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Barnard ME, DuPré NC, Heine JJ, Fowler EE, Murthy DJ, Nelleke RL, Chan A, Warner ET, Tamimi RM. Reproductive risk factors for breast cancer and association with novel breast density measurements among Hispanic, Black, and White women. Breast Cancer Res Treat 2024; 204:309-325. [PMID: 38095811 PMCID: PMC10948301 DOI: 10.1007/s10549-023-07174-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 11/02/2023] [Indexed: 12/21/2023]
Abstract
PURPOSE There are differences in the distributions of breast cancer incidence and risk factors by race and ethnicity. Given the strong association between breast density and breast cancer, it is of interest describe racial and ethnic variation in the determinants of breast density. METHODS We characterized racial and ethnic variation in reproductive history and several measures of breast density for Hispanic (n = 286), non-Hispanic Black (n = 255), and non-Hispanic White (n = 1694) women imaged at a single hospital. We quantified associations between reproductive factors and percent volumetric density (PVD), dense volume (DV), non-dense volume (NDV), and a novel measure of pixel intensity variation (V) using multivariable-adjusted linear regression, and tested for statistical heterogeneity by race and ethnicity. RESULTS Reproductive factors most strongly associated with breast density were age at menarche, parity, and oral contraceptive use. Variation by race and ethnicity was most evident for the associations between reproductive factors and NDV (minimum p-heterogeneity:0.008) and V (minimum p-heterogeneity:0.004) and least evident for PVD (minimum p-heterogeneity:0.042) and DV (minimum p-heterogeneity:0.041). CONCLUSION Reproductive choices, particularly those related to childbearing and oral contraceptive use, may contribute to racial and ethnic variation in breast density.
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Affiliation(s)
- Mollie E Barnard
- Slone Epidemiology Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, 02118, USA.
- University of Utah Intermountain Healthcare Department of Population Health Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA.
| | - Natalie C DuPré
- Department of Epidemiology and Population Health, School of Public Health and Information Sciences, University of Louisville, Louisville, KY, USA
| | - John J Heine
- Division of Population Sciences, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Erin E Fowler
- Division of Population Sciences, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Divya J Murthy
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Rebecca L Nelleke
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Ariane Chan
- Volpara Health Technologies Ltd., Wellington, New Zealand
| | - Erica T Warner
- Clinical Translational Epidemiology Unit, Department of Medicine, Mongan Institute, Massachusetts General Hospital, Boston, MA, USA
| | - Rulla M Tamimi
- Department of Population Health Sciences, Weill Cornell Medical, New York, NY, USA
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Warner ET, Rice MS, Zeleznik OA, Fowler EE, Murthy D, Vachon CM, Bertrand KA, Rosner BA, Heine J, Tamimi RM. Automated percent mammographic density, mammographic texture variation, and risk of breast cancer: a nested case-control study. NPJ Breast Cancer 2021; 7:68. [PMID: 34059687 PMCID: PMC8166859 DOI: 10.1038/s41523-021-00272-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Accepted: 05/03/2021] [Indexed: 12/03/2022] Open
Abstract
Percent mammographic density (PMD) is a strong breast cancer risk factor, however, other mammographic features, such as V, the standard deviation (SD) of pixel intensity, may be associated with risk. We assessed whether PMD, automated PMD (APD), and V, yielded independent associations with breast cancer risk. We included 1900 breast cancer cases and 3921 matched controls from the Nurses' Health Study (NHS) and the NHSII. Using digitized film mammograms, we estimated PMD using a computer-assisted thresholding technique. APD and V were determined using an automated computer algorithm. We used logistic regression to generate odds ratios (ORs) and 95% confidence intervals (CIs). Median time from mammogram to diagnosis was 4.1 years (interquartile range: 1.6-6.8 years). PMD (OR per SD:1.52, 95% CI: 1.42, 1.63), APD (OR per SD:1.32, 95% CI: 1.24, 1.41), and V (OR per SD:1.32, 95% CI: 1.24, 1.40) were positively associated with breast cancer risk. Associations for APD were attenuated but remained statistically significant after mutual adjustment for PMD or V. Women in the highest quartile of both APD and V (OR vs Q1/Q1: 2.49, 95% CI: 2.02, 3.06), or PMD and V (OR vs Q1/Q1: 3.57, 95% CI: 2.79, 4.58) had increased breast cancer risk. An automated method of PMD assessment is feasible and yields similar, but somewhat weaker, estimates to a manual measure. PMD, APD and V are each independently, positively associated with breast cancer risk. Women with dense breasts and greater texture variation are at the highest relative risk of breast cancer.
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Affiliation(s)
- Erica T Warner
- Clinical and Translational Epidemiology Unit, Department of Medicine, Mongan Institute, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
| | - Megan S Rice
- Clinical and Translational Epidemiology Unit, Department of Medicine, Mongan Institute, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Oana A Zeleznik
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Erin E Fowler
- Division of Population Sciences, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Divya Murthy
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Celine M Vachon
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | | | - Bernard A Rosner
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - John Heine
- Division of Population Sciences, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Rulla M Tamimi
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
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Fowler EE, Berglund A, Schell MJ, Sellers TA, Eschrich S, Heine J. Empirically-derived synthetic populations to mitigate small sample sizes. J Biomed Inform 2020; 105:103408. [PMID: 32173502 DOI: 10.1016/j.jbi.2020.103408] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Revised: 02/10/2020] [Accepted: 03/10/2020] [Indexed: 01/28/2023]
Abstract
Limited sample sizes can lead to spurious modeling findings in biomedical research. The objective of this work is to present a new method to generate synthetic populations (SPs) from limited samples using matched case-control data (n = 180 pairs), considered as two separate limited samples. SPs were generated with multivariate kernel density estimations (KDEs) with unconstrained bandwidth matrices. We included four continuous variables and one categorical variable for each individual. Bandwidth matrices were determined with Differential Evolution (DE) optimization by covariance comparisons. Four synthetic samples (n = 180) were derived from their respective SPs. Similarity between observed samples with synthetic samples was compared assuming their empirical probability density functions (EPDFs) were similar. EPDFs were compared with the maximum mean discrepancy (MMD) test statistic based on the Kernel Two-Sample Test. To evaluate similarity within a modeling context, EPDFs derived from the Principal Component Analysis (PCA) scores and residuals were summarized with the distance to the model in X-space (DModX) as additional comparisons. Four SPs were generated from each sample. The probability of selecting a replicate when randomly constructing synthetic samples (n = 180) was infinitesimally small. MMD tests indicated that the observed sample EPDFs were similar to the respective synthetic EPDFs. For the samples, PCA scores and residuals did not deviate significantly when compared with their respective synthetic samples. The feasibility of this approach was demonstrated by producing synthetic data at the individual level, statistically similar to the observed samples. The methodology coupled KDE with DE optimization and deployed novel similarity metrics derived from PCA. This approach could be used to generate larger-sized synthetic samples. To develop this approach into a research tool for data exploration purposes, additional evaluation with increased dimensionality is required. Moreover, given a fully specified population, the degree to which individuals can be discarded while synthesizing the respective population accurately will be investigated. When these objectives are addressed, comparisons with other techniques such as bootstrapping will be required for a complete evaluation.
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Affiliation(s)
- Erin E Fowler
- Cancer Epidemiology Department, MCC, Moffitt Cancer Center & Research Institute, 12901 Bruce B. Downs Blvd, Tampa, FL 33612, United States.
| | - Anders Berglund
- Department of Biostatistics and Bioinformatics, MCC, Moffitt Cancer Center & Research Institute, 12901 Bruce B. Downs Blvd, Tampa, FL 33612, United States.
| | - Michael J Schell
- Department of Biostatistics and Bioinformatics, MCC, Moffitt Cancer Center & Research Institute, 12901 Bruce B. Downs Blvd, Tampa, FL 33612, United States.
| | | | - Steven Eschrich
- Department of Biostatistics and Bioinformatics, MCC, Moffitt Cancer Center & Research Institute, 12901 Bruce B. Downs Blvd, Tampa, FL 33612, United States.
| | - John Heine
- Cancer Epidemiology Department, MCC, Moffitt Cancer Center & Research Institute, 12901 Bruce B. Downs Blvd, Tampa, FL 33612, United States.
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Oh H, Rice MS, Warner ET, Bertrand KA, Fowler EE, Eliassen AH, Rosner BA, Heine JJ, Tamimi RM. Early-Life and Adult Anthropometrics in Relation to Mammographic Image Intensity Variation in the Nurses' Health Studies. Cancer Epidemiol Biomarkers Prev 2020; 29:343-351. [PMID: 31826913 PMCID: PMC7007347 DOI: 10.1158/1055-9965.epi-19-0832] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Revised: 09/29/2019] [Accepted: 12/03/2019] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND The V measure captures grayscale intensity variation on a mammogram and is positively associated with breast cancer risk, independent of percent mammographic density (PMD), an established marker of breast cancer risk. We examined whether anthropometrics are associated with V, independent of PMD. METHODS The analysis included 1,700 premenopausal and 1,947 postmenopausal women without breast cancer within the Nurses' Health Study (NHS) and NHSII. Participants recalled their body fatness at ages 5, 10, and 20 years using a 9-level pictogram (level 1: most lean) and reported weight at age 18 years, current adult weight, and adult height. V was estimated by calculating standard deviation of pixels on screening mammograms. Linear mixed models were used to estimate beta coefficients (ß) and 95% confidence intervals (CI) for the relationships between anthropometric measures and V, adjusting for confounders and PMD. RESULTS V and PMD were positively correlated (Spearman r = 0.60). Higher average body fatness at ages 5 to 10 years (level ≥ 4.5 vs. 1) was significantly associated with lower V in premenopausal (ß = -0.32; 95% CI, -0.48 to -0.16) and postmenopausal (ß = -0.24; 95% CI, -0.37 to -0.10) women, independent of current body mass index (BMI) and PMD. Similar inverse associations were observed with average body fatness at ages 10 to 20 years and BMI at age 18 years. Current BMI was inversely associated with V, but the associations were largely attenuated after adjustment for PMD. Height was not associated with V. CONCLUSIONS Our data suggest that early-life body fatness may reflect lifelong impact on breast tissue architecture beyond breast density. However, further studies are needed to confirm the results. IMPACT This study highlights strong inverse associations of early-life adiposity with mammographic image intensity variation.
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Affiliation(s)
- Hannah Oh
- Department of Public Health Sciences, Graduate School, Korea University, Seoul, Republic of Korea.
- Division of Health Policy and Management, College of Health Sciences, Korea University, Seoul, Republic of Korea
| | - Megan S Rice
- Biostatistics, Sanofi Genzyme, Cambridge, Massachusetts
| | - Erica T Warner
- Department of Medicine, Harvard Medical School and Massachusetts General Hospital, Boston, Massachusetts
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | | | - Erin E Fowler
- Division of Population Sciences, Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - A Heather Eliassen
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Bernard A 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
| | - John J Heine
- Division of Population Sciences, Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Rulla M Tamimi
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
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Fowler EE, Smallwood A, Khan N, Miltich C, Drukteinis J, Sellers TA, Heine J. Calibrated Breast Density Measurements. Acad Radiol 2019; 26:1181-1190. [PMID: 30545682 PMCID: PMC6557684 DOI: 10.1016/j.acra.2018.10.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Revised: 09/28/2018] [Accepted: 10/04/2018] [Indexed: 11/20/2022]
Abstract
RATIONALE AND OBJECTIVES Mammographic density is an important risk factor for breast cancer, but translation to the clinic requires assurance that prior work based on mammography is applicable to current technologies. The purpose of this work is to evaluate whether a calibration methodology developed previously produces breast density metrics predictive of breast cancer risk when applied to a case-control study. MATERIALS AND METHODS A matched case control study (n = 319 pairs) was used to evaluate two calibrated measures of breast density. Two-dimensional mammograms were acquired from six Hologic mammography units: three conventional Selenia two-dimensional full-field digital mammography systems and three Dimensions digital breast tomosynthesis systems. We evaluated the capability of two calibrated breast density measures to quantify breast cancer risk: the mean (PGm) and standard deviation (PGsd) of the calibrated pixels. Matching variables included age, hormone replacement therapy usage/duration, screening history, and mammography unit. Calibrated measures were compared to the percentage of breast density (PD) determined with the operator-assisted Cumulus method. Conditional logistic regression was used to generate odds ratios (ORs) from continuous and quartile (Q) models with 95% confidence intervals. The area under the receiver operating characteristic curve (Az) was also used as a comparison metric. Both univariate models and models adjusted for body mass index and ethnicity were evaluated. RESULTS In adjusted models, both PGsd and PD were statistically significantly associated with breast cancer with similar Az of 0.61-0.62. The corresponding ORs and confidence intervals were also similar. For PGsd, the OR was 1.34 (1.09, 1.66) for the continuous measure and 1.83 (1.11, 3.02), 2.19 (1.28, 3.73), and 2.20 (1.26, 3.85) for Q2-Q4. For PD, the OR was 1.43 (1.16, 1.76) for the continuous measure and 0.84 (0.52, 1.38), 1.96 (1.19, 3.23), and 2.27 (1.29, 4.00) for Q2-Q4. The results for PGm were slightly attenuated and not statistically significant. The OR was 1.22 (0.99, 1.51) with Az = 0.60 for the continuous measure and 1.24 (0.78, 1.97), 0.98 (0.60, 1.61), and 1.26, (0.77, 2.07) for Q2-Q4 with Az = 0.60. CONCLUSION The calibrated PGsd measure provided significant associations with breast cancer comparable to those given by PD. The calibrated PGm performed slightly worse. These findings indicate that the calibration approach developed previously replicates under more general conditions.
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Affiliation(s)
| | | | | | | | - Jennifer Drukteinis
- Moffitt Cancer Center & Research Institute, 12901 Bruce B. Downs Blvd, Tampa FL, 33612 (MCC)
| | | | - John Heine
- Corresponding Author information: John Heine, PhD, Moffitt Cancer Center & Research Institute, 12901 Bruce B, Downs Blvd, Mail Stop: Can/Cont, Tampa, FL 33612, Phone: 813-745-6719
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Chang EC, Lin J, Fowler EE, Yu EA, Yu T, Jilani Z, Kahle ER, Hirsch JK. Sexual Assault and Depressive Symptoms in College Students: Do Psychological Needs Account for the Relationship? Soc Work 2015; 60:211-218. [PMID: 26173362 DOI: 10.1093/sw/swv017] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
In this study, authors examined basic psychological needs (namely, competence, autonomy, and relatedness) as potential mediators of the association between sexual assault and depressive symptoms in a sample of 342 college students. Results from conducting a multiple mediation test provided support for partial mediation involving the indirect effects of competence and autonomy. In contrast, no support for mediation was found involving relatedness. It is notable that sexual assault remained a significant predictor of depressive symptoms in students. Therefore, findings indicate how sexual assault may both directly and indirectly (through psychological needs) lead to greater depressive symptoms in students. Authors concluded the article with a discussion of the implications of their findings for expanding the study of basic psychological needs in college students and the need for greater efforts to prevent and treat sexual assault on campus.
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Chang EC, Jilani Z, Fowler EE, Yu T, Chia SW, Yu EA, McCabe HK, Hirsch JK. The relationship between multidimensional spirituality and depressive symptoms in college students: Examining hope agency and pathways as potential mediators. The Journal of Positive Psychology 2015. [DOI: 10.1080/17439760.2015.1037859] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Chang EC, Yu T, Jilani Z, Fowler EE, Yu EA, Lin J, Hirsch JK. Hope Under Assault: Understanding the Impact of Sexual Assault on the Relation Between Hope and Suicidal Risk in College Students. Journal of Social and Clinical Psychology 2015. [DOI: 10.1521/jscp.2015.34.3.221] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Chang EC, Jilani Z, Yu T, Fowler EE, Lin J, Webb JR, Hirsch JK. Fundamental dimensions of personality underlying spirituality: Further evidence for the construct validity of the RiTE measure of spirituality. Personality and Individual Differences 2015. [DOI: 10.1016/j.paid.2014.11.027] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Fowler EE, Sellers TA, Lu B, Heine JJ. Breast Imaging Reporting and Data System (BI-RADS) breast composition descriptors: automated measurement development for full field digital mammography. Med Phys 2014; 40:113502. [PMID: 24320473 DOI: 10.1118/1.4824319] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE The Breast Imaging Reporting and Data System (BI-RADS) breast composition descriptors are used for standardized mammographic reporting and are assessed visually. This reporting is clinically relevant because breast composition can impact mammographic sensitivity and is a breast cancer risk factor. New techniques are presented and evaluated for generating automated BI-RADS breast composition descriptors using both raw and calibrated full field digital mammography (FFDM) image data. METHODS A matched case-control dataset with FFDM images was used to develop three automated measures for the BI-RADS breast composition descriptors. Histograms of each calibrated mammogram in the percent glandular (pg) representation were processed to create the new BR(pg) measure. Two previously validated measures of breast density derived from calibrated and raw mammograms were converted to the new BR(vc) and BR(vr) measures, respectively. These three measures were compared with the radiologist-reported BI-RADS compositions assessments from the patient records. The authors used two optimization strategies with differential evolution to create these measures: method-1 used breast cancer status; and method-2 matched the reported BI-RADS descriptors. Weighted kappa (κ) analysis was used to assess the agreement between the new measures and the reported measures. Each measure's association with breast cancer was evaluated with odds ratios (ORs) adjusted for body mass index, breast area, and menopausal status. ORs were estimated as per unit increase with 95% confidence intervals. RESULTS The three BI-RADS measures generated by method-1 had κ between 0.25-0.34. These measures were significantly associated with breast cancer status in the adjusted models: (a) OR = 1.87 (1.34, 2.59) for BR(pg); (b) OR = 1.93 (1.36, 2.74) for BR(vc); and (c) OR = 1.37 (1.05, 1.80) for BR(vr). The measures generated by method-2 had κ between 0.42-0.45. Two of these measures were significantly associated with breast cancer status in the adjusted models: (a) OR = 1.95 (1.24, 3.09) for BR(pg); (b) OR = 1.42 (0.87, 2.32) for BR(vc); and (c) OR = 2.13 (1.22, 3.72) for BR(vr). The radiologist-reported measures from the patient records showed a similar association, OR = 1.49 (0.99, 2.24), although only borderline statistically significant. CONCLUSIONS A general framework was developed and validated for converting calibrated mammograms and continuous measures of breast density to fully automated approximations for the BI-RADS breast composition descriptors. The techniques are general and suitable for a broad range of clinical and research applications.
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Affiliation(s)
- E E Fowler
- Department of Cancer Epidemiology, Division of Population Sciences, H. Lee Moffitt Cancer Center, Tampa, Florida 33612
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Behera M, Fowler EE, Owonikoko TK, Land WH, Mayfield W, Chen Z, Khuri FR, Ramalingam SS, Heine JJ. Statistical learning methods as a preprocessing step for survival analysis: evaluation of concept using lung cancer data. Biomed Eng Online 2011; 10:97. [PMID: 22067671 PMCID: PMC3280940 DOI: 10.1186/1475-925x-10-97] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2011] [Accepted: 11/08/2011] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Statistical learning (SL) techniques can address non-linear relationships and small datasets but do not provide an output that has an epidemiologic interpretation. METHODS A small set of clinical variables (CVs) for stage-1 non-small cell lung cancer patients was used to evaluate an approach for using SL methods as a preprocessing step for survival analysis. A stochastic method of training a probabilistic neural network (PNN) was used with differential evolution (DE) optimization. Survival scores were derived stochastically by combining CVs with the PNN. Patients (n = 151) were dichotomized into favorable (n = 92) and unfavorable (n = 59) survival outcome groups. These PNN derived scores were used with logistic regression (LR) modeling to predict favorable survival outcome and were integrated into the survival analysis (i.e. Kaplan-Meier analysis and Cox regression). The hybrid modeling was compared with the respective modeling using raw CVs. The area under the receiver operating characteristic curve (Az) was used to compare model predictive capability. Odds ratios (ORs) and hazard ratios (HRs) were used to compare disease associations with 95% confidence intervals (CIs). RESULTS The LR model with the best predictive capability gave Az = 0.703. While controlling for gender and tumor grade, the OR = 0.63 (CI: 0.43, 0.91) per standard deviation (SD) increase in age indicates increasing age confers unfavorable outcome. The hybrid LR model gave Az = 0.778 by combining age and tumor grade with the PNN and controlling for gender. The PNN score and age translate inversely with respect to risk. The OR = 0.27 (CI: 0.14, 0.53) per SD increase in PNN score indicates those patients with decreased score confer unfavorable outcome. The tumor grade adjusted hazard for patients above the median age compared with those below the median was HR = 1.78 (CI: 1.06, 3.02), whereas the hazard for those patients below the median PNN score compared to those above the median was HR = 4.0 (CI: 2.13, 7.14). CONCLUSION We have provided preliminary evidence showing that the SL preprocessing may provide benefits in comparison with accepted approaches. The work will require further evaluation with varying datasets to confirm these findings.
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Affiliation(s)
- Madhusmita Behera
- Department of Hematology and Medical Oncology, Emory University, Winship Cancer Institute, 1365 Clifton Road NE, Rm C-3090, Atlanta, GA 30322, USA
| | - Erin E Fowler
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, MRC-CANCONT, Tampa, FL 33612, USA
| | - Taofeek K Owonikoko
- Department of Hematology and Medical Oncology, Emory University, Winship Cancer Institute, 1365 Clifton Road NE, Rm C-3090, Atlanta, GA 30322, USA
| | - Walker H Land
- Thomas J. Watson School of Engineering, Binghamton University, State University of New York, PO Box 6000, Binghamton, NY 13902-6000, USA
| | | | - Zhengjia Chen
- Biostatistics & Bioinformatics Shared Resource at Wnship Cancer Institute, Department of Biostatistics & Bioinformatics, Rollins School of Public Health, Atlanta, GA, USA
| | - Fadlo R Khuri
- Department of Hematology and Medical Oncology, Emory University, Winship Cancer Institute, 1365 Clifton Road NE, Rm C-3090, Atlanta, GA 30322, USA
| | - Suresh S Ramalingam
- Department of Hematology and Medical Oncology, Emory University, Winship Cancer Institute, 1365 Clifton Road NE, Rm C-3090, Atlanta, GA 30322, USA
| | - John J Heine
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, MRC-CANCONT, Tampa, FL 33612, USA
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Zoble RG, Hariman RJ, Fowler EE, Curtis AB. Abstract P357: A Simple Mortality Risk Score for Calcific Aortic Stenosis. Circ Cardiovasc Qual Outcomes 2011. [DOI: 10.1161/circoutcomes.4.suppl_1.ap357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background:
Calcific aortic stenosis (CAS) of the elderly is associated with high mortality. It would be useful to have a simple and accurate tool (risk score) to identify risk.
Methods:
We retrospectively reviewed all echocardiograms at our hospital from October 2006 to March 2008 and identified 208 veterans aged 65 years or older with CAS, aortic valve peak gradient >30 mmHg, but no prior aortic valve surgery. Aortic valve peak gradient, age, clinical co-morbidities, tobacco and alcohol use, creatinine (Cr) and hemoglobin (Hb) data were analyzed. Variables with significant odds ratios for 1-year mortality were entered into a regression model.
Results:
All-cause 1-year mortality was 16.3%. Significant odds ratios for mortality were found for Hb =< 10.0 g/dL (OR=13.3, p<0.0001); alcohol abuse (OR=3.76; p=0.008) and heart failure (HF) history (OR=2.88, p=0.027). The c-statistic for this model was good (c=0.848). Aortic valve peak gradient did not add to the model, but all subjects were required to have a gradient >30 mmHg to be included. A risk score was developed by assigning 2 points to Hb of 10 g/dL or lower, 1 point to alcohol abuse, and 1 point to HF. The figure plots survival vs. risk scores and predicts very low (2.9%), low (14.4%), intermediate (41.2%), high (52.4%), and very high (100%) 1-year mortality for scores of 0, 1, 2, 3 and 4, respectively. This represents a >30-fold range of mortality risk.
Conclusion:
For CAS patients 65 years or older with aortic valve peak gradient >30 mmHg and no prior aortic valve surgery, our simple risk score identifies a wide range in 1-year mortality risk and allows prediction of mortality risk with a high degree of predictive accuracy (c-statistic=0.85).
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Zoble RG, Fowler EE, Meadows J, Zoble A, Foulis P, Neugaard BI, Hariman RJ. Abstract P122: Predictors of 30-Day Mortality After Heart Failure Hospitalization. Circ Cardiovasc Qual Outcomes 2011. [DOI: 10.1161/circoutcomes.4.suppl_2.ap122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background:
Heart failure (HF) has a high rate of mortality. It would be useful to have, at the time of a HF admission, a method of predicting post-discharge short-term mortality risk.
Methods:
We studied 947 Veterans hospitalized for HF at our VA medical center from January 2004 to December 2008, survived to discharge, had an ICD-9 code for HF as the primary discharge diagnosis, and had complete data for comorbidities, labs, medications and ECG findings. Mortality at 30-days post-discharge was determined and multivariable analyses identified independent predictors of mortality by logistic regression. These were used to develop a mortality risk score.
Results:
Mortality at 30-days occurred in 3.9% (37/947). Independent predictors (p-value <0.05) were substance abuse (OR = 3.10), abnormal admission serum sodium (OR=2.56), abnormal admission troponin (OR = 2.15), absence of dyslipidemia (OR = 2.04), not on a calcium channel blocker at admission (OR = 3.33), and not on an oral anticoagulant at admission (OR = 5.26). Each independent predictor was assigned 1-point and the resulting figure shows the rates of 30-day mortality to be 0.49%, 5.33% and 19.15% for those with risk scores of 0 to 2, 3 or 4, and ≥5, respectively. This represents a 39-fold difference in risk between the low-risk and high-risk patients. The c-statistic for the model was good (c = 0.796).
Conclusions:
Risk for post-discharge short-term mortality can be accurately predicted at the time of initial HF admission by a risk score employing admission labs, co-morbidities and medications. Patients identified as high risk at the time of admission might benefit from more intense inpatient evaluation and closer outpatient care.
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