]
Abstract
INTRODUCTION One of the most important steps in combating breast cancer is early and accurate diagnosis. Unfortunately, breast cancer is asymptomatic at the early stage, although some symptoms are presented at a later time, but at symptomatic stage treatment could be complicated or even become impossible thereby leading to death. Proper risk assessment is hence very important in reducing mortality. Some computational techniques have been developed for breast cancer risk assessment in the developed world, but such techniques do not work well in Africa because of the difference in risk profiles of African women e.g. later menarche, low drug abuse and low smoking rate. AIM In this work, we propose a bespoke risk prediction model for African women using Random Forest Classifier (RFC) machine learning technique. METHODS A total of 180 subjects were studied out of which 90 were confirmed cases of breast cancer and 90 were benign. Twenty-five risk factors were included, for example, smoking, alcohol intake, occupational hazards and age at menopause. Four approaches were empirically used in the feature selection, these are the use of Chi-Square, mutual information gain, Spearman correlation and the entire features. RFC algorithm was used to develop the prediction model. RESULTS We found that family history of breast cancer, dense breast, deliberate abortion, age at first child, fruit intake and regular exercise are predictors of breast cancer. The RFC model gave an accuracy of 91.67%, sensitivity of 87.10%, specificity of 96.55% and Area under curve (AUC) of 92% when all the risk factors were included in the model while an accuracy of 96.67%, sensitivity of 93.75%, specificity of 100% and AUC of 97% were obtained when correlation-selected features were included in the model. The Chi-Square selected features gave the best performance with 98.33% accuracy, 100% sensitivity, 96.55 specificity and 98% AUC. Mutual information gain selected feature gave the same results as Chi-Square selected features. CONCLUSION Random Forest Classifier has a good potential at predicting the risk of breast cancer in African women. The study helped to identify the risk factors of breast cancer in African women. This is a valuable information which can help African women to pay attention to those risk factors with the intention of reducing the incidence of breast cancer in Africa.
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Affiliation(s)
| | | | - Soji Alabi Akande
- Department of Surgery, Lagos State University Teaching Hospital, Nigeria
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15
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Prediction of Incident Cancers in the Lifelines Population-Based Cohort. Cancers (Basel) 2021; 13:cancers13092133. [PMID: 33925159 PMCID: PMC8125183 DOI: 10.3390/cancers13092133] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 04/23/2021] [Indexed: 12/23/2022] Open
Abstract
Simple Summary The accurate prediction of incident cancers could be relevant to understanding and reducing cancer incidence. The aim of this study was to develop machine learning (ML) models that could predict an incident diagnosis of cancer. Data were available for 116,188 cancer-free participants and 4232 incident cancer cases. The main outcome was an incident cancer (excluding skin cancer) during follow-up assessment in a population-based cohort. The performance of three ML algorithms was evaluated using supervised binary classification to identify incident cancers among participants. An overall area under the receiver operator curve (AUC) < 0.75 was obtained; the highest AUC was for prostate cancer AUC > 0.80. Linear and non-linear ML algorithms including socioeconomic, lifestyle, and clinical variables produced a moderate predictive performance of incident cancers in the Lifelines cohort. Abstract Cancer incidence is rising, and accurate prediction of incident cancers could be relevant to understanding and reducing cancer incidence. The aim of this study was to develop machine learning (ML) models that could predict an incident diagnosis of cancer. Participants without any history of cancer within the Lifelines population-based cohort were followed for a median of 7 years. Data were available for 116,188 cancer-free participants and 4232 incident cancer cases. At baseline, socioeconomic, lifestyle, and clinical variables were assessed. The main outcome was an incident cancer during follow-up (excluding skin cancer), based on linkage with the national pathology registry. The performance of three ML algorithms was evaluated using supervised binary classification to identify incident cancers among participants. Elastic net regularization and Gini index were used for variables selection. An overall area under the receiver operator curve (AUC) <0.75 was obtained, the highest AUC value was for prostate cancer (random forest AUC = 0.82 (95% CI 0.77–0.87), logistic regression AUC = 0.81 (95% CI 0.76–0.86), and support vector machines AUC = 0.83 (95% CI 0.78–0.88), respectively); age was the most important predictor in these models. Linear and non-linear ML algorithms including socioeconomic, lifestyle, and clinical variables produced a moderate predictive performance of incident cancers in the Lifelines cohort.
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Gilman EA, Pruthi S, Hofstatter EW, Mussallem DM. Preventing Breast Cancer Through Identification and Pharmacologic Management of High-Risk Patients. Mayo Clin Proc 2021; 96:1033-1040. [PMID: 33814072 DOI: 10.1016/j.mayocp.2021.01.028] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 01/05/2021] [Accepted: 01/28/2021] [Indexed: 12/21/2022]
Abstract
Breast cancer remains the most common cancer in women in the United States. For certain women at high risk for breast cancer, endocrine therapy (ET) can greatly decrease the risk. Tools such as the Breast Cancer Risk Assessment Tool (or Gail Model) and the International Breast Cancer Intervention Study risk calculator are available to help identify women at increased risk for breast cancer. Physician awareness of family history, reproductive and lifestyle factors, dense breast tissue, and history of benign proliferative breast disease are important when identifying high-risk women. The updated US Preventive Services Task Force and American Society of Clinical Oncology guidelines encourage primary care providers to identify at-risk women and offer risk-reducing medications. Among the various ETs, which include tamoxifen, raloxifene, anastrozole, and exemestane, tamoxifen is the only one available for premenopausal women aged 35 years and older. A shared decision-making process should be used to increase the usage of ET and must be individualized. This individualized approach must account for each woman's medical history and weigh the benefits and risks of ET in combination with the personal values of the patient.
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Affiliation(s)
- Elizabeth A Gilman
- Division of General Internal Medicine, Breast Diagnostic Clinic, Mayo Clinic, Rochester, MN.
| | - Sandhya Pruthi
- Division of General Internal Medicine, Breast Diagnostic Clinic, Mayo Clinic, Rochester, MN
| | - Erin W Hofstatter
- Department of Internal Medicine, Section of Medical Oncology, Smilow Cancer Hospital, Yale University, New Haven, CT
| | - Dawn M Mussallem
- Department of Internal Medicine, Jacoby Center for Breast Health, Mayo Clinic, Jacksonville, FL
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Rostami S, Rafei A, Damghanian M, Khakbazan Z, Maleki F, Zendehdel K. Discriminatory Accuracy of the Gail Model for Breast Cancer Risk Assessment among Iranian Women. IRANIAN JOURNAL OF PUBLIC HEALTH 2021; 49:2205-2213. [PMID: 33708742 PMCID: PMC7917489 DOI: 10.18502/ijph.v49i11.4739] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Background: The Gail model is the most well-known tool for breast cancer risk assessment worldwide. Although it was validated in various Western populations, inconsistent results were reported from Asian populations. We used data from a large case-control study and evaluated the discriminatory accuracy of the Gail model for breast cancer risk assessment among the Iranian female population. Methods: We used data from 942 breast cancer patients and 975 healthy controls at the Cancer Institute of Iran, Tehran, Iran, in 2016. We refitted the Gail model to our case-control data (the IR-Gail model). We compared the discriminatory power of the IR-Gail with the original Gail model, using ROC curve analyses and estimation of the area under the ROC curve (AUC). Results: Except for the history of biopsies that showed an extremely high relative risk (OR=9.1), the observed ORs were similar to the estimates observed in Gail’s study. Incidence rates of breast cancer were extremely lower in Iran than in the USA, leading to a lower average absolute risk among the Iranian population (2.78, ±SD 2.45). The AUC was significantly improved after refitting the model, but it remained modest (0.636 vs. 0.627, ΔAUC = 0.009, bootstrapped P=0.008). We reported that the cut-point of 1.67 suggested in the Gail study did not discriminate between breast cancer patients and controls among the Iranian female population. Conclusion: Although the coefficients from the local study improved the discriminatory accuracy of the model, it remained modest. Cohort studies are warranted to evaluate the validity of the model for Iranian women.
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Affiliation(s)
- Sahar Rostami
- Department of Reproductive Health and Midwifery, School of Nursing and Midwifery, Tehran University of Medical Sciences, Tehran, Iran.,Cancer Research Center, Cancer Institute of Iran, Tehran University of Medical Sciences, Tehran, Iran
| | - Ali Rafei
- Cancer Research Center, Cancer Institute of Iran, Tehran University of Medical Sciences, Tehran, Iran
| | - Maryam Damghanian
- Nursing and Midwifery Care Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Zohreh Khakbazan
- Nursing and Midwifery Care Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Farzad Maleki
- Cancer Research Center, Cancer Institute of Iran, Tehran University of Medical Sciences, Tehran, Iran.,Social Determinants of Health Research Center, Urmia University of Medical Sciences, Urmia, Iran
| | - Kazem Zendehdel
- Cancer Research Center, Cancer Institute of Iran, Tehran University of Medical Sciences, Tehran, Iran.,Cancer Biology Research Center, Cancer Institute of Iran, Tehran University of Medical Sciences, Tehran, Iran.,Breast Disease Research Center, Cancer Institute of Iran, Tehran University of Medical Sciences, Tehran, Iran
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McCarthy AM, Guan Z, Welch M, Griffin ME, Sippo DA, Deng Z, Coopey SB, Acar A, Semine A, Parmigiani G, Braun D, Hughes KS. Performance of Breast Cancer Risk-Assessment Models in a Large Mammography Cohort. J Natl Cancer Inst 2021; 112:489-497. [PMID: 31556450 DOI: 10.1093/jnci/djz177] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2019] [Revised: 07/23/2019] [Accepted: 09/04/2019] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Several breast cancer risk-assessment models exist. Few studies have evaluated predictive accuracy of multiple models in large screening populations. METHODS We evaluated the performance of the BRCAPRO, Gail, Claus, Breast Cancer Surveillance Consortium (BCSC), and Tyrer-Cuzick models in predicting risk of breast cancer over 6 years among 35 921 women aged 40-84 years who underwent mammography screening at Newton-Wellesley Hospital from 2007 to 2009. We assessed model discrimination using the area under the receiver operating characteristic curve (AUC) and assessed calibration by comparing the ratio of observed-to-expected (O/E) cases. We calculated the square root of the Brier score and positive and negative predictive values of each model. RESULTS Our results confirmed the good calibration and comparable moderate discrimination of the BRCAPRO, Gail, Tyrer-Cuzick, and BCSC models. The Gail model had slightly better O/E ratio and AUC (O/E = 0.98, 95% confidence interval [CI] = 0.91 to 1.06, AUC = 0.64, 95% CI = 0.61 to 0.65) compared with BRCAPRO (O/E = 0.94, 95% CI = 0.88 to 1.02, AUC = 0.61, 95% CI = 0.59 to 0.63) and Tyrer-Cuzick (version 8, O/E = 0.84, 95% CI = 0.79 to 0.91, AUC = 0.62, 95% 0.60 to 0.64) in the full study population, and the BCSC model had the highest AUC among women with available breast density information (O/E = 0.97, 95% CI = 0.89 to 1.05, AUC = 0.64, 95% CI = 0.62 to 0.66). All models had poorer predictive accuracy for human epidermal growth factor receptor 2 positive and triple-negative breast cancers than hormone receptor positive human epidermal growth factor receptor 2 negative breast cancers. CONCLUSIONS In a large cohort of patients undergoing mammography screening, existing risk prediction models had similar, moderate predictive accuracy and good calibration overall. Models that incorporate additional genetic and nongenetic risk factors and estimate risk of tumor subtypes may further improve breast cancer risk prediction.
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Affiliation(s)
- Anne Marie McCarthy
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Zoe Guan
- Department of Biostatistics, Harvard University T H Chan School of Public Health, Boston, MA.,Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA
| | - Michaela Welch
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA
| | - Molly E Griffin
- Division of Surgical Oncology, Massachusetts General Hospital, Boston, MA
| | - Dorothy A Sippo
- Department of Radiology, Massachusetts General Hospital, Boston, MA
| | - Zhengyi Deng
- Division of Surgical Oncology, Massachusetts General Hospital, Boston, MA
| | - Suzanne B Coopey
- Division of Surgical Oncology, Massachusetts General Hospital, Boston, MA
| | - Ahmet Acar
- Istanbul School of Medicine, Istanbul University, Istanbul, Turkey
| | - Alan Semine
- Department of Radiology, Newton-Wellesley Hospital, Newton, MA
| | - Giovanni Parmigiani
- Department of Biostatistics, Harvard University T H Chan School of Public Health, Boston, MA.,Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA
| | - Danielle Braun
- Department of Biostatistics, Harvard University T H Chan School of Public Health, Boston, MA.,Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA
| | - Kevin S Hughes
- Division of Surgical Oncology, Massachusetts General Hospital, Boston, MA
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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: 58] [Impact Index Per Article: 14.5] [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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Development of a comprehensive health-risk prediction tool for postmenopausal women. ACTA ACUST UNITED AC 2020; 26:1385-1394. [PMID: 31567871 DOI: 10.1097/gme.0000000000001411] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
OBJECTIVE The aim of the study was to develop a web-based calculator that predicts the likelihood of experiencing multiple, competing outcomes prospectively over 5, 10, and 15 years. METHODS Baseline demographic and medical data from a healthy and racially and ethnically diverse cohort of 161,808 postmenopausal women, aged 50 to 79 at study baseline, who participated in the Women's Health Initiative (WHI), were used to develop and evaluate a risk-prediction calculator designed to predict individual risk for morbidity and mortality outcomes. Women were enrolled from 40 sites arranged in four regions of the United States. The calculator predicts all-cause mortality, adjudicated outcomes of health events (ie, myocardial infarction [MI], stroke, and hip fracture), and disease (lung, breast, and colorectal cancer). A proportional subdistribution hazards regression model was used to develop the calculator in a training dataset using data from three regions. The calculator was evaluated using the C-statistic in a test dataset with data from the fourth region. RESULTS The predictive validity of our calculator measured by the C-statistic in the test dataset for a first event at 5 and 15 years was as follows: MI 0.77, 0.61, stroke 0.77, 0.72, lung cancer 0.82, 0.79, breast cancer 0.60, 0.59, colorectal cancer 0.67, 0.60, hip fracture 0.79, 0.76, and death 0.74, 0.72. CONCLUSION This study represents the first large-scale study to develop a risk prediction calculator that yields health risk prediction for several outcomes simultaneously. Development of this tool is a first step toward enabling women to prioritize interventions that may decrease these risks. : Video Summary:http://links.lww.com/MENO/A463.
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Jiang X, McGuinness JE, Sin M, Silverman T, Kukafka R, Crew KD. Identifying Women at High Risk for Breast Cancer Using Data From the Electronic Health Record Compared With Self-Report. JCO Clin Cancer Inform 2020; 3:1-8. [PMID: 30869999 DOI: 10.1200/cci.18.00072] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
PURPOSE A barrier to chemoprevention uptake among high-risk women is the lack of routine breast cancer risk assessment in the primary care setting. We calculated breast cancer risk using the Breast Cancer Surveillance Consortium (BCSC) model, accounting for age, race/ethnicity, first-degree family history of breast cancer, benign breast disease, and mammographic density, using data collected from the electronic health records (EHRs) and self-reports. PATIENTS AND METHODS Among women undergoing screening mammography, we enrolled those age 35 to 74 years without a prior history of breast cancer. Data on demographics, first-degree family history, breast radiology, and pathology reports were extracted from the EHR. We assessed agreement between the EHR and self-report on information about breast cancer risk. RESULTS Among 9,514 women with known race/ethnicity, 1,443 women (15.2%) met high-risk criteria based upon a 5-year invasive breast cancer risk of 1.67% or greater according to the BCSC model. Among 1,495 women with both self-report and EHR data, more women with a first-degree family history of breast cancer (14.6% v 4.4%) and previous breast biopsies (21.3% v 11.3%) were identified by self-report versus EHR, respectively. However, more women with atypia and lobular carcinoma in situ were identified from the EHR. There was moderate agreement in identification of high-risk women between EHR and self-report data (κ, 0.48; 95% CI, 0.42-0.54). CONCLUSION By using EHR data, we determined that 15% of women undergoing screening mammography had a high risk for breast cancer according to the BCSC model. There was moderate agreement between information on breast cancer risk derived from the EHR and self-report. Examining EHR data may serve as an initial screen for identifying women eligible for breast cancer chemoprevention.
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Affiliation(s)
| | | | | | | | | | - Katherine D Crew
- Columbia University, New York, NY.,Herbert Irving Comprehensive Cancer Center, New York, NY
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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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Breast cancer risk assessment and early diagnosis using Principal Component Analysis and support vector machine techniques. INFORMATICS IN MEDICINE UNLOCKED 2020. [DOI: 10.1016/j.imu.2020.100459] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
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Guan Z, Raut JR, Weigl K, Schöttker B, Holleczek B, Zhang Y, Brenner H. Individual and joint performance of DNA methylation profiles, genetic risk score and environmental risk scores for predicting breast cancer risk. Mol Oncol 2019; 14:42-53. [PMID: 31677238 PMCID: PMC6944111 DOI: 10.1002/1878-0261.12594] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Revised: 08/30/2019] [Accepted: 10/24/2019] [Indexed: 12/24/2022] Open
Abstract
DNA methylation patterns in the blood, genetic risk scores (GRSs), and environmental risk factors can potentially improve breast cancer (BC) risk prediction. We assessed the individual and joint predictive performance of methylation, GRS, and environmental risk factors for BC incidence in a prospective cohort study. In a cohort of 5462 women aged 50–75 from Germany, 101 BC cases were identified during 14 years of follow‐up and were compared to 263 BC‐free controls in a nested case–control design. Three previously suggested methylation risk scores (MRSs) based on methylation of 423, 248, and 131 cytosine‐phosphate‐guanine (CpG) loci, and a GRS based on the risk alleles from 269 recently identified single nucleotide polymorphisms were constructed. Additionally, multiple previously proposed environmental risk scores (ERSs) were built based on environmental variables. Areas under the receiver operating characteristic curves (AUCs) were estimated for evaluating BC risk prediction performance. MRS and ERS showed limited accuracy in predicting BC incidence, with AUCs ranging from 0.52 to 0.56 and from 0.52 to 0.59, respectively. The GRS predicted BC incidence with a higher accuracy (AUC = 0.61). Adjusted odds ratios per standard deviation increase (95% confidence interval) were 1.07 (0.84–1.36) and 1.40 (1.09–1.80) for the best performing MRS and ERS, respectively, and 1.48 (1.16–1.90) for the GRS. A full risk model combining the MRS, GRS, and ERS predicted BC incidence with the highest accuracy (AUC = 0.64) and might be useful for identifying high‐risk populations for BC screening.
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Affiliation(s)
- Zhong Guan
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.,Medical Faculty Heidelberg, University of Heidelberg, Germany
| | - Janhavi R Raut
- Medical Faculty Heidelberg, University of Heidelberg, Germany.,Division of Preventive Oncology, German Cancer Research Center (DKFZ), National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Korbinian Weigl
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.,German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Ben Schöttker
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.,Network Aging Research, University of Heidelberg, Germany
| | | | - Yan Zhang
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.,German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.,Division of Preventive Oncology, German Cancer Research Center (DKFZ), National Center for Tumor Diseases (NCT), Heidelberg, Germany.,German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
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Tan J, Qi Y, Liu C, Xiong Y, He Q, Zhang G, Chen M, He G, Wang W, Liu X, Sun X. The use of rigorous methods was strongly warranted among prognostic prediction models for obstetric care. J Clin Epidemiol 2019; 115:98-105. [PMID: 31326543 DOI: 10.1016/j.jclinepi.2019.07.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2019] [Revised: 07/01/2019] [Accepted: 07/15/2019] [Indexed: 02/05/2023]
Abstract
OBJECTIVE The objective of the study was to examine methodological characteristics about the design and conduct in prognostic prediction models used for obstetric care. STUDY DESIGN AND SETTING We searched PubMed for studies on prognostic prediction models for obstetric care, published in top general medicine or major specialty journals between January 2011 and February 2018. Teams of method-trained investigators independently screened titles and abstracts and collected data using a prespecified, pilot-tested, structured questionnaire. RESULTS In total, 91 studies were eligible, of which two were published in top general medicine journals, 20 (22.0%) involved an epidemiologist or statistician, 18 (19.4%) published study protocols, 53 (58.2%) did not include any model validation, 20 (22.0%) did not clearly state the intended timing of use, 23 (25.3%) had no eligibility criteria, 15 (16.5%) did not use clear criteria for ascertaining outcome, and 69 (75.82%) did not apply blinding to outcome assessment. Among those models, 11 (12.1%) included participants fewer than 200 events, 41 (48.8%) had fewer than 100 events, and 19 (24.7%) had fewer than 10 events per variable. CONCLUSION The prognostic prediction models have important limitations in design and conduct. Substantial efforts are needed to strengthen the production of reliable prognostic prediction models for obstetric care.
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Affiliation(s)
- Jing Tan
- Chinese Evidence-based Medicine Center and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Yana Qi
- Chinese Evidence-based Medicine Center and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Chunrong Liu
- Chinese Evidence-based Medicine Center and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Yiquan Xiong
- Chinese Evidence-based Medicine Center and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Qiao He
- Chinese Evidence-based Medicine Center and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Guiting Zhang
- Chinese Evidence-based Medicine Center and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Meng Chen
- Department of Obstetrics and Gynecology, and Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Guolin He
- Department of Obstetrics and Gynecology, and Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Wen Wang
- Chinese Evidence-based Medicine Center and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Xinghui Liu
- Department of Obstetrics and Gynecology, and Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Xin Sun
- Chinese Evidence-based Medicine Center and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China.
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26
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A systematic review and quality assessment of individualised breast cancer risk prediction models. Br J Cancer 2019; 121:76-85. [PMID: 31114019 PMCID: PMC6738106 DOI: 10.1038/s41416-019-0476-8] [Citation(s) in RCA: 78] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2019] [Accepted: 04/25/2019] [Indexed: 01/08/2023] Open
Abstract
Background Individualised breast cancer risk prediction models may be key for planning risk-based screening approaches. Our aim was to conduct a systematic review and quality assessment of these models addressed to women in the general population. Methods We followed the Cochrane Collaboration methods searching in Medline, EMBASE and The Cochrane Library databases up to February 2018. We included studies reporting a model to estimate the individualised risk of breast cancer in women in the general population. Study quality was assessed by two independent reviewers. Results are narratively summarised. Results We included 24 studies out of the 2976 citations initially retrieved. Twenty studies were based on four models, the Breast Cancer Risk Assessment Tool (BCRAT), the Breast Cancer Surveillance Consortium (BCSC), the Rosner & Colditz model, and the International Breast Cancer Intervention Study (IBIS), whereas four studies addressed other original models. Four of the studies included genetic information. The quality of the studies was moderate with some limitations in the discriminative power and data inputs. A maximum AUROC value of 0.71 was reported in the study conducted in a screening context. Conclusion Individualised risk prediction models are promising tools for implementing risk-based screening policies. However, it is a challenge to recommend any of them since they need further improvement in their quality and discriminatory capacity.
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Glynn RJ, Colditz GA, Tamimi RM, Chen WY, Hankinson SE, Willett WW, Rosner B. Comparison of Questionnaire-Based Breast Cancer Prediction Models in the Nurses' Health Study. Cancer Epidemiol Biomarkers Prev 2019; 28:1187-1194. [PMID: 31015199 DOI: 10.1158/1055-9965.epi-18-1039] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2018] [Revised: 12/06/2018] [Accepted: 04/11/2019] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND The Gail model and the model developed by Tyrer and Cuzick are two questionnaire-based approaches with demonstrated ability to predict development of breast cancer in a general population. METHODS We compared calibration, discrimination, and net reclassification of these models, using data from questionnaires sent every 2 years to 76,922 participants in the Nurses' Health Study between 1980 and 2006, with 4,384 incident invasive breast cancers identified by 2008 (median follow-up, 24 years; range, 1-28 years). In a random one third sample of women, we also compared the performance of these models with predictions from the Rosner-Colditz model estimated from the remaining participants. RESULTS Both the Gail and Tyrer-Cuzick models showed evidence of miscalibration (Hosmer-Lemeshow P < 0.001 for each) with notable (P < 0.01) overprediction in higher-risk women (2-year risk above about 1%) and underprediction in lower-risk women (risk below about 0.25%). The Tyrer-Cuzick model had slightly higher C-statistics both overall (P < 0.001) and in age-specific comparisons than the Gail model (overall C, 0.63 for Tyrer-Cuzick vs. 0.61 for the Gail model). Evaluation of net reclassification did not favor either model. In the one third sample, the Rosner-Colditz model had better calibration and discrimination than the other two models. All models had C-statistics <0.60 among women ages ≥70 years. CONCLUSIONS Both the Gail and Tyrer-Cuzick models had some ability to discriminate breast cancer cases and noncases, but have limitations in their model fit. IMPACT Refinements may be needed to questionnaire-based approaches to predict breast cancer in older and higher-risk women.
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Affiliation(s)
- Robert J Glynn
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts. .,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.,Division of Preventive Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Graham A Colditz
- Alvin J. Siteman Cancer Center and Department of Surgery, Division of Public Health Sciences, School of Medicine, Washington University of St. Louis, St. Louis, Missouri
| | - Rulla M Tamimi
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Wendy Y Chen
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.,Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Susan E Hankinson
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.,Division of Biostatistics and Epidemiology, School of Public Health Sciences, University of Massachusetts, Amherst, Massachusetts
| | - Walter W Willett
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.,Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Bernard Rosner
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
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28
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Clendenen TV, Ge W, Koenig KL, Afanasyeva Y, Agnoli C, Brinton LA, Darvishian F, Dorgan JF, Eliassen AH, Falk RT, Hallmans G, Hankinson SE, Hoffman-Bolton J, Key TJ, Krogh V, Nichols HB, Sandler DP, Schoemaker MJ, Sluss PM, Sund M, Swerdlow AJ, Visvanathan K, Zeleniuch-Jacquotte A, Liu M. Breast cancer risk prediction in women aged 35-50 years: impact of including sex hormone concentrations in the Gail model. Breast Cancer Res 2019; 21:42. [PMID: 30890167 PMCID: PMC6425605 DOI: 10.1186/s13058-019-1126-z] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Accepted: 03/05/2019] [Indexed: 12/28/2022] Open
Abstract
Background Models that accurately predict risk of breast cancer are needed to help younger women make decisions about when to begin screening. Premenopausal concentrations of circulating anti-Müllerian hormone (AMH), a biomarker of ovarian reserve, and testosterone have been positively associated with breast cancer risk in prospective studies. We assessed whether adding AMH and/or testosterone to the Gail model improves its prediction performance for women aged 35–50. Methods In a nested case-control study including ten prospective cohorts (1762 invasive cases/1890 matched controls) with pre-diagnostic serum/plasma samples, we estimated relative risks (RR) for the biomarkers and Gail risk factors using conditional logistic regression and random-effects meta-analysis. Absolute risk models were developed using these RR estimates, attributable risk fractions calculated using the distributions of the risk factors in the cases from the consortium, and population-based incidence and mortality rates. The area under the receiver operating characteristic curve (AUC) was used to compare the discriminatory accuracy of the models with and without biomarkers. Results The AUC for invasive breast cancer including only the Gail risk factor variables was 55.3 (95% CI 53.4, 57.1). The AUC increased moderately with the addition of AMH (AUC 57.6, 95% CI 55.7, 59.5), testosterone (AUC 56.2, 95% CI 54.4, 58.1), or both (AUC 58.1, 95% CI 56.2, 59.9). The largest AUC improvement (4.0) was among women without a family history of breast cancer. Conclusions AMH and testosterone moderately increase the discriminatory accuracy of the Gail model among women aged 35–50. We observed the largest AUC increase for women without a family history of breast cancer, the group that would benefit most from improved risk prediction because early screening is already recommended for women with a family history. Electronic supplementary material The online version of this article (10.1186/s13058-019-1126-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Tess V Clendenen
- Department of Population Health, New York University School of Medicine, 650 First Avenue, New York, NY, 10016, USA
| | - Wenzhen Ge
- Department of Population Health, New York University School of Medicine, 650 First Avenue, New York, NY, 10016, USA
| | - Karen L Koenig
- Department of Population Health, New York University School of Medicine, 650 First Avenue, New York, NY, 10016, USA
| | - Yelena Afanasyeva
- Department of Population Health, New York University School of Medicine, 650 First Avenue, New York, NY, 10016, USA
| | - Claudia Agnoli
- Epidemiology and Prevention Unit, Fondazione IRCCS - Istituto Nazionale dei Tumori, Milan, Italy
| | - Louise A Brinton
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Farbod Darvishian
- Department of Pathology, New York University School of Medicine, New York, NY, USA.,Perlmutter Cancer Center, New York University School of Medicine, New York, NY, USA
| | - Joanne F Dorgan
- Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD, USA
| | - A Heather Eliassen
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, and Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Roni T Falk
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Göran Hallmans
- Department of Biobank Research, Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | - Susan E Hankinson
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, and Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA, USA
| | - Judith Hoffman-Bolton
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Timothy J Key
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Vittorio Krogh
- Epidemiology and Prevention Unit, Fondazione IRCCS - Istituto Nazionale dei Tumori, Milan, Italy
| | - Hazel B Nichols
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
| | - Dale P Sandler
- Epidemiology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA
| | - Minouk J Schoemaker
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK.,Division of Breast Cancer Research, The Institute of Cancer Research, London, UK
| | - Patrick M Sluss
- Department of Pathology, Harvard Medical School, Boston, MA, USA
| | - Malin Sund
- Department of Surgery, Umeå University Hospital, Umeå, Sweden
| | - Anthony J Swerdlow
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK
| | - Kala Visvanathan
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.,Sidney Kimmel Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Anne Zeleniuch-Jacquotte
- Department of Population Health, New York University School of Medicine, 650 First Avenue, New York, NY, 10016, USA.,Perlmutter Cancer Center, New York University School of Medicine, New York, NY, USA
| | - Mengling Liu
- Department of Population Health, New York University School of Medicine, 650 First Avenue, New York, NY, 10016, USA. .,Perlmutter Cancer Center, New York University School of Medicine, New York, NY, USA.
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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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30
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Fung SM, Wong XY, Lee SX, Miao H, Hartman M, Wee HL. Performance of Single-Nucleotide Polymorphisms in Breast Cancer Risk Prediction Models: A Systematic Review and Meta-analysis. Cancer Epidemiol Biomarkers Prev 2018; 28:506-521. [DOI: 10.1158/1055-9965.epi-18-0810] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Revised: 10/30/2018] [Accepted: 12/03/2018] [Indexed: 11/16/2022] Open
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31
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Li K, Anderson G, Viallon V, Arveux P, Kvaskoff M, Fournier A, Krogh V, Tumino R, Sánchez MJ, Ardanaz E, Chirlaque MD, Agudo A, Muller DC, Smith T, Tzoulaki I, Key TJ, Bueno-de-Mesquita B, Trichopoulou A, Bamia C, Orfanos P, Kaaks R, Hüsing A, Fortner RT, Zeleniuch-Jacquotte A, Sund M, Dahm CC, Overvad K, Aune D, Weiderpass E, Romieu I, Riboli E, Gunter MJ, Dossus L, Prentice R, Ferrari P. Risk prediction for estrogen receptor-specific breast cancers in two large prospective cohorts. Breast Cancer Res 2018; 20:147. [PMID: 30509329 PMCID: PMC6276150 DOI: 10.1186/s13058-018-1073-0] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2018] [Accepted: 11/04/2018] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Few published breast cancer (BC) risk prediction models consider the heterogeneity of predictor variables between estrogen-receptor positive (ER+) and negative (ER-) tumors. Using data from two large cohorts, we examined whether modeling this heterogeneity could improve prediction. METHODS We built two models, for ER+ (ModelER+) and ER- tumors (ModelER-), respectively, in 281,330 women (51% postmenopausal at recruitment) from the European Prospective Investigation into Cancer and Nutrition cohort. Discrimination (C-statistic) and calibration (the agreement between predicted and observed tumor risks) were assessed both internally and externally in 82,319 postmenopausal women from the Women's Health Initiative study. We performed decision curve analysis to compare ModelER+ and the Gail model (ModelGail) regarding their applicability in risk assessment for chemoprevention. RESULTS Parity, number of full-term pregnancies, age at first full-term pregnancy and body height were only associated with ER+ tumors. Menopausal status, age at menarche and at menopause, hormone replacement therapy, postmenopausal body mass index, and alcohol intake were homogeneously associated with ER+ and ER- tumors. Internal validation yielded a C-statistic of 0.64 for ModelER+ and 0.59 for ModelER-. External validation reduced the C-statistic of ModelER+ (0.59) and ModelGail (0.57). In external evaluation of calibration, ModelER+ outperformed the ModelGail: the former led to a 9% overestimation of the risk of ER+ tumors, while the latter yielded a 22% underestimation of the overall BC risk. Compared with the treat-all strategy, ModelER+ produced equal or higher net benefits irrespective of the benefit-to-harm ratio of chemoprevention, while ModelGail did not produce higher net benefits unless the benefit-to-harm ratio was below 50. The clinical applicability, i.e. the area defined by the net benefit curve and the treat-all and treat-none strategies, was 12.7 × 10- 6 for ModelER+ and 3.0 × 10- 6 for ModelGail. CONCLUSIONS Modeling heterogeneous epidemiological risk factors might yield little improvement in BC risk prediction. Nevertheless, a model specifically predictive of ER+ tumor risk could be more applicable than an omnibus model in risk assessment for chemoprevention.
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Affiliation(s)
- Kuanrong Li
- Nutritional Methodology and Biostatistics Group, Nutrition and Metabolism Section, International Agency for Research on Cancer, 150 cours Albert Thomas, 69372 Lyon Cedex 08, France
| | - Garnet Anderson
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, USA
| | - Vivian Viallon
- Nutritional Methodology and Biostatistics Group, Nutrition and Metabolism Section, International Agency for Research on Cancer, 150 cours Albert Thomas, 69372 Lyon Cedex 08, France
| | - Patrick Arveux
- Breast and Gynaecologic Cancer Registry of Côte d’Or, Georges-François Leclerc Comprehensive Cancer Care Centre, Dijon, France
- EA 4184, Medical School, University of Burgundy, Dijon, France
- CESP, INSERM U1018, Univ. Paris-Sud, UVSQ, Université Paris-Saclay, Villejuif, France
- Gustave Roussy, Villejuif, France
| | - Marina Kvaskoff
- CESP, INSERM U1018, Univ. Paris-Sud, UVSQ, Université Paris-Saclay, Villejuif, France
- Gustave Roussy, Villejuif, France
| | - Agnès Fournier
- CESP, INSERM U1018, Univ. Paris-Sud, UVSQ, Université Paris-Saclay, Villejuif, France
- Gustave Roussy, Villejuif, France
| | - Vittorio Krogh
- Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Rosario Tumino
- Cancer Registry and Histopathology Department, “Civic-M. P.Arezzo” Hospital, ASP, Ragusa, Italy
| | - Maria-Jose Sánchez
- Escuela Andaluza de Salud Pública, Instituto de Investigación Biosanitaria ibs. GRANADA, Hospitales Universitarios de Granada/ Universidad de Granada, Granada, Spain
- CIBER de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Eva Ardanaz
- CIBER de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Navarra Public Health Institute, Pamplona, Spain
- IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
| | - María-Dolores Chirlaque
- CIBER de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Department of Epidemiology, Regional Health Council, IMIB-Arrixaca, Murcia, Spain
- Department of Health and Social Sciences, Universidad de Murcia, Murcia, Spain
| | - Antonio Agudo
- Unit of Nutrition and Cancer. Cancer Epidemiology Research Program, Catalan Institute of Oncology-IDIBELL. L’Hospitalet de Llobregat, Barcelona, Spain
| | - David C. Muller
- Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Todd Smith
- Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Ioanna Tzoulaki
- Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Timothy J. Key
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Bas Bueno-de-Mesquita
- Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, UK
- Department for Determinants of Chronic Diseases, National Institute for Public Health and the Environment, Bilthoven, The Netherlands
- Department of Gastroenterology and Hepatology, University Medical Centre, Utrecht, The Netherlands
- Department of Social & Preventive Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Antonia Trichopoulou
- Hellenic Health Foundation, Athens, Greece
- WHO Collaborating Center for Nutrition and Health, Unit of Nutritional Epidemiology and Nutrition in Public Health, Department of Hygiene, Epidemiology and Medical Statistics, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece
| | - Christina Bamia
- Hellenic Health Foundation, Athens, Greece
- WHO Collaborating Center for Nutrition and Health, Unit of Nutritional Epidemiology and Nutrition in Public Health, Department of Hygiene, Epidemiology and Medical Statistics, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece
| | - Philippos Orfanos
- Hellenic Health Foundation, Athens, Greece
- WHO Collaborating Center for Nutrition and Health, Unit of Nutritional Epidemiology and Nutrition in Public Health, Department of Hygiene, Epidemiology and Medical Statistics, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece
| | - Rudolf Kaaks
- Division of Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany
| | - Anika Hüsing
- Division of Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany
| | - Renée T. Fortner
- Division of Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany
| | - Anne Zeleniuch-Jacquotte
- Department of Population Health, New York University School of Medicine, New York, USA
- Department of Environmental Medicine, New York University School of Medicine, New York, USA
- Perlmutter Cancer Center, New York University School of Medicine, New York, USA
- Department of Public Health and Clinical Medicine, Nutritional Research, Umeå University, Umeå, Sweden
| | - Malin Sund
- Department of Surgical and Perioperative Sciences, Umeå University, Umeå, Sweden
| | - Christina C. Dahm
- Section for Epidemiology, Department of Public Health, Aarhus University, Aarhus, Denmark
| | - Kim Overvad
- Section for Epidemiology, Department of Public Health, Aarhus University, Aarhus, Denmark
- Department of Cardiology, Aalborg University Hospital, Aalborg, Denmark
| | - Dagfinn Aune
- Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, UK
- Department of Nutrition, Bjørknes University College, Oslo, Norway
| | - Elisabete Weiderpass
- Department of Research, Cancer Registry of Norway, Institute of Population-Based Cancer Research, Oslo, Norway
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Genetic Epidemiology Group, Folkhälsan Research Center, Helsinki, Finland
- Department of Community Medicine, University of Tromsø, The Arctic University of Norway, Tromsø, Norway
| | - Isabelle Romieu
- Nutritional Epidemiology Group, Nutrition and Metabolism Section, International Agency for Research on Cancer, Lyon, France
| | - Elio Riboli
- Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Marc J. Gunter
- Nutritional Epidemiology Group, Nutrition and Metabolism Section, International Agency for Research on Cancer, Lyon, France
| | - Laure Dossus
- Biomarkers Group, Nutrition and Metabolism Section, International Agency for Research on Cancer, Lyon, France
| | - Ross Prentice
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, USA
| | - Pietro Ferrari
- Nutritional Methodology and Biostatistics Group, Nutrition and Metabolism Section, International Agency for Research on Cancer, 150 cours Albert Thomas, 69372 Lyon Cedex 08, France
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Rajbongshi N, Nath DC, Mahanta LB. Estimating Risk of Breast Cancer Occurrences at Different Ages: Application of Survival Techniques. Asian Pac J Cancer Prev 2018; 19:3033-3038. [PMID: 30484988 PMCID: PMC6318421 DOI: 10.31557/apjcp.2018.19.11.3033] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2017] [Accepted: 10/07/2018] [Indexed: 11/25/2022] Open
Abstract
Background: Awareness is the primary means to control breast cancer occurrence. The purpose of the present work is to study the risk of breast cancer occurrence in different age group, for the study area, Assam, India, by means of survival analysis techniques. Methods: Survival and hazard functions are key concepts in survival analysis for describing the distribution of event times. In the present research a new individialized model has been proposed for cumulative hazard function, taking gamma probability distribution as probability distribution of breast cancer occurrences. Kaplan Meier Survival method has been applied to find out the probability of diseases occurrence in the early menarche and late menarche group. The data used for implementation were collected from the Record Department of a prime local cancer institute, for the period 2010-2012. The information for the risk factor age at menarche were collected from the patients registered during August 2011 to February 2012. Results: The study reveals that in the study area, cumulative hazard of the women belonging to 35 to 50 years is higher than the early and late aged women. The cumulative hazard plot with shape parameter 0.5, 1 and 10 shows that cumulative risk for early aged women are greater than the late age women but when this values is increased from 10, the opposite trend is observed. Further, the median age of disease occurrence among early menarche group is 52 years and for late menarche it is 54 years. Conclusion: The model developed could successfully point out the age group for women lying at higher risk of breast cancer occurrence. Additionally the important risk factor, age at menarche, was effectively applied to supplement to this calculation. It is hoped that practical use of this method would enhance not only awareness but also early detection of the said disease.
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Affiliation(s)
- N Rajbongshi
- Central Computational and Numerical Sciences Division (CCNS), Institute of Advanced Study in Science and Technology (IASST) (An Autonomous Institute under Department of Science and Technology), Guwahati, India.
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Al-Ajmi K, Lophatananon A, Yuille M, Ollier W, Muir KR. Review of non-clinical risk models to aid prevention of breast cancer. Cancer Causes Control 2018; 29:967-986. [PMID: 30178398 PMCID: PMC6182451 DOI: 10.1007/s10552-018-1072-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2017] [Accepted: 08/10/2018] [Indexed: 12/29/2022]
Abstract
A disease risk model is a statistical method which assesses the probability that an individual will develop one or more diseases within a stated period of time. Such models take into account the presence or absence of specific epidemiological risk factors associated with the disease and thereby potentially identify individuals at higher risk. Such models are currently used clinically to identify people at higher risk, including identifying women who are at increased risk of developing breast cancer. Many genetic and non-genetic breast cancer risk models have been developed previously. We have evaluated existing non-genetic/non-clinical models for breast cancer that incorporate modifiable risk factors. This review focuses on risk models that can be used by women themselves in the community in the absence of clinical risk factors characterization. The inclusion of modifiable factors in these models means that they can be used to improve primary prevention and health education pertinent for breast cancer. Literature searches were conducted using PubMed, ScienceDirect and the Cochrane Database of Systematic Reviews. Fourteen studies were eligible for review with sample sizes ranging from 654 to 248,407 participants. All models reviewed had acceptable calibration measures, with expected/observed (E/O) ratios ranging from 0.79 to 1.17. However, discrimination measures were variable across studies with concordance statistics (C-statistics) ranging from 0.56 to 0.89. We conclude that breast cancer risk models that include modifiable risk factors have been well calibrated but have less ability to discriminate. The latter may be a consequence of the omission of some significant risk factors in the models or from applying models to studies with limited sample sizes. More importantly, external validation is missing for most of the models. Generalization across models is also problematic as some variables may not be considered applicable to some populations and each model performance is conditioned by particular population characteristics. In conclusion, it is clear that there is still a need to develop a more reliable model for estimating breast cancer risk which has a good calibration, ability to accurately discriminate high risk and with better generalizability across populations.
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Affiliation(s)
- Kawthar Al-Ajmi
- Division of Population Health, Health Services Research and Primary Care, Faculty of Biology, Medicine and Health, Centre for Epidemiology, The University of Manchester, Manchester, M139 PL UK
| | - Artitaya Lophatananon
- Division of Population Health, Health Services Research and Primary Care, Faculty of Biology, Medicine and Health, Centre for Epidemiology, The University of Manchester, Manchester, M139 PL UK
| | - Martin Yuille
- Division of Population Health, Health Services Research and Primary Care, Faculty of Biology, Medicine and Health, Centre for Epidemiology, The University of Manchester, Manchester, M139 PL UK
| | - William Ollier
- Division of Population Health, Health Services Research and Primary Care, Faculty of Biology, Medicine and Health, Centre for Epidemiology, The University of Manchester, Manchester, M139 PL UK
| | - Kenneth R. Muir
- Division of Population Health, Health Services Research and Primary Care, Faculty of Biology, Medicine and Health, Centre for Epidemiology, The University of Manchester, Manchester, M139 PL UK
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Witteveen A, Nane GF, Vliegen IM, Siesling S, IJzerman MJ. Comparison of Logistic Regression and Bayesian Networks for Risk Prediction of Breast Cancer Recurrence. Med Decis Making 2018; 38:822-833. [DOI: 10.1177/0272989x18790963] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Purpose. For individualized follow-up, accurate prediction of locoregional recurrence (LRR) and second primary (SP) breast cancer risk is required. Current prediction models employ regression, but with large data sets, machine-learning techniques such as Bayesian Networks (BNs) may be better alternatives. In this study, logistic regression was compared with different BNs, built with network classifiers and constraint- and score-based algorithms. Methods. Women diagnosed with early breast cancer between 2003 and 2006 were selected from the Netherlands Cancer Registry (NCR) ( N = 37,320). BN structures were developed using 1) Bayesian network classifiers, 2) correlation coefficients with different cutoffs, 3) constraint-based learning algorithms, and 4) score-based learning algorithms. The different models were compared with logistic regression using the area under the receiver operating characteristic curve, an external validation set obtained from the NCR from 2007 and 2008 ( N = 12,308), and subgroup analyses for a high- and low-risk group. Results. The BNs with the most links showed the best performance in both LRR and SP prediction (c-statistic of 0.76 for LRR and 0.69 for SP). In the external validation, logistic regression generally outperformed the BNs in both SP and LRR (c-statistic of 0.71 for LRR and 0.64 for SP). The differences were nonetheless small. Although logistic regression performed best on most parts of the subgroup analysis, BNs outperformed regression with respect to average risk for SP prediction in low- and high-risk groups. Conclusions. Although estimates of regression coefficients depend on other independent variables, there is no assumed dependence relationship between coefficient estimators and the change in value of other variables as in the case of BNs. Nonetheless, this analysis suggests that regression is still more accurate or at least as accurate as BNs for risk estimation for both LRRs and SP tumors.
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Affiliation(s)
- Annemieke Witteveen
- Department of Health Technology and Services Research (HTSR), Technical Medical Centre, University of Twente, Enschede, the Netherlands (AW, SS, MJIJ)
- Delft Institute of Applied Mathematics (DIAM), Delft University of Technology, Delft, the Netherlands (GFN)
- Department of Industrial Engineering and Innovation Sciences, Eindhoven University of Technology, Eindhoven, the Netherlands (IMHV)
- Department of Research, Netherlands Comprehensive Cancer Organisation (IKNL), Utrecht, the Netherlands (SS)
| | - Gabriela F. Nane
- Department of Health Technology and Services Research (HTSR), Technical Medical Centre, University of Twente, Enschede, the Netherlands (AW, SS, MJIJ)
- Delft Institute of Applied Mathematics (DIAM), Delft University of Technology, Delft, the Netherlands (GFN)
- Department of Industrial Engineering and Innovation Sciences, Eindhoven University of Technology, Eindhoven, the Netherlands (IMHV)
- Department of Research, Netherlands Comprehensive Cancer Organisation (IKNL), Utrecht, the Netherlands (SS)
| | - Ingrid M.H. Vliegen
- Department of Health Technology and Services Research (HTSR), Technical Medical Centre, University of Twente, Enschede, the Netherlands (AW, SS, MJIJ)
- Delft Institute of Applied Mathematics (DIAM), Delft University of Technology, Delft, the Netherlands (GFN)
- Department of Industrial Engineering and Innovation Sciences, Eindhoven University of Technology, Eindhoven, the Netherlands (IMHV)
- Department of Research, Netherlands Comprehensive Cancer Organisation (IKNL), Utrecht, the Netherlands (SS)
| | - Sabine Siesling
- Department of Health Technology and Services Research (HTSR), Technical Medical Centre, University of Twente, Enschede, the Netherlands (AW, SS, MJIJ)
- Delft Institute of Applied Mathematics (DIAM), Delft University of Technology, Delft, the Netherlands (GFN)
- Department of Industrial Engineering and Innovation Sciences, Eindhoven University of Technology, Eindhoven, the Netherlands (IMHV)
- Department of Research, Netherlands Comprehensive Cancer Organisation (IKNL), Utrecht, the Netherlands (SS)
| | - Maarten J. IJzerman
- Department of Health Technology and Services Research (HTSR), Technical Medical Centre, University of Twente, Enschede, the Netherlands (AW, SS, MJIJ)
- Delft Institute of Applied Mathematics (DIAM), Delft University of Technology, Delft, the Netherlands (GFN)
- Department of Industrial Engineering and Innovation Sciences, Eindhoven University of Technology, Eindhoven, the Netherlands (IMHV)
- Department of Research, Netherlands Comprehensive Cancer Organisation (IKNL), Utrecht, the Netherlands (SS)
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Iniesta R, Hodgson K, Stahl D, Malki K, Maier W, Rietschel M, Mors O, Hauser J, Henigsberg N, Dernovsek MZ, Souery D, Dobson R, Aitchison KJ, Farmer A, McGuffin P, Lewis CM, Uher R. Antidepressant drug-specific prediction of depression treatment outcomes from genetic and clinical variables. Sci Rep 2018; 8:5530. [PMID: 29615645 PMCID: PMC5882876 DOI: 10.1038/s41598-018-23584-z] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2017] [Accepted: 03/13/2018] [Indexed: 12/19/2022] Open
Abstract
Individuals with depression differ substantially in their response to treatment with antidepressants. Specific predictors explain only a small proportion of these differences. To meaningfully predict who will respond to which antidepressant, it may be necessary to combine multiple biomarkers and clinical variables. Using statistical learning on common genetic variants and clinical information in a training sample of 280 individuals randomly allocated to 12-week treatment with antidepressants escitalopram or nortriptyline, we derived models to predict remission with each antidepressant drug. We tested the reproducibility of each prediction in a validation set of 150 participants not used in model derivation. An elastic net logistic model based on eleven genetic and six clinical variables predicted remission with escitalopram in the validation dataset with area under the curve 0.77 (95%CI; 0.66-0.88; p = 0.004), explaining approximately 30% of variance in who achieves remission. A model derived from 20 genetic variables predicted remission with nortriptyline in the validation dataset with an area under the curve 0.77 (95%CI; 0.65-0.90; p < 0.001), explaining approximately 36% of variance in who achieves remission. The predictive models were antidepressant drug-specific. Validated drug-specific predictions suggest that a relatively small number of genetic and clinical variables can help select treatment between escitalopram and nortriptyline.
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Affiliation(s)
- Raquel Iniesta
- Biostatistics and Health Informatics Department. Institute of Psychiatry, Psychology and Neuroscience, Kings College London. 16 De Crespigny Park, London, SE5 8AF, UK
| | - Karen Hodgson
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, 16 De Crespigny Park, Denmark Hill, London, SE5 8AF, UK
| | - Daniel Stahl
- Biostatistics and Health Informatics Department. Institute of Psychiatry, Psychology and Neuroscience, Kings College London. 16 De Crespigny Park, London, SE5 8AF, UK
| | - Karim Malki
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, 16 De Crespigny Park, Denmark Hill, London, SE5 8AF, UK
| | - Wolfgang Maier
- Department of Psychiatry, University of Bonn, Regina-Pacis-Weg 3, 53113, Bonn, Germany
| | - Marcella Rietschel
- Central Institute of Mental Health, Division of Genetic Epidemiology in Psychiatry, Square J5, 68159, Mannheim, Germany
| | - Ole Mors
- Research Department P, Aarhus University Hospital, Norrebrogade 44, DK-8000, Aarhus C Risskov, Denmark
| | - Joanna Hauser
- Laboratory of Psychiatric Genetics, Department of Psychiatry, Poznan University of Medical Sciences, Collegium Maius, Fredry 10, 61-701, Poznań, Poland
| | - Neven Henigsberg
- Croatian Institute for Brain Research, Medical School, University of Zagreb, 10 000, Zagreb, Salata 3, Croatia
| | - Mojca Zvezdana Dernovsek
- Vzgojni zavod Planina, Planina 211, 6232 Planina, Slovenina and Universitiy of Ljubljana, Medical Faculty, Vrazov trg 2, 1000, Ljubljana, Slovenia
| | - Daniel Souery
- Laboratoire de Psychologie Médicale, Université Libre de Bruxelles and Psy Pluriel - Centre Européen de Psychologie Médicale, Av Jack Pastur 47a, 1180, Uccle, Belgium
| | - Richard Dobson
- Biostatistics and Health Informatics Department. Institute of Psychiatry, Psychology and Neuroscience, Kings College London. 16 De Crespigny Park, London, SE5 8AF, UK
| | - Katherine J Aitchison
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, 16 De Crespigny Park, Denmark Hill, London, SE5 8AF, UK
- Department of Psychiatry and Medical Genetics, University of Alberta, 116 St and 85 Ave, Edmonton, AB T6G 2R3, Canada
| | - Anne Farmer
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, 16 De Crespigny Park, Denmark Hill, London, SE5 8AF, UK
| | - Peter McGuffin
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, 16 De Crespigny Park, Denmark Hill, London, SE5 8AF, UK
| | - Cathryn M Lewis
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, 16 De Crespigny Park, Denmark Hill, London, SE5 8AF, UK
| | - Rudolf Uher
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, 16 De Crespigny Park, Denmark Hill, London, SE5 8AF, UK.
- Dalhousie University Department of Psychiatry, 5909 Veterans' Memorial Lane, Halifax, B3H 2E2, Nova Scotia, Canada.
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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: 57] [Impact Index Per Article: 9.5] [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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Lazar MA, Pan Z, Ragguett RM, Lee Y, Subramaniapillai M, Mansur RB, Rodrigues N, McIntyre RS. Digital revolution in depression: A technologies update for clinicians. ACTA ACUST UNITED AC 2017. [DOI: 10.1016/j.pmip.2017.09.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
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Iorfino F, Davenport TA, Ospina-Pinillos L, Hermens DF, Cross S, Burns J, Hickie IB. Using New and Emerging Technologies to Identify and Respond to Suicidality Among Help-Seeking Young People: A Cross-Sectional Study. J Med Internet Res 2017; 19:e247. [PMID: 28701290 PMCID: PMC5529742 DOI: 10.2196/jmir.7897] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2017] [Revised: 05/26/2017] [Accepted: 05/26/2017] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Suicidal thoughts are common among young people presenting to face-to-face and online mental health services. The early detection and rapid response to these suicidal thoughts and other suicidal behaviors is a priority for suicide prevention and early intervention efforts internationally. Establishing how best to use new and emerging technologies to facilitate person-centered systematic assessment and early intervention for suicidality is crucial to these efforts. OBJECTIVE The aim of this study was to examine the use of a suicidality escalation protocol to respond to suicidality among help-seeking young people. METHODS A total of 232 young people in the age range of 16-25 years were recruited from either a primary mental health care service or online in the community. Each young person used the Synergy Online System and completed an initial clinical assessment online before their face-to-face or online clinical appointment. A suicidality escalation protocol was used to identify and respond to current and previous suicidal thoughts and behaviors. RESULTS A total of 153 young people (66%, 153/232) reported some degree of suicidality and were provided with a real-time alert online. Further levels of escalation (email or phone contact and clinical review) were initiated for the 35 young people (15%, 35/232) reporting high suicidality. Higher levels of psychological distress (P<.001) and a current alcohol or substance use problem (P=.02) predicted any level of suicidality compared with no suicidality. Furthermore, predictors of high suicidality compared with low suicidality were higher levels of psychological distress (P=.01), psychosis-like symptoms in the last 12 months (P=.01), a previous mental health problem (P=.01), and a history of suicide planning or attempts (P=.001). CONCLUSIONS This study demonstrates the use of new and emerging technologies to facilitate the systematic assessment and detection of help-seeking young people presenting with suicidality. This protocol empowered the young person by suggesting pathways to care that were based on their current needs. The protocol also enabled an appropriate and timely response from service providers for young people reporting high suicidality that was associated with additional comorbid issues, including psychosis-like symptoms, and a history of suicide plans and attempts.
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Affiliation(s)
- Frank Iorfino
- Brain and Mind Centre, The University of Sydney, Sydney, Australia
| | | | | | - Daniel F Hermens
- Brain and Mind Centre, The University of Sydney, Sydney, Australia
| | - Shane Cross
- Brain and Mind Centre, The University of Sydney, Sydney, Australia
| | - Jane Burns
- Brain and Mind Centre, The University of Sydney, Sydney, Australia
| | - Ian B Hickie
- Brain and Mind Centre, The University of Sydney, Sydney, Australia
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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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40
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Wu M, Ma J. Association Between Imaging Characteristics and Different Molecular Subtypes of Breast Cancer. Acad Radiol 2017; 24:426-434. [PMID: 27955963 DOI: 10.1016/j.acra.2016.11.012] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2016] [Revised: 09/18/2016] [Accepted: 11/10/2016] [Indexed: 01/09/2023]
Abstract
RATIONALE AND OBJECTIVE Breast cancer can be divided into four major molecular subtypes based on the expression of hormone receptor (estrogen receptor and progesterone receptor), human epidermal growth factor receptor 2, HER2 status, and molecular proliferation rate (Ki67). In this study, we sought to investigate the association between breast cancer subtype and radiological findings in the Chinese population. MATERIALS AND METHODS Medical records of 300 consecutive invasive breast cancer patients were reviewed from the database: the Breast Imaging Reporting and Data System. The imaging characteristics of the lesions were evaluated. The molecular subtypes of breast cancer were classified into four types: luminal A, luminal B, HER2 overexpressed (HER2), and basal-like breast cancer (BLBC). Univariate and multivariate logistic regression analyses were performed to assess the association between the subtype (dependent variable) and mammography or 15 magnetic resonance imaging (MRI) indicators (independent variables). RESULTS Luminal A and B subtypes were commonly associated with "clustered calcification distribution," "nipple invasion," or "skin invasion" (P <0.05). The BLBC subtype was more commonly associated with "rim enhancement" and persistent inflow type enhancement in delayed phase (P <0.05). HER2 overexpressed cancers showed association with persistent enhancement in the delayed phase on MRI and "clustered calcification distribution" on mammography (P <0.05). CONCLUSION Certain radiological features are strongly associated with the molecular subtype and hormone receptor status of breast tumor, which are potentially useful tools in the diagnosis and subtyping of breast cancer.
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Risk Prediction Models for Incident Heart Failure: A Systematic Review of Methodology and Model Performance. J Card Fail 2017; 23:680-687. [PMID: 28336380 DOI: 10.1016/j.cardfail.2017.03.005] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2016] [Revised: 02/15/2017] [Accepted: 03/19/2017] [Indexed: 12/20/2022]
Abstract
BACKGROUND Numerous models predicting the risk of incident heart failure (HF) have been developed; however, evidence of their methodological rigor and reporting remains unclear. This study critically appraises the methods underpinning incident HF risk prediction models. METHODS AND RESULTS EMBASE and PubMed were searched for articles published between 1990 and June 2016 that reported at least 1 multivariable model for prediction of HF. Model development information, including study design, variable coding, missing data, and predictor selection, was extracted. Nineteen studies reporting 40 risk prediction models were included. Existing models have acceptable discriminative ability (C-statistics > 0.70), although only 6 models were externally validated. Candidate variable selection was based on statistical significance from a univariate screening in 11 models, whereas it was unclear in 12 models. Continuous predictors were retained in 16 models, whereas it was unclear how continuous variables were handled in 16 models. Missing values were excluded in 19 of 23 models that reported missing data, and the number of events per variable was < 10 in 13 models. Only 2 models presented recommended regression equations. There was significant heterogeneity in discriminative ability of models with respect to age (P < .001) and sample size (P = .007). CONCLUSIONS There is an abundance of HF risk prediction models that had sufficient discriminative ability, although few are externally validated. Methods not recommended for the conduct and reporting of risk prediction modeling were frequently used, and resulting algorithms should be applied with caution.
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Using patient self-reports to study heterogeneity of treatment effects in major depressive disorder. Epidemiol Psychiatr Sci 2017; 26:22-36. [PMID: 26810628 PMCID: PMC5125904 DOI: 10.1017/s2045796016000020] [Citation(s) in RCA: 101] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
BACKGROUNDS Clinicians need guidance to address the heterogeneity of treatment responses of patients with major depressive disorder (MDD). While prediction schemes based on symptom clustering and biomarkers have so far not yielded results of sufficient strength to inform clinical decision-making, prediction schemes based on big data predictive analytic models might be more practically useful. METHOD We review evidence suggesting that prediction equations based on symptoms and other easily-assessed clinical features found in previous research to predict MDD treatment outcomes might provide a foundation for developing predictive analytic clinical decision support models that could help clinicians select optimal (personalised) MDD treatments. These methods could also be useful in targeting patient subsamples for more expensive biomarker assessments. RESULTS Approximately two dozen baseline variables obtained from medical records or patient reports have been found repeatedly in MDD treatment trials to predict overall treatment outcomes (i.e., intervention v. control) or differential treatment outcomes (i.e., intervention A v. intervention B). Similar evidence has been found in observational studies of MDD persistence-severity. However, no treatment studies have yet attempted to develop treatment outcome equations using the full set of these predictors. Promising preliminary empirical results coupled with recent developments in statistical methodology suggest that models could be developed to provide useful clinical decision support in personalised treatment selection. These tools could also provide a strong foundation to increase statistical power in focused studies of biomarkers and MDD heterogeneity of treatment response in subsequent controlled trials. CONCLUSIONS Coordinated efforts are needed to develop a protocol for systematically collecting information about established predictors of heterogeneity of MDD treatment response in large observational treatment studies, applying and refining these models in subsequent pragmatic trials, carrying out pooled secondary analyses to extract the maximum amount of information from these coordinated studies, and using this information to focus future discovery efforts in the segment of the patient population in which continued uncertainty about treatment response exists.
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Methodological issues in current practice may lead to bias in the development of biomarker combinations for predicting acute kidney injury. Kidney Int 2017; 89:429-38. [PMID: 26398494 PMCID: PMC4805513 DOI: 10.1038/ki.2015.283] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2015] [Revised: 07/27/2015] [Accepted: 07/31/2015] [Indexed: 12/22/2022]
Abstract
Individual biomarkers of renal injury are only modestly predictive of acute kidney injury (AKI). Using multiple biomarkers has the potential to improve predictive capacity. In this systematic review, statistical methods of articles developing biomarker combinations to predict acute kidney injury were assessed. We identified and described three potential sources of bias (resubstitution bias, model selection bias and bias due to center differences) that may compromise the development of biomarker combinations. Fifteen studies reported developing kidney injury biomarker combinations for the prediction of AKI after cardiac surgery (8 articles), in the intensive care unit (4 articles) or other settings (3 articles). All studies were susceptible to at least one source of bias and did not account for or acknowledge the bias. Inadequate reporting often hindered our assessment of the articles. We then evaluated, when possible (7 articles), the performance of published biomarker combinations in the TRIBE-AKI cardiac surgery cohort. Predictive performance was markedly attenuated in six out of seven cases. Thus, deficiencies in analysis and reporting are avoidable and care should be taken to provide accurate estimates of risk prediction model performance. Hence, rigorous design, analysis and reporting of biomarker combination studies are essential to realizing the promise of biomarkers in clinical practice.
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Sprague BL, Conant EF, Onega T, Garcia MP, Beaber EF, Herschorn SD, Lehman CD, Tosteson ANA, Lacson R, Schnall MD, Kontos D, Haas JS, Weaver DL, Barlow WE. Variation in Mammographic Breast Density Assessments Among Radiologists in Clinical Practice: A Multicenter Observational Study. Ann Intern Med 2016; 165:457-464. [PMID: 27428568 PMCID: PMC5050130 DOI: 10.7326/m15-2934] [Citation(s) in RCA: 128] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND About half of the United States has legislation requiring radiology facilities to disclose mammographic breast density information to women, often with language recommending discussion of supplemental screening options for women with dense breasts. OBJECTIVE To examine variation in breast density assessment across radiologists in clinical practice. DESIGN Cross-sectional and longitudinal analyses of prospectively collected observational data. SETTING 30 radiology facilities within the 3 breast cancer screening research centers of the Population-based Research Optimizing Screening through Personalized Regimens (PROSPR) consortium. PARTICIPANTS Radiologists who interpreted at least 500 screening mammograms during 2011 to 2013 (n = 83). Data on 216 783 screening mammograms from 145 123 women aged 40 to 89 years were included. MEASUREMENTS Mammographic breast density, as clinically recorded using the 4 Breast Imaging Reporting and Data System categories (heterogeneously dense and extremely dense categories were considered "dense" for analyses), and patient age, race, and body mass index (BMI). RESULTS Overall, 36.9% of mammograms were rated as showing dense breasts. Across radiologists, this percentage ranged from 6.3% to 84.5% (median, 38.7% [interquartile range, 28.9% to 50.9%]), with multivariable adjustment for patient characteristics having little effect (interquartile range, 29.9% to 50.8%). Examination of patient subgroups revealed that variation in density assessment across radiologists was pervasive in all but the most extreme patient age and BMI combinations. Among women with consecutive mammograms interpreted by different radiologists, 17.2% (5909 of 34 271) had discordant assessments of dense versus nondense status. LIMITATION Quantitative measures of mammographic breast density were not available for comparison. CONCLUSION There is wide variation in density assessment across radiologists that should be carefully considered by providers and policymakers when considering supplemental screening strategies. The likelihood of a woman being told she has dense breasts varies substantially according to which radiologist interprets her mammogram. PRIMARY FUNDING SOURCE National Institutes of Health.
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Affiliation(s)
- Brian L Sprague
- From University of Vermont, Burlington, Vermont; Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania; Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire; Fred Hutchinson Cancer Research Center and Cancer Research and Biostatistics, Seattle, Washington; and Massachusetts General Hospital and Brigham and Women's Hospital, Boston, Massachusetts
| | - Emily F Conant
- From University of Vermont, Burlington, Vermont; Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania; Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire; Fred Hutchinson Cancer Research Center and Cancer Research and Biostatistics, Seattle, Washington; and Massachusetts General Hospital and Brigham and Women's Hospital, Boston, Massachusetts
| | - Tracy Onega
- From University of Vermont, Burlington, Vermont; Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania; Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire; Fred Hutchinson Cancer Research Center and Cancer Research and Biostatistics, Seattle, Washington; and Massachusetts General Hospital and Brigham and Women's Hospital, Boston, Massachusetts
| | - Michael P Garcia
- From University of Vermont, Burlington, Vermont; Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania; Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire; Fred Hutchinson Cancer Research Center and Cancer Research and Biostatistics, Seattle, Washington; and Massachusetts General Hospital and Brigham and Women's Hospital, Boston, Massachusetts
| | - Elisabeth F Beaber
- From University of Vermont, Burlington, Vermont; Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania; Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire; Fred Hutchinson Cancer Research Center and Cancer Research and Biostatistics, Seattle, Washington; and Massachusetts General Hospital and Brigham and Women's Hospital, Boston, Massachusetts
| | - Sally D Herschorn
- From University of Vermont, Burlington, Vermont; Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania; Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire; Fred Hutchinson Cancer Research Center and Cancer Research and Biostatistics, Seattle, Washington; and Massachusetts General Hospital and Brigham and Women's Hospital, Boston, Massachusetts
| | - Constance D Lehman
- From University of Vermont, Burlington, Vermont; Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania; Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire; Fred Hutchinson Cancer Research Center and Cancer Research and Biostatistics, Seattle, Washington; and Massachusetts General Hospital and Brigham and Women's Hospital, Boston, Massachusetts
| | - Anna N A Tosteson
- From University of Vermont, Burlington, Vermont; Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania; Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire; Fred Hutchinson Cancer Research Center and Cancer Research and Biostatistics, Seattle, Washington; and Massachusetts General Hospital and Brigham and Women's Hospital, Boston, Massachusetts
| | - Ronilda Lacson
- From University of Vermont, Burlington, Vermont; Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania; Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire; Fred Hutchinson Cancer Research Center and Cancer Research and Biostatistics, Seattle, Washington; and Massachusetts General Hospital and Brigham and Women's Hospital, Boston, Massachusetts
| | - Mitchell D Schnall
- From University of Vermont, Burlington, Vermont; Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania; Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire; Fred Hutchinson Cancer Research Center and Cancer Research and Biostatistics, Seattle, Washington; and Massachusetts General Hospital and Brigham and Women's Hospital, Boston, Massachusetts
| | - Despina Kontos
- From University of Vermont, Burlington, Vermont; Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania; Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire; Fred Hutchinson Cancer Research Center and Cancer Research and Biostatistics, Seattle, Washington; and Massachusetts General Hospital and Brigham and Women's Hospital, Boston, Massachusetts
| | - Jennifer S Haas
- From University of Vermont, Burlington, Vermont; Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania; Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire; Fred Hutchinson Cancer Research Center and Cancer Research and Biostatistics, Seattle, Washington; and Massachusetts General Hospital and Brigham and Women's Hospital, Boston, Massachusetts
| | - Donald L Weaver
- From University of Vermont, Burlington, Vermont; Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania; Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire; Fred Hutchinson Cancer Research Center and Cancer Research and Biostatistics, Seattle, Washington; and Massachusetts General Hospital and Brigham and Women's Hospital, Boston, Massachusetts
| | - William E Barlow
- From University of Vermont, Burlington, Vermont; Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania; Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire; Fred Hutchinson Cancer Research Center and Cancer Research and Biostatistics, Seattle, Washington; and Massachusetts General Hospital and Brigham and Women's Hospital, Boston, Massachusetts
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Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports. Mol Psychiatry 2016; 21:1366-71. [PMID: 26728563 PMCID: PMC4935654 DOI: 10.1038/mp.2015.198] [Citation(s) in RCA: 125] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/22/2015] [Revised: 09/30/2015] [Accepted: 10/26/2015] [Indexed: 01/01/2023]
Abstract
Heterogeneity of major depressive disorder (MDD) illness course complicates clinical decision-making. Although efforts to use symptom profiles or biomarkers to develop clinically useful prognostic subtypes have had limited success, a recent report showed that machine-learning (ML) models developed from self-reports about incident episode characteristics and comorbidities among respondents with lifetime MDD in the World Health Organization World Mental Health (WMH) Surveys predicted MDD persistence, chronicity and severity with good accuracy. We report results of model validation in an independent prospective national household sample of 1056 respondents with lifetime MDD at baseline. The WMH ML models were applied to these baseline data to generate predicted outcome scores that were compared with observed scores assessed 10-12 years after baseline. ML model prediction accuracy was also compared with that of conventional logistic regression models. Area under the receiver operating characteristic curve based on ML (0.63 for high chronicity and 0.71-0.76 for the other prospective outcomes) was consistently higher than for the logistic models (0.62-0.70) despite the latter models including more predictors. A total of 34.6-38.1% of respondents with subsequent high persistence chronicity and 40.8-55.8% with the severity indicators were in the top 20% of the baseline ML-predicted risk distribution, while only 0.9% of respondents with subsequent hospitalizations and 1.5% with suicide attempts were in the lowest 20% of the ML-predicted risk distribution. These results confirm that clinically useful MDD risk-stratification models can be generated from baseline patient self-reports and that ML methods improve on conventional methods in developing such models.
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Relationship of Predicted Risk of Developing Invasive Breast Cancer, as Assessed with Three Models, and Breast Cancer Mortality among Breast Cancer Patients. PLoS One 2016; 11:e0160966. [PMID: 27560501 PMCID: PMC4999085 DOI: 10.1371/journal.pone.0160966] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2016] [Accepted: 07/27/2016] [Indexed: 11/19/2022] Open
Abstract
PURPOSE Breast cancer risk prediction models are used to plan clinical trials and counsel women; however, relationships of predicted risks of breast cancer incidence and prognosis after breast cancer diagnosis are unknown. METHODS Using largely pre-diagnostic information from the Breast Cancer Surveillance Consortium (BCSC) for 37,939 invasive breast cancers (1996-2007), we estimated 5-year breast cancer risk (<1%; 1-1.66%; ≥1.67%) with three models: BCSC 1-year risk model (BCSC-1; adapted to 5-year predictions); Breast Cancer Risk Assessment Tool (BCRAT); and BCSC 5-year risk model (BCSC-5). Breast cancer-specific mortality post-diagnosis (range: 1-13 years; median: 5.4-5.6 years) was related to predicted risk of developing breast cancer using unadjusted Cox proportional hazards models, and in age-stratified (35-44; 45-54; 55-69; 70-89 years) models adjusted for continuous age, BCSC registry, calendar period, income, mode of presentation, stage and treatment. Mean age at diagnosis was 60 years. RESULTS Of 6,021 deaths, 2,993 (49.7%) were ascribed to breast cancer. In unadjusted case-only analyses, predicted breast cancer risk ≥1.67% versus <1.0% was associated with lower risk of breast cancer death; BCSC-1: hazard ratio (HR) = 0.82 (95% CI = 0.75-0.90); BCRAT: HR = 0.72 (95% CI = 0.65-0.81) and BCSC-5: HR = 0.84 (95% CI = 0.75-0.94). Age-stratified, adjusted models showed similar, although mostly non-significant HRs. Among women ages 55-69 years, HRs approximated 1.0. Generally, higher predicted risk was inversely related to percentages of cancers with unfavorable prognostic characteristics, especially among women 35-44 years. CONCLUSIONS Among cases assessed with three models, higher predicted risk of developing breast cancer was not associated with greater risk of breast cancer death; thus, these models would have limited utility in planning studies to evaluate breast cancer mortality reduction strategies. Further, when offering women counseling, it may be useful to note that high predicted risk of developing breast cancer does not imply that if cancer develops it will behave aggressively.
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Armero C, Forné C, Rué M, Forte A, Perpiñán H, Gómez G, Baré M. Bayesian joint ordinal and survival modeling for breast cancer risk assessment. Stat Med 2016; 35:5267-5282. [PMID: 27523800 PMCID: PMC5129536 DOI: 10.1002/sim.7065] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2015] [Revised: 05/18/2016] [Accepted: 07/04/2016] [Indexed: 11/22/2022]
Abstract
We propose a joint model to analyze the structure and intensity of the association between longitudinal measurements of an ordinal marker and time to a relevant event. The longitudinal process is defined in terms of a proportional‐odds cumulative logit model. Time‐to‐event is modeled through a left‐truncated proportional‐hazards model, which incorporates information of the longitudinal marker as well as baseline covariates. Both longitudinal and survival processes are connected by means of a common vector of random effects. General inferences are discussed under the Bayesian approach and include the posterior distribution of the probabilities associated to each longitudinal category and the assessment of the impact of the baseline covariates and the longitudinal marker on the hazard function. The flexibility provided by the joint model makes possible to dynamically estimate individual event‐free probabilities and predict future longitudinal marker values. The model is applied to the assessment of breast cancer risk in women attending a population‐based screening program. The longitudinal ordinal marker is mammographic breast density measured with the Breast Imaging Reporting and Data System (BI‐RADS) scale in biennial screening exams. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.
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Affiliation(s)
- C Armero
- Department of Statistics and Operational Research, Universitat de València, Doctor Moliner, 50, 46100, Burjassot, Spain.
| | - C Forné
- Department of Basic Medical Sciences, Universitat de Lleida-IRBLleida, Avda. Rovira Roure, 80, 25198, Lleida, Spain.,Oblikue Consulting, Barcelona, Spain
| | - M Rué
- Department of Basic Medical Sciences, Universitat de Lleida-IRBLleida, Avda. Rovira Roure, 80, 25198, Lleida, Spain.,Health Services Research Network in Chronic Diseases (REDISSEC), Spain
| | - A Forte
- Department of Statistics and Operational Research, Universitat de València, Doctor Moliner, 50, 46100, Burjassot, Spain
| | - H Perpiñán
- Department of Statistics and Operational Research, Universitat de València, Doctor Moliner, 50, 46100, Burjassot, Spain.,Fundación para el Fomento de la Investigación Sanitaria y Biomédica (FISABIO), Generalitat Valenciana, Spain
| | - G Gómez
- Department of Statistics and Operations Research, Universitat Politècnica de Catalunya, Barcelona, Spain
| | - M Baré
- Clinical Epidemiology and Cancer Screening, Corporació Sanitària Parc Taulí-UAB, Sabadell, Parc Taulí s/n, Sabadell, 08208, Spain
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Delaloge S, Bachelot T, Bidard FC, Espie M, Brain E, Bonnefoi H, Gligorov J, Dalenc F, Hardy-Bessard AC, Azria D, Jacquin JP, Lemonnier J, Jacot W, Goncalves A, Coutant C, Ganem G, Petit T, Penault-Llorca F, Debled M, Campone M, Levy C, Coudert B, Lortholary A, Venat-Bouvet L, Grenier J, Bourgeois H, Asselain B, Arvis J, Castro M, Tardivon A, Cox DG, Arveux P, Balleyguier C, André F, Rouzier R. [Breast cancer screening: On our way to the future]. Bull Cancer 2016; 103:753-63. [PMID: 27473920 DOI: 10.1016/j.bulcan.2016.06.005] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2016] [Revised: 06/02/2016] [Accepted: 06/19/2016] [Indexed: 01/24/2023]
Abstract
Breast cancer remains a potentially lethal disease, which requires aggressive treatments and is associated with long-term consequences. Its prognosis is linked to both tumor biology and burden at diagnosis. Although treatments have allowed important improvements in prognosis over the past 20 years, breast cancer screening remains necessary. Mammographic screening allows earlier stage diagnoses and a decrease of breast cancer specific mortality. However, breast cancer screening modalities should be revised with the objective to address demonstrated limitations of mammographic screening (limited benefit, imperfect sensitivity and specificity, overdiagnoses, radiation-induced morbidity). Furthermore, both objective and perceived performances of screening procedures should be improved. Numerous large international efforts are ongoing, leading to scientific progresses that should have rapid clinical implications in this area. Among them is improvement of imaging techniques performance, development of real time diagnosis, and development of new non radiological screening techniques such as the search for circulating tumor DNA, development of biomarkers able to allow precise risk evaluation and stratified screening. As well, overtreatment is currently addressed by biomarker-based de-escalation clinical trials. These advances need to be associated with strong societal support, as well as major paradigm changes regarding the way health and cancer prevention is perceived by individuals.
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Affiliation(s)
- Suzette Delaloge
- Université Paris Saclay, institut Gustave-Roussy, département de médecine oncologique, Inserm U981, 114, rue Edouard-Vaillant, 94800 Villejuif, France.
| | - Thomas Bachelot
- Centre Léon-Bérard, département de cancérologie médicale, 28, rue Laënnec, 69008 Lyon cedex 08, France
| | - François-Clément Bidard
- Université de recherche Paris, sciences et lettres, institut Curie, 26, rue d'Ulm, 75005 Paris, France
| | - Marc Espie
- Hôpital Saint-Louis, 1, avenue Claude-Vellefaux, 75010 Paris, France
| | - Etienne Brain
- Institut Curie, Saint-Cloud, 35, rue Dailly, 92210 Saint-Cloud, France; Université Versailles-Saint-Quentin, 78180 Montigny-le-Bretonneux, France
| | - Hervé Bonnefoi
- Université de Bordeaux, institut Bergonie, 229, cours de l'Argonne, 33000 Bordeaux, France
| | - Joseph Gligorov
- Hôpital Tenon, université Paris-Sorbonne, Inserm U938, 4, rue de la Chine, 75020 Paris, France
| | - Florence Dalenc
- Institut universitaire du cancer-Toulouse oncopole, 1, avenue Irène-Joliot-Curie, 31059 Toulouse cedex 9, France
| | | | - David Azria
- Université de Montpellier, institut du cancer, IRCM U1194, 34298 Montpellier, France
| | - Jean-Philippe Jacquin
- Institut de cancérologie de la Loire, 108 B, avenue Albert-Raimond, 42270 Saint-Priest-en-Jarez, France
| | | | - William Jacot
- Université de Montpellier, institut du cancer, IRCM U1194, 34298 Montpellier, France
| | - Anthony Goncalves
- Université Aix-Marseille, institut Paoli-Calmettes, Inserm U1068, 232, boulevard de Sainte-Marguerite, 13009 Marseille, France
| | - Charles Coutant
- Université de Bourgogne, centre Georges-François-Leclerc, 1, rue du Pr-Marion, 21000 Dijon, France
| | - Gérard Ganem
- Centre Jean-Bernard, 9, rue Beauverger, 72000 Le Mans, France
| | - Thierry Petit
- Université de Strasbourg, centre Paul-Strauss, 3, rue de la Porte-de-l'Hôpital, 67000 Strasbourg, France
| | | | - Marc Debled
- Université de Bordeaux, institut Bergonie, 229, cours de l'Argonne, 33000 Bordeaux, France
| | - Mario Campone
- Institut d'oncologie de l'Ouest, Inserm U892, IRT-UN, 8, quai Moncousu, 44007 Nantes cedex, France
| | - Christelle Levy
- Centre François-Baclesse, 3, avenue du Général-Harris, 14000 Caen, France
| | - Bruno Coudert
- Université de Bourgogne, centre Georges-François-Leclerc, 1, rue du Pr-Marion, 21000 Dijon, France
| | - Alain Lortholary
- Centre Catherine-de-Sienne, 2, rue Éric-Tabarly, 44202 Nantes, France
| | - Laurence Venat-Bouvet
- CHU de Limoges, service d'oncologie médicale, 22, avenue Martin-Luther-King, 87000 Limoges, France
| | - Julien Grenier
- Institut Sainte-Catherine, 250, chemin de Baignes-Pieds, 84918 Avignon cedex 9, France
| | | | | | - Johanna Arvis
- Ligue nationale contre le cancer, comité du Lot, 28, boulevard Gambetta, 46000 Cahors, France
| | - Martine Castro
- Europadonna France, 14, rue Corvisart, 75013 Paris, France
| | - Anne Tardivon
- Université de recherche Paris, sciences et lettres, institut Curie, 26, rue d'Ulm, 75005 Paris, France
| | - David G Cox
- Université de Lyon, 69000 Lyon, France; Université Lyon 1, 69100 Villeurbanne, France; Centre de recherche en cancérologie de Lyon, Inserm U1052, CNRS UMR5286, 69000 Lyon, France; Centre Léon-Bérard, 69008 Lyon, France
| | - Patrick Arveux
- Registre de Côte d'Or, centre Georges-François-Leclerc, 1, rue du Pr-Marion, 21000 Dijon, France
| | - Corinne Balleyguier
- Institut Gustave-Roussy, département d'imagerie médicale, 114, rue Edouard-Vaillant, 94800 Villejuif, France
| | - Fabrice André
- Université Paris Saclay, institut Gustave-Roussy, département de médecine oncologique, Inserm U981, 114, rue Edouard-Vaillant, 94800 Villejuif, France
| | - Roman Rouzier
- Université de recherche Paris, sciences et lettres, institut Curie, 26, rue d'Ulm, 75005 Paris, France; Institut Curie, Saint-Cloud, 35, rue Dailly, 92210 Saint-Cloud, France; Université Versailles-Saint-Quentin, 78180 Montigny-le-Bretonneux, France
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Passos IC, Mwangi B, Cao B, Hamilton JE, Wu MJ, Zhang XY, Zunta-Soares GB, Quevedo J, Kauer-Sant'Anna M, Kapczinski F, Soares JC. Identifying a clinical signature of suicidality among patients with mood disorders: A pilot study using a machine learning approach. J Affect Disord 2016; 193:109-16. [PMID: 26773901 PMCID: PMC4744514 DOI: 10.1016/j.jad.2015.12.066] [Citation(s) in RCA: 99] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2015] [Revised: 12/09/2015] [Accepted: 12/26/2015] [Indexed: 12/31/2022]
Abstract
OBJECTIVE A growing body of evidence has put forward clinical risk factors associated with patients with mood disorders that attempt suicide. However, what is not known is how to integrate clinical variables into a clinically useful tool in order to estimate the probability of an individual patient attempting suicide. METHOD A total of 144 patients with mood disorders were included. Clinical variables associated with suicide attempts among patients with mood disorders and demographic variables were used to 'train' a machine learning algorithm. The resulting algorithm was utilized in identifying novel or 'unseen' individual subjects as either suicide attempters or non-attempters. Three machine learning algorithms were implemented and evaluated. RESULTS All algorithms distinguished individual suicide attempters from non-attempters with prediction accuracy ranging between 65% and 72% (p<0.05). In particular, the relevance vector machine (RVM) algorithm correctly predicted 103 out of 144 subjects translating into 72% accuracy (72.1% sensitivity and 71.3% specificity) and an area under the curve of 0.77 (p<0.0001). The most relevant predictor variables in distinguishing attempters from non-attempters included previous hospitalizations for depression, a history of psychosis, cocaine dependence and post-traumatic stress disorder (PTSD) comorbidity. CONCLUSION Risk for suicide attempt among patients with mood disorders can be estimated at an individual subject level by incorporating both demographic and clinical variables. Future studies should examine the performance of this model in other populations and its subsequent utility in facilitating selection of interventions to prevent suicide.
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Affiliation(s)
- Ives Cavalcante Passos
- Center of Excellence on Mood Disorder, Department of Psychiatry and Behavioral Sciences, The University of Texas Science Center at Houston, Houston, Texas, USA,Bipolar Disorder Program and Laboratory of Molecular Psychiatry, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Benson Mwangi
- Center of Excellence on Mood Disorder, Department of Psychiatry and Behavioral Sciences, The University of Texas Health Science Center at Houston, Houston, TX, USA.
| | - Bo Cao
- Center of Excellence on Mood Disorder, Department of Psychiatry and Behavioral Sciences, The University of Texas Science Center at Houston, Houston, Texas, USA
| | - Jane E Hamilton
- Center of Excellence on Mood Disorder, Department of Psychiatry and Behavioral Sciences, The University of Texas Science Center at Houston, Houston, Texas, USA
| | - Mon-Ju Wu
- Center of Excellence on Mood Disorder, Department of Psychiatry and Behavioral Sciences, The University of Texas Science Center at Houston, Houston, Texas, USA
| | - Xiang Yang Zhang
- Center of Excellence on Mood Disorder, Department of Psychiatry and Behavioral Sciences, The University of Texas Science Center at Houston, Houston, Texas, USA,Beijing HuiLongGuan Hospital, Peking University, Beijing, China
| | - Giovana B. Zunta-Soares
- Center of Excellence on Mood Disorder, Department of Psychiatry and Behavioral Sciences, The University of Texas Science Center at Houston, Houston, Texas, USA
| | - Joao Quevedo
- Center of Excellence on Mood Disorder, Department of Psychiatry and Behavioral Sciences, The University of Texas Science Center at Houston, Houston, Texas, USA
| | - Marcia Kauer-Sant'Anna
- Bipolar Disorder Program and Laboratory of Molecular Psychiatry, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Flávio Kapczinski
- Bipolar Disorder Program and Laboratory of Molecular Psychiatry, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Jair C. Soares
- Center of Excellence on Mood Disorder, Department of Psychiatry and Behavioral Sciences, The University of Texas Science Center at Houston, Houston, Texas, USA
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Huh SJ, Oh H, Peterson MA, Almendro V, Hu R, Bowden M, Lis RL, Cotter MB, Loda M, Barry WT, Polyak K, Tamimi RM. The Proliferative Activity of Mammary Epithelial Cells in Normal Tissue Predicts Breast Cancer Risk in Premenopausal Women. Cancer Res 2016; 76:1926-34. [PMID: 26941287 DOI: 10.1158/0008-5472.can-15-1927] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2015] [Accepted: 01/06/2016] [Indexed: 01/09/2023]
Abstract
The frequency and proliferative activity of tissue-specific stem and progenitor cells are suggested to correlate with cancer risk. In this study, we investigated the association between breast cancer risk and the frequency of mammary epithelial cells expressing p27, estrogen receptor (ER), and Ki67 in normal breast tissue. We performed a nested case-control study of 302 women (69 breast cancer cases, 233 controls) who had been initially diagnosed with benign breast disease according to the Nurses' Health Studies. Immunofluorescence for p27, ER, and Ki67 was performed on tissue microarrays constructed from benign biopsies containing normal mammary epithelium and scored by computational image analysis. We found that the frequency of Ki67(+) cells was positively associated with breast cancer risk among premenopausal women [OR = 10.1, 95% confidence interval (CI) = 2.12-48.0]. Conversely, the frequency of ER(+) or p27(+) cells was inversely, but not significantly, associated with subsequent breast cancer risk (ER(+): OR = 0.70, 95% CI, 0.33-1.50; p27(+): OR = 0.89, 95% CI, 0.45-1.75). Notably, high Ki67(+)/low p27(+) and high Ki67(+)/low ER(+) cell frequencies were significantly associated with a 5-fold higher risk of breast cancer compared with low Ki67(+)/low p27(+) and low Ki67(+)/low ER(+) cell frequencies, respectively, among premenopausal women (Ki67(hi)/p27(lo): OR = 5.08, 95% CI, 1.43-18.1; Ki67(hi)/ER(lo): OR = 4.68, 95% CI, 1.63-13.5). Taken together, our data suggest that the fraction of actively cycling cells in normal breast tissue may represent a marker for breast cancer risk assessment, which may therefore impact the frequency of screening procedures in at-risk women. Cancer Res; 76(7); 1926-34. ©2016 AACR.
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Affiliation(s)
- Sung Jin Huh
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts. Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts. Department of Medicine, Harvard Medical School, Boston, Massachusetts
| | - Hannah Oh
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts. Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts. Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, Massachusetts
| | - Michael A Peterson
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts. Center for Molecular Oncologic Pathology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Vanessa Almendro
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts. Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts. Department of Medicine, Harvard Medical School, Boston, Massachusetts
| | - Rong Hu
- Department of Medicine, Harvard Medical School, Boston, Massachusetts. Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts. Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts. Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, Massachusetts
| | - Michaela Bowden
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts. Center for Molecular Oncologic Pathology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Rosina L Lis
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts. Center for Molecular Oncologic Pathology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Maura B Cotter
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts. Center for Molecular Oncologic Pathology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Massimo Loda
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts. Center for Molecular Oncologic Pathology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - William T Barry
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Kornelia Polyak
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts. Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts. Department of Medicine, Harvard Medical School, Boston, Massachusetts. Harvard Stem Cell Institute, Cambridge, Massachusetts.
| | - Rulla M Tamimi
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts. Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts.
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