Reproductive risk factors for breast cancer and association with novel breast density measurements among Hispanic, Black, and White women.

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.

Empirically-derived synthetic populations to mitigate small sample sizes.

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.

A comparison of calibration data from full field digital mammography units for breast density measurements.

BACKGROUND: Breast density is a significant breast cancer risk factor measured from mammograms. The most appropriate method for measuring breast density for risk applications is still under investigation. Calibration standardizes mammograms to account for acquisition technique differences prior to making breast density measurements. We evaluated whether a calibration methodology developed for an indirect x-ray conversion full field digital mammography (FFDM) technology applies to direct x-ray conversion FFDM systems. METHODS: Breast tissue equivalent (BTE) phantom images were used to establish calibration datasets for three similar direct x-ray conversion FFDM systems. The calibration dataset for each unit is a function of the target/filter combination, x-ray tube voltage, current × time (mAs), phantom height, and two detector fields of view (FOVs). Methods were investigated to reduce the amount of calibration data by restricting the height, mAs, and FOV sampling. Calibration accuracy was evaluated with mixture phantoms. We also compared both intra- and inter-system calibration characteristics and accuracy. RESULTS: Calibration methods developed previously apply to direct x-ray conversion systems with modification. Calibration accuracy was largely within the acceptable range of ± 4 standardized units from the ideal value over the entire acquisition parameter space for the direct conversion units. Acceptable calibration accuracy was maintained with a cubic-spline height interpolation, representing a modification to previous work. Calibration data is unit specific, can be acquired with the large FOV, and requires a minimum of one reference mAs sample. The mAs sampling, calibration accuracy, and the necessity for machine specific calibration data are common characteristics and in agreement with our previous work. CONCLUSION: The generality of our calibration approach was established under ideal conditions. Evaluation with patient data using breast cancer status as the endpoint is required to demonstrate that the approach produces a breast density measure associated with breast cancer.

Techniques to produce and evaluate realistic multivariate synthetic data.

Data modeling requires a sufficient sample size for reproducibility. A small sample size can inhibit model evaluation. A synthetic data generation technique addressing this small sample size problem is evaluated: from the space of arbitrarily distributed samples, a subgroup (class) has a latent multivariate normal characteristic; synthetic data can be generated from this class with univariate kernel density estimation (KDE); and synthetic samples are statistically like their respective samples. Three samples (n = 667) were investigated with 10 input variables (X). KDE was used to augment the sample size in X. Maps produced univariate normal variables in Y. Principal component analysis in Y produced uncorrelated variables in T, where the probability density functions were approximated as normal and characterized; synthetic data was generated with normally distributed univariate random variables in T. Reversing each step produced synthetic data in Y and X. All samples were approximately multivariate normal in Y, permitting the generation of synthetic data. Probability density function and covariance comparisons showed similarity between samples and synthetic samples. A class of samples has a latent normal characteristic. For such samples, this approach offers a solution to the small sample size problem. Further studies are required to understand this latent class.

Breast Density Analysis Using Digital Breast Tomosynthesis.

We evaluated an automated percentage of breast density (BD) technique (PDa) with digital breast tomosynthesis (DBT) data. The approach is based on the wavelet expansion followed by analyzing signal dependent noise. Several measures were investigated as risk factors: normalized volumetric; total dense volume; average of the DBT slices (slice-mean); a two-dimensional (2D) metric applied to the synthetic images; and the mean and standard deviations of the pixel values. Volumetric measures were derived theoretically, and PDa was modeled as a function of compressed breast thickness. An alternative method for constructing synthetic 2D mammograms was investigated using the volume results. A matched case-control study (n = 426 pairs) was analyzed. Conditional logistic regression modeling, controlling body mass index and ethnicity, was used to estimate odds ratios (ORs) for each measure with 95% confidence intervals provided parenthetically. There were several significant findings: volumetric measure [OR = 1.43 (1.18, 1.72)], which produced an identical OR as the slice-mean measure as predicted; [OR =1.44 (1.18, 1.75)] when applied to the synthetic images; and mean of the pixel values (volume or 2D synthetic) [ORs ~ 1.31 (1.09, 1.57)]. PDa was modeled as 2nd degree polynomial (concave-down): its maximum value occurred at 0.41×(compressed breast thickness), which was similar across case-control groups, and was significant from this position [OR = 1.47 (1.21, 1.78)]. A standardized 2D synthetic image was produced, where each pixel value represents the percentage of BD above its location. The significant findings indicate the validity of the technique. Derivations supported by empirical analyses produced a new synthetic 2D standardized image technique. Ancillary to the objectives, the results provide evidence for understanding the percentage of BD measure applied to 2D mammograms. Notwithstanding the findings, the study design provides a template for investigating other measures such as texture.

Breast density analysis of digital breast tomosynthesis.

Mammography shifted to digital breast tomosynthesis (DBT) in the US. An automated percentage of breast density (PD) technique designed for two-dimensional (2D) applications was evaluated with DBT using several breast cancer risk prediction measures: normalized-volumetric; dense volume; applied to the volume slices and averaged (slice-mean); and applied to synthetic 2D images. Volumetric measures were derived theoretically. PD was modeled as a function of compressed breast thickness (CBT). The mean and standard deviation of the pixel values were investigated. A matched case-control (CC) study (n = 426 pairs) was evaluated. Odd ratios (ORs) were estimated with 95% confidence intervals. ORs were significant for PD: identical for volumetric and slice-mean measures [OR = 1.43 (1.18, 1.72)] and [OR = 1.44 (1.18, 1.75)] for synthetic images. A 2nd degree polynomial (concave-down) was used to model PD as a function of CBT: location of the maximum PD value was similar across CCs, occurring at 0.41 × CBT, and PD was significant [OR = 1.47 (1.21, 1.78)]. The means from the volume and synthetic images were also significant [ORs ~ 1.31 (1.09, 1.57)]. An alternative standardized 2D synthetic image was constructed, where each pixel value represents the percentage of breast density above its location. Several measures were significant and an alternative method for constructing a standardized 2D synthetic image was produced.

Statistical learning methods as a preprocessing step for survival analysis: evaluation of concept using lung cancer data.

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.

Automated percent mammographic density, mammographic texture variation, and risk of breast cancer: a nested case-control study.

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.

Early-Life and Adult Anthropometrics in Relation to Mammographic Image Intensity Variation in the Nurses' Health Studies.

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.

Technical challenges in generalizing calibration techniques for breast density measurements.

PURPOSE: We are developing a calibration methodology for full-field digital mammography (FFDM). Calibration compensates for image acquisition technique influences on the pixel representation, ideally producing improved inter-image breast density estimates. This approach relies on establishing references with rigid breast tissue-equivalent phantoms (BTEs) and requires an accurate estimate of the compressed breast thickness because the system readout is nominal. There is also an attenuation mismatch between adipose breast tissue and the adipose BTE that was noted in our previous work. It is referred to as the "attenuation anomaly" and addressed in this report. The objectives are to evaluate methods to correct for the compressed breast thickness and compensate for the attenuation anomaly. METHODS: Thickness correction surfaces were established with a deformable phantom (DP) using both image and physical measurements for three direct x-ray conversion FFDM units. The Cumulative Sum serial quality control procedure was established to ensure the thickness correction measurements were stable over time by imaging and calibrating DPs biweekly in lieu of physical measurements. The attenuation anomaly was addressed by evaluating adipose image regions coupled with an optimization technique to adjust the adipose calibration data. We compared calibration consistency across matched left and right cranial caudal (CC) mammographic views (n = 199) with and without corrections using Bland-Altman plots. These plots were complemented by comparing the right and left breast calibrated average (µa ) and population distribution mean (ma ) with 95% confidence intervals and difference distribution variances with the F-test for uncorrected and corrected data. RESULTS: Thickness correction surfaces were well approximated as tilted planes and were dependent upon compression force. A correction was developed for the attenuation anomaly. All paddles (large and small paddles for all units) exhibited similar tilt as a function of force. Without correction, ma  = 0.92 (-1.77, 3.62) was not significantly different from zero with many negative µa samples. The thickness correction produced a significant shift in the µa distribution in the positive direction with ma  = 13.99 (11.17, 16.80) and reduced the difference distribution variance significantly (P < 0.0001). Applying both corrections in tandem gave ma  = 22.83 (20.32, 25.34), representing another significant positive shift in comparison with the thickness correction in isolation. Thickness corrections were stable over approximately a 2-year timeframe for all units. CONCLUSION: These correction techniques are valid approaches for addressing technical problems with calibration that relies on reference phantoms. The efficacy of the calibration methodology will require validation with clinical endpoints in future studies.

Calibrated Breast Density Measurements.

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.

Spatial Correlation and Breast Cancer Risk.

We present a novel method for evaluating local spatial correlation structure in two-dimensional (2D) mammograms and evaluate its capability for risk prediction as one possible application. Two matched case-control studies were analyzed. Study 1 included women (N = 588 pairs) with mammograms acquired with either Hologic Selenia full field digital mammography (FFDM) units or Hologic Dimensions digital breast tomosynthesis units. Study 2 included women (N =180 pairs) with mammograms acquired with a General Electric Senographe 2000D FFDM unit. Matching variables included age, HRT usage/duration, screening history, and mammography unit. Local autocorrelation functions were determined with Fourier analysis and compared with a template defined as a 2D double-sided exponential function with one spatial extent parameter: n = 4, 12, 24, 50, 74, 100, and 124, where (n+1)×(n+1) is the area of the local spatial extent measured in pixels. The difference between the local correlation and template was gauged within an adjustable parameter kernel and summarized, producing two measures: the mean (mn+1), and standard deviation (sn+1). Both adjustable parameters were varied in Study 1. Select measures that produced significant associations with breast cancer were translated to Study 2. Breast cancer associations were evaluated with conditional logistic regression, adjusted for body mass index and ethnicity. Odds ratios (ORs) were estimated as per standard deviation increment with 95% confidence intervals (CIs). Two measures were selected for breast cancer association analysis in Study 1: m75 and s25. Both measures revealed significant associations with breast cancer: OR = 1.45 (1.23, 1.66) for m75 and OR = 1.30 (1.14, 1.49) for s25. When translating to Study 2, these measures also revealed significant associations: OR = 1.49 (1.12, 1.96) for m75 and OR = 1.34 (1.06, 1.69) for s25. Novel correlation metrics presented in this work produced significant associations with breast cancer risk. This approach is general and may have applications beyond mammography.

Generalized breast density metrics.

Mammograms represent data that can inform future risk of breast cancer. Data from two case-control study populations were analyzed. Population 1 included women (N = 180 age matched case-control pairs) with mammograms acquired with one indirect x-ray conversion mammography unit. Population 2 included women (N = 319 age matched case-control pairs) with mammograms acquired from 6 direct x-ray conversion units. The Fourier domain was decomposed into n concentric rings (radial spatial frequency bands). The power in each ring was summarized giving a set of measures. We investigated images in raw, for presentation (processed) and calibrated representations and made comparison with the percentage of breast density (BD) determined with the operator assisted Cumulus method. Breast cancer associations were evaluated with conditional logistic regression, adjusted for body mass index and ethnicity. Odds ratios (ORs), per standard deviation increase derived from the respective breast density distributions and 95% confidence intervals (CIs) were estimated. A measure from a lower radial frequency ring, corresponding 0.083-0.166 cycles mm-1 and BD had significant associations with risk in both populations. In Population 1, the Fourier measure produced significant associations in each representation: OR = 1.76 (1.33, 2.32) for raw; OR = 1.43 (1.09, 1.87) for processed; and OR = 1.68 (1.26, 2.25) for calibrated. BD also provided significant associations in Population 1: OR = 1.72 (1.27, 2.33). In Population 2, the Fourier measure produced significant associations for each representation as well: OR = 1.47 (1.19, 1.80) for raw; OR = 1.38 (1.15, 1.67) for processed; and OR = 1.42 (1.15, 1.75) for calibrated. BD provided significant associations in Population 2: OR = 1.43 (1.17, 1.76). Other coincident spectral regions were also predictive of case-control status. In sum, generalized breast density measures were significantly associated with breast cancer in both FFDM technologies.

Sexual Assault and Depressive Symptoms in College Students: Do Psychological Needs Account for the Relationship?

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.

Automated Percentage of Breast Density Measurements for Full-field Digital Mammography Applications.

RATIONALE AND OBJECTIVES: Increased mammographic breast density is a significant risk factor for breast cancer. A reproducible, accurate, and automated breast density measurement is required for full-field digital mammography (FFDM) to support clinical applications. We evaluated a novel automated percentage of breast density measure (PDa) and made comparisons with the standard operator-assisted measure (PD) using FFDM data. METHODS: We used a nested breast cancer case-control study matched on age, year of mammogram and diagnosis with images acquired from a specific direct x-ray conversion FFDM technology. PDa was applied to the raw and clinical display (or processed) representation images. We evaluated the transformation (pixel mapping) of the raw image, giving a third representation (raw-transformed), to improve the PDa performance using differential evolution optimization. We applied PD to the raw and clinical display images as a standard for measurement comparison. Conditional logistic regression was used to estimate the odd ratios (ORs) for breast cancer with 95% confidence intervals (CI) for all measurements; analyses were adjusted for body mass index. PDa operates by evaluating signal-dependent noise (SDN), captured as local signal variation. Therefore, we characterized the SDN relationship to understand the PDa performance as a function of data representation and investigated a variation analysis of the transformation. RESULTS: The associations of the quartiles of operator-assisted PD with breast cancer were similar for the raw (OR: 1.00 [ref.]; 1.59 [95% CI, 0.93-2.70]; 1.70 [95% CI, 0.95-3.04]; 2.04 [95% CI, 1.13-3.67]) and clinical display (OR: 1.00 [ref.]; 1.31 [95% CI, 0.79-2.18]; 1.14 [95% CI, 0.65-1.98]; 1.95 [95% CI, 1.09-3.47]) images. PDa could not be assessed on the raw images without preprocessing. However, PDa had similar associations with breast cancer when assessed on 1) raw-transformed (OR: 1.00 [ref.]; 1.27 [95% CI, 0.74-2.19]; 1.86 [95% CI, 1.05-3.28]; 3.00 [95% CI, 1.67-5.38]) and 2) clinical display (OR: 1.00 [ref.]; 1.79 [95% CI, 1.04-3.11]; 1.61 [95% CI, 0.90-2.88]; 2.94 [95% CI, 1.66-5.19]) images. The SDN analysis showed that a nonlinear relationship between the mammographic signal and its variation (ie, the biomarker for the breast density) is required for PDa. Although variability in the transform influenced the respective PDa distribution, it did not affect the measurement's association with breast cancer. CONCLUSIONS: PDa assessed on either raw-transformed or clinical display images is a valid automated breast density measurement for a specific FFDM technology and compares well against PD. Further work is required for measurement generalization.

Survival analysis of patients with stage I non-small-cell lung cancer using clinical and DNA repair pathway expression variables.

BACKGROUND: Lung cancer is the leading cause of cancer-related mortality. Understanding patient attributes that enhance survival and predict recurrence is necessary to individualize treatment options. METHODS: Patients (N = 162) were dichotomized into favorable (n = 101) and unfavorable (n = 61) groups based on survival characteristics. Ku86 and poly(ADP-ribose) polymerase (PARP) expression measures were incorporated into the analyses. LR, Kaplan-Meier analysis, and Cox regression were used to investigate intervariable relationships and survival. Odds ratios (ORs) and hazard ratios (HRs) with 95% confidence intervals (CIs) were used to assess associations. RESULTS: Sex (OR, 0.32; CI-0.14, 0.76), squamous cell carcinoma (SCC) (OR, 0.41; CI-0.17, 0.98), and recurrence (OR, 0.04; CI-0.01, 0.20) confer an unfavorable outcome with area under the receiver operating characteristic curve (Az) = 0.788. Patients with increased tumor grade (OR = 1.84; CI-1.06, 3.19) or increased Ku86 intensity (OR, 2.03; CI-1.08, 3.82) were more likely to be male individuals, and older patients (OR, 1.70; CI-(1.14, 2.52) were more likely to have SCC. Patients older than the median age (HR, 1.86; CI-1.11, 3.12), patients with SCC (HR, 1.78; CI-1.05, 3.01), patients with recurrence (HR, 4.16; CI-2.37, 7.31), and male patients (HR, 2.03; CI-1.20, 3.43) have a higher hazard. None of the DNA repair measures were associated with significant HRs. CONCLUSION: Clinical and pathologic factors that enhance and limit survival for patients with stage I NSCLC were quantified. The DNA repair measures showed little association. These findings are important given that certain clinical and pathologic features are related to better long-term survival outcome than others.

Full field digital mammography and breast density: comparison of calibrated and noncalibrated measurements.

RATIONALE AND OBJECTIVES: Mammographic breast density is an important and widely accepted risk factor for breast cancer. A statement about breast density in the mammographic report is becoming a requirement in many States. However, there is significant inter-observer variation between radiologists in their interpretation of breast density. A properly designed automated system could provide benefits in maintaining consistency and reproducibility. We have developed a new automated and calibrated measure of breast density using full field digital mammography (FFDM). This new measure assesses spatial variation within a mammogram and produced significant associations with breast cancer in a small study. The costs of this automation are delays from advanced image and data analyses before the study can be processed. We evaluated this new calibrated variation measure using a larger dataset than previously. We also explored the possibility of developing an automated measure from unprocessed (raw data) mammograms as an approximation for this calibrated breast density measure. MATERIALS AND METHODS: A case-control study comprised of 160 cases and 160 controls matched by age, screening history, and hormone replacement therapy was used to compare the calibrated variation measure of breast density with three variants of a noncalibrated measure of spatial variation. The operator-assisted percentage of breast density measure (PD) was used as a standard reference for comparison. Odds ratio (OR) quartile analysis was used to compare these measures. Linear regression analysis was applied to assess the calibration's impact on the raw pixel distribution. RESULTS: All breast density measures showed significant breast cancer associations. The calibrated spatial variation measure produced the strongest associations (OR: 1.0 [ref.], 4.6, 4.3, 7.4). The associations for PD were diminished in comparison (OR: 1.0 [ref.], 2.7, 2.9, 5.2). Two additional non-calibrated measures restricted in region size also showed significant associations (OR: 1.0 [ref.], 2.9, 4.4, 5.4), and (OR: 1.0 [ref.], 3.5, 3.1, 4.9). Regression analyses indicated the raw image mean is influenced by the calibration more so than its standard deviation. CONCLUSION: Breast density measures can be automated. The associated calibration produced risk information not retrievable from the raw data representation. Although the calibrated measure produced the stronger association, the non-calibrated measures may offer an alternative to PD and other operator based methods after further evaluation, because they can be implemented automatically with a simple processing algorithm.