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BackgroundMetacognition is a cognitive process that involves self-awareness of thinking, understanding, and performance. This study assesses pathologists' metacognition by examining the association between their diagnostic accuracy and self-reported confidence levels while interpreting skin and breast biopsies.DesignWe studied 187 pathologists from the Melanoma Pathology Study (M-Path) and 115 pathologists from the Breast Pathology Study (B-Path). We measured pathologists' metacognitive ability by examining the area under the curve (AUC), the area under each pathologist's receiver operating characteristic (ROC) curve summarizing the association between confidence and diagnostic accuracy. We investigated possible relationships between this AUC measure, referred to as metacognitive sensitivity, and pathologist attributes. We also assessed whether higher metacognitive sensitivity affected the association between diagnostic accuracy and a secondary diagnostic action such as requesting a second opinion.ResultsWe found no significant associations between pathologist clinical attributes and metacognitive AUC. However, we found that pathologists with higher AUC showed a stronger trend to request secondary diagnostic action for inaccurate diagnoses and not for accurate diagnoses compared with pathologists with lower AUC.LimitationsPathologists reported confidence in specific diagnostic terms, rather than the broader classes into which the diagnostic terms were later grouped to determine accuracy. In addition, while there is no gold standard for the correct diagnosis to determine the accuracy of pathologists' interpretations, our studies achieved a high-quality reference diagnosis by using the consensus diagnosis of 3 experienced pathologists.ConclusionsMetacognition can affect clinical decisions. If pathologists have self-awareness that their diagnosis may be inaccurate, they can request additional tests or second opinions, providing the opportunity to correct inaccurate diagnoses.HighlightsMetacognitive sensitivity varied across pathologists, with most showing higher sensitivity than expected by chance.None of the demographic or clinical characteristics we examined was significantly associated with metacognitive sensitivity.Pathologists with higher metacognitive sensitivity were more likely to request additional tests or second opinions for their inaccurate diagnoses.

Background The COVID-19 pandemic reduced mammography use, potentially delaying breast cancer diagnoses. Purpose To examine breast biopsy recommendations and breast cancers diagnosed before and during the COVID-19 pandemic by mode of detection (screen detected vs symptomatic) and women's characteristics. Materials and Methods In this secondary analysis of prospectively collected data, monthly breast biopsy recommendations after mammography, US, or both with subsequent biopsy performed were examined from 66 facilities of the Breast Cancer Surveillance Consortium between January 2019 and September 2020. The number of monthly and cumulative biopsies recommended and performed and the number of subsequent cancers diagnosed during the pandemic period (March 2020 to September 2020) were compared with data from the prepandemic period using Wald χ2 tests. Analyses were stratified by mode of detection and race or ethnicity. Results From January 2019 to September 2020, 17 728 biopsies were recommended and performed, with 6009 cancers diagnosed. From March to September 2020, there were substantially fewer breast biopsy recommendations with cancer diagnoses when compared with the same period in 2019 (1650 recommendations in 2020 vs 2171 recommendations in 2019 [24% fewer], P < .001), predominantly due to fewer screen-detected cancers (722 cancers in 2020 vs 1169 cancers in 2019 [38% fewer], P < .001) versus symptomatic cancers (895 cancers in 2020 vs 965 cancers in 2019 [7% fewer], P = .27). The decrease in cancer diagnoses was largest in Asian (67 diagnoses in 2020 vs 142 diagnoses in 2019 [53% fewer], P = .06) and Hispanic (82 diagnoses in 2020 vs 145 diagnoses in 2019 [43% fewer], P = .13) women, followed by Black women (210 diagnoses in 2020 vs 287 diagnoses in 2019 [27% fewer], P = .21). The decrease was smallest in non-Hispanic White women (1128 diagnoses in 2020 vs 1357 diagnoses in 2019 [17% fewer], P = .09). Conclusion There were substantially fewer breast biopsies with cancer diagnoses during the COVID-19 pandemic from March to September 2020 compared with the same period in 2019, with Asian and Hispanic women experiencing the largest declines, followed by Black women. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Heller in this issue.

PurposePreoperative breast MRI is used to evaluate for additional cancer and extent of disease for newly diagnosed breast cancer, yet benefits and harms of preoperative MRI are not well-documented. We examined whether preoperative MRI yields additional biopsy and cancer detection by extent of breast density.MethodsWe followed women in the Breast Cancer Surveillance Consortium with an incident breast cancer diagnosed from 2005 to 2017. We quantified breast biopsies and cancers detected within 6 months of diagnosis by preoperative breast MRI receipt, overall and by breast density, accounting for MRI selection bias using inverse probability weighted logistic regression.ResultsAmong 19,324 women with newly diagnosed breast cancer, 28% had preoperative MRI, 11% additional biopsy, and 5% additional cancer detected. Four times as many women with preoperative MRI underwent additional biopsy compared to women without MRI (22.6% v. 5.1%). Additional biopsy rates with preoperative MRI increased with increasing breast density (27.4% for extremely dense compared to 16.2% for almost entirely fatty breasts). Rates of additional cancer detection were almost four times higher for women with v. without MRI (9.9% v. 2.6%). Conditional on additional biopsy, age-adjusted rates of additional cancer detection were lowest among women with extremely dense breasts, regardless of imaging modality (with MRI: 35.0%; 95% CI 27.0-43.0%; without MRI: 45.1%; 95% CI 32.6-57.5%).ConclusionFor women with dense breasts, preoperative MRI was associated with much higher biopsy rates, without concomitant higher cancer detection. Preoperative MRI may be considered for some women, but selecting women based on breast density is not supported by evidence.Trial registrationClinicalTrials.gov: NCT02980848; registered 2017.

Microenvironmental and molecular factors mediating the progression of Breast Ductal Carcinoma In Situ (DCIS) are not well understood, impeding the development of prevention strategies and the safe testing of treatment de-escalation. We addressed methodological barriers and characterized the mutational, transcriptional, histological, and microenvironmental landscape across 85 multiple microdissected regions from 39 cases. Most somatic alterations, including whole-genome duplications, were clonal, but genetic divergence increased with physical distance. Phenotypic and subtype heterogeneity was frequently associated with underlying genetic heterogeneity and regions with low-risk features preceded those with high-risk features according to the inferred phylogeny. B- and T-lymphocytes spatial analysis identified three immune states, including an epithelial excluded state located preferentially at DCIS regions, and characterized by histological and molecular features of immune escape, independently from molecular subtypes. Such breast pre-cancer atlas with uniquely integrated observations will help scope future expansion studies and build finer models of outcomes and progression risk.

Background The COVID-19 pandemic reduced mammography utilization, potentially delaying breast cancer diagnoses. Purpose To examine breast biopsy recommendations and breast cancers diagnosed before and during the COVID-19 pandemic by mode of detection (screen-detected versus symptomatic) and women's characteristics. Materials and Methods In this secondary analysis of prospectively collected data, monthly breast biopsy recommendations following mammography and/or ultrasound with subsequent biopsy performed were examined from 66 facilities of the Breast Cancer Surveillance Consortium between January 2019 and September 2020. Monthly and cumulative biopsies recommended and performed, and subsequent cancers diagnosed in the pandemic period (March-September 2020) versus the pre-pandemic period were compared using Wald chi-squared tests. Analyses were stratified by mode of detection and race/ethnicity. Results From January 2019 to September 2020, 17,728 biopsies were recommended and performed with 6,009 subsequent cancers diagnosed. From March to September 2020, there were substantially fewer breast biopsy recommendations with cancer diagnoses compared to 2019 (1,650 in 2020 versus 2,171 in 2019, or 24% fewer in 2020; p

BackgroundThe coronavirus disease 2019 (COVID-19) pandemic led to a near-total cessation of mammography services in the United States in mid-March 2020. It is unclear if screening and diagnostic mammography volumes have recovered to prepandemic levels and whether use has varied by women's characteristics.MethodsWe collected data on 461 083 screening mammograms and 112 207 diagnostic mammograms conducted during January 2019 through July 2020 at 62 radiology facilities in the Breast Cancer Surveillance Consortium. We compared monthly screening and diagnostic mammography volumes before and during the pandemic stratified by age, race and ethnicity, breast density, and family history of breast cancer.ResultsScreening and diagnostic mammography volumes in April 2020 were 1.1% (95% confidence interval [CI] = 0.5% to 2.4%) and 21.4% (95% CI = 18.7% to 24.4%) of the April 2019 prepandemic volumes, respectively, but by July 2020 had rebounded to 89.7% (95% CI = 79.6% to 101.1%) and 101.6% (95% CI = 93.8% to 110.1%) of the July 2019 prepandemic volumes, respectively. The year-to-date cumulative volume of screening and diagnostic mammograms performed through July 2020 was 66.2% (95% CI = 60.3% to 72.6%) and 79.9% (95% CI = 75.4% to 84.6%), respectively, of year-to-date volume through July 2019. Screening mammography rebound was similar across age groups and by family history of breast cancer. Monthly screening mammography volume in July 2020 for Black, White, Hispanic, and Asian women reached 96.7% (95% CI = 88.1% to 106.1%), 92.9% (95% CI = 82.9% to 104.0%), 72.7% (95% CI = 56.5% to 93.6%), and 51.3% (95% CI = 39.7% to 66.2%) of the July 2019 prepandemic volume, respectively.ConclusionsDespite a strong overall rebound in mammography volume by July 2020, the rebound lagged among Asian and Hispanic women, and a substantial cumulative deficit in missed mammograms accumulated, which may have important health consequences.

ObjectiveModeling variable-sized regions of interest (ROIs) in whole slide images using deep convolutional networks is a challenging task, as these networks typically require fixed-sized inputs that should contain sufficient structural and contextual information for classification. We propose a deep feature extraction framework that builds an ROI-level feature representation via weighted aggregation of the representations of variable numbers of fixed-sized patches sampled from nuclei-dense regions in breast histopathology images.MethodsFirst, the initial patch-level feature representations are extracted from both fully-connected layer activations and pixel-level convolutional layer activations of a deep network, and the weights are obtained from the class predictions of the same network trained on patch samples. Then, the final patch-level feature representations are computed by concatenation of weighted instances of the extracted feature activations. Finally, the ROI-level representation is obtained by fusion of the patch-level representations by average pooling.ResultsExperiments using a well-characterized data set of 240 slides containing 437 ROIs marked by experienced pathologists with variable sizes and shapes result in an accuracy score of 72.65% in classifying ROIs into four diagnostic categories that cover the whole histologic spectrum.ConclusionThe results show that the proposed feature representations are superior to existing approaches and provide accuracies that are higher than the average accuracy of another set of pathologists.SignificanceThe proposed generic representation that can be extracted from any type of deep convolutional architecture combines the patch appearance information captured by the network activations and the diagnostic relevance predicted by the class-specific scoring of patches for effective modeling of variable-sized ROIs.

* Context.--The International Collaboration on Cancer Reporting (ICCR), supported by major pathology and cancer organizations, aims at the standardization of evidence-based pathology reporting of different types of cancers, with the inclusion of all parameters deemed to be relevant for best patient care and future data collection. Lymph node metastasis is one of the most important prognostic factors in breast cancer. Objective.--To produce a histopathology reporting guide by a panel of recognized experts from the fields of pathology and surgery with elements deemed to be core (required) and noncore (recommended) to report when assessing regional lymph nodes of patients with breast cancer. Data Sources.--Published literature, previous guidelines/recommendations, and current cancer staging principles were the basis of the data set drafted by the expert panel. This was discussed in a series of teleconferences and email communications. The draft data set was then made available for public consultation through the ICCR Web site. After this consultation and ICCR ratification, the data set was finalized. Conclusions.--The ICCR has published a data set for the reporting of surgically removed lymph nodes (including sentinel lymph node biopsy, axillary lymph node dissection, targeted axillary surgery, and lymph node sampling specimens) for breast tumors. This is part of a series of 4 ICCR breast cancer-related data sets. It includes 10 core elements along with 2 noncore elements. This should allow for synoptic reporting, which is more precise, uniform, and complete than nonsynoptic reporting, and leads to improved patient outcomes. doi: 10.5858/arpa.2022-0060-RA, The International Collaboration on Cancer Reporting (ICCR) stems from an unprecedented effort to provide standards for evidence-based reporting of the pathologic diagnosis of different types of cancers, including all parameters [...]

PurposeMachine Learning Package for Cancer Diagnosis (MLCD) is the result of a National Institutes of Health/National Cancer Institute (NIH/NCI)-sponsored project for developing a unified software package from state-of-the-art breast cancer biopsy diagnosis and machine learning algorithms that can improve the quality of both clinical practice and ongoing research.MethodsWhole-slide images of 240 well-characterized breast biopsy cases, initially assembled under R01 CA140560, were used for developing the algorithms and training the machine learning models. This software package is based on the methodology developed and published under our recent NIH/NCI-sponsored research grant (R01 CA172343) for finding regions of interest (ROIs) in whole-slide breast biopsy images, for segmenting ROIs into histopathologic tissue types and for using this segmentation in classifiers that can suggest final diagnoses.ResultThe package provides an ROI detector for whole-slide images and modules for semantic segmentation into tissue classes and diagnostic classification into 4 classes (benign, atypia, ductal carcinoma in situ, invasive cancer) of the ROIs. It is available through the GitHub repository under the Massachusetts Institute of Technology license and will later be distributed with the Pathology Image Informatics Platform system. A Web page provides instructions for use.ConclusionOur tools have the potential to provide help to other cancer researchers and, ultimately, to practicing physicians and will motivate future research in this field. This article describes the methodology behind the software development and gives sample outputs to guide those interested in using this package.

Digital whole slide images are Food and Drug Administration approved for clinical diagnostic use in pathology; however, integration is nascent. Trainees from 9 pathology training programs completed an online survey to ascertain attitudes toward and experiences with whole slide images for pathological interpretations. Respondents (n = 76) reported attending 63 unique medical schools (45 United States, 18 international). While 63% reported medical school exposure to whole slide images, most reported ≤ 5 hours. Those who began training more recently were more likely to report at least some exposure to digital whole slide image training in medical school compared to those who began training earlier: 75% of respondents beginning training in 2017 or 2018 reported exposure to whole slide images compared to 54% for trainees beginning earlier. Trainees exposed to whole slide images in medical school were more likely to agree they were comfortable using whole slide images for interpretation compared to those not exposed (29% vs 12%; P = .06). Most trainees agreed that accurate diagnoses can be made using whole slide images for primary diagnosis (92%; 95% CI: 86-98) and that whole slide images are useful for obtaining second opinions (93%; 95% CI: 88-99). Trainees reporting whole slide image experience during training, compared to those with no experience, were more likely to agree they would use whole slide images in 5 years for primary diagnosis (64% vs 50%; P = .3) and second opinions (86% vs 76%; P = .4). In conclusion, although exposure to whole slide images in medical school has increased, overall exposure is limited. Positive attitudes toward future whole slide image diagnostic use were associated with exposure to this technology during medical training. Curricular integration may promote adoption.

ImportanceFollowing recent US Food and Drug Administration approval, adoption of whole slide imaging in clinical settings may be imminent, and diagnostic accuracy, particularly among challenging breast biopsy specimens, may benefit from computerized diagnostic support tools.ObjectiveTo develop and evaluate computer vision methods to assist pathologists in diagnosing the full spectrum of breast biopsy samples, from benign to invasive cancer.Design, setting, and participantsIn this diagnostic study, 240 breast biopsies from Breast Cancer Surveillance Consortium registries that varied by breast density, diagnosis, patient age, and biopsy type were selected, reviewed, and categorized by 3 expert pathologists as benign, atypia, ductal carcinoma in situ (DCIS), and invasive cancer. The atypia and DCIS cases were oversampled to increase statistical power. High-resolution digital slide images were obtained, and 2 automated image features (tissue distribution feature and structure feature) were developed and evaluated according to the consensus diagnosis of the expert panel. The performance of the automated image analysis methods was compared with independent interpretations from 87 practicing US pathologists. Data analysis was performed between February 2017 and February 2019.Main outcomes and measuresDiagnostic accuracy defined by consensus reference standard of 3 experienced breast pathologists.ResultsThe accuracy of machine learning tissue distribution features, structure features, and pathologists for classification of invasive cancer vs noninvasive cancer was 0.94, 0.91, and 0.98, respectively; the accuracy of classification of atypia and DCIS vs benign tissue was 0.70, 0.70, and 0.81, respectively; and the accuracy of classification of DCIS vs atypia was 0.83, 0.85, and 0.80, respectively. The sensitivity of both machine learning features was lower than that of the pathologists for the invasive vs noninvasive classification (tissue distribution feature, 0.70; structure feature, 0.49; pathologists, 0.84) but higher for the classification of atypia and DCIS vs benign cases (tissue distribution feature, 0.79; structure feature, 0.85; pathologists, 0.72) and the classification of DCIS vs atypia (tissue distribution feature, 0.88; structure feature, 0.89; pathologists, 0.70). For the DCIS vs atypia classification, the specificity of the machine learning feature classification was similar to that of the pathologists (tissue distribution feature, 0.78; structure feature, 0.80; pathologists, 0.82).Conclusion and relevanceThe computer-based automated approach to interpreting breast pathology showed promise, especially as a diagnostic aid in differentiating DCIS from atypical hyperplasia.

BackgroundCurrent data on the pathologic diagnoses of breast biopsy after mammography can inform patients, clinicians, and researchers about important population trends.MethodsBreast Cancer Surveillance Consortium data on 4,020,140 mammograms between 1996 and 2008 were linked to 76,567 pathology specimens. Trends in diagnoses in biopsies by time and risk factors (patient age, breast density, and family history of breast cancer) were examined for screening and diagnostic mammography (performed for a breast symptom or short-interval follow-up).ResultsOf the total mammograms, 88.5% were screening and 11.5% diagnostic; 1.2% of screening and 6.8% of diagnostic mammograms were followed by biopsies. The frequency of biopsies over time was stable after screening mammograms, but increased after diagnostic mammograms. For biopsies obtained after screening, frequencies of invasive carcinoma increased over time for women ages 40-49 and 60-69, Ductal carcinoma in situ (DCIS) increased for those ages 40-69, whereas benign diagnoses decreased for all ages. No trends in pathology diagnoses were found following diagnostic mammograms. Dense breast tissue was associated with high-risk lesions and DCIS relative to nondense breast tissue. Family history of breast cancer was associated with DCIS and invasive cancer.ConclusionsAlthough the frequency of breast biopsy after screening mammography has not changed over time, the percentages of biopsies with DCIS and invasive cancer diagnoses have increased. Among biopsies following mammography, women with dense breasts or family history of breast cancer were more likely to have high-risk lesions or invasive cancer. These findings are relevant to breast cancer screening and diagnostic practices.

Physician attributes, job satisfaction and confidence in clinical skills are associated with enhanced performance and better patient outcomes. We surveyed 252 pathologists to evaluate associations between enjoyment of breast pathology, demographic/clinical characteristics and diagnostic performance. Diagnostic performance was determined by comparing pathologist assessments of a set of 60 cases with consensus assessments of the same cases made by a panel of experienced pathologists. Eighty-three percent of study participants reported enjoying breast pathology. Pathologists who enjoy breast interpretation were more likely to review ≥10 cases/week (p = 0.003), report breast interpretation expertise (p = 0.013) and have high levels of confidence interpreting breast pathology (p < 0.001). These pathologists were less likely to report that the field was challenging (p < 0.001) and that breast cases make them more nervous than other types of pathology (p < 0.001). Enjoyment was not associated with diagnostic performance. Millions of women undergo breast biopsy annually, thus it is reassuring that although nearly a fifth of practicing pathologists who interpret breast tissue report not enjoying the field, precision is not impacted.

Screening mammography utilization in Vermont has declined since 2009 during a time of changing screening guidelines and increased interest in personalized screening regimens. This study evaluates whether the breast cancer risk distribution of the state's screened population changed during the observed decline. We examined the breast cancer risk distribution among screened women between 2001 and 2012 using data from the Vermont Breast Cancer Surveillance System. We estimated each screened woman's 5-year risk of breast cancer using the Breast Cancer Surveillance Consortium risk calculator. Annual screening counts by risk group were normalized and age-adjusted to the Vermont female population by direct standardization. The normalized rate of low-risk (5-year breast cancer risk of

Objective This study explores the feasibility of using machine learning to predict accurate versus inaccurate diagnoses made by pathologists based on their spatiotemporal viewing behavior when evaluating digital breast biopsy images. Materials and Methods The study gathered data from 140 pathologists of varying experience levels who each reviewed a set of 14 digital whole slide images of breast biopsy tissue. Pathologists' viewing behavior, including zooming and panning actions, was recorded during image evaluation. A total of 30 features were extracted from the viewing behavior data, and 4 machine learning algorithms were used to build classifiers for predicting diagnostic accuracy. Results The Random Forest classifier demonstrated the best overall performance, achieving a test accuracy of 0.81 and area under the receiver-operator characteristic curve of 0.86. Features related to attention distribution and focus on critical regions of interest were found to be important predictors of diagnostic accuracy. Further including case-level and pathologist-level information incrementally improved classifier performance. Discussion Results suggest that pathologists' viewing behavior during digital image evaluation can be leveraged to predict diagnostic accuracy, affording automated feedback and decision support systems based on viewing behavior to aid in training and, ultimately, clinical practice. They also carry implications for basic research examining the interplay between perception, thought, and action in diagnostic decision-making. Conclusion The classifiers developed herein have potential applications in training and clinical settings to provide timely feedback and support to pathologists during diagnostic decision-making. Further research could explore the generalizability of these findings to other medical domains and varied levels of expertise. [ABSTRACT FROM AUTHOR]

BackgroundControversy remains regarding the frequency of screening mammography. Women with different risks for developing breast cancer because of body mass index (BMI) may benefit from tailored recommendations.ObjectiveTo determine the impact of mammography screening interval for women who are normal weight (BMI < 25), overweight (BMI 25-29.9), or obese (BMI ≥ 30), stratified by menopausal status.DesignTwo cohorts selected from the Breast Cancer Surveillance Consortium. Patient and mammography data were linked to pathology databases and tumor registries.ParticipantsThe cohort included 4,432 women aged 40-74 with breast cancer; the false-positive analysis included a cohort of 553,343 women aged 40-74 without breast cancer.Main measuresStage, tumor size and lymph node status by BMI and screening interval (biennial vs. annual). Cumulative probability of false-positive recall or biopsy by BMI and screening interval. Analyses were stratified by menopausal status.Key resultsPremenopausal obese women undergoing biennial screening had a non-significantly increased odds of a tumor size > 20 mm relative to annual screeners (odds ratio [OR] = 2.07; 95 % confidence interval [CI] 0.997 to 4.30). Across all BMI categories from normal to obese, postmenopausal women with breast cancer did not present with higher stage, larger tumor size or node positive tumors if they received biennial rather than annual screening. False-positive recall and biopsy recommendations were more common among annually screened women.ConclusionThe only negative outcome identified for biennial vs. annual screening was a larger tumor size (> 20 mm) among obese premenopausal women. Since annual mammography does not improve stage at diagnosis compared to biennial screening and false-positive recall/biopsy rates are higher with annual screening, women and their primary care providers should weigh the harms and benefits when deciding on annual versus biennial screening.

BackgroundBenign breast disease and high breast density are prevalent, strong risk factors for breast cancer. Women with both risk factors may be at very high risk.MethodsWe included 42818 women participating in the Breast Cancer Surveillance Consortium who had no prior diagnosis of breast cancer and had undergone at least one benign breast biopsy and mammogram; 1359 women developed incident breast cancer in 6.1 years of follow-up (78.1% invasive, 21.9% ductal carcinoma in situ). We calculated hazard ratios (HRs) using Cox regression analysis. The referent group was women with nonproliferative changes and average density. All P values are two-sided.ResultsBenign breast disease and breast density were independently associated with breast cancer. The combination of atypical hyperplasia and very high density was uncommon (0.6% of biopsies) but was associated with the highest risk for breast cancer (HR = 5.34; 95% confidence interval [CI] = 3.52 to 8.09, P < .001). Proliferative disease without atypia (25.6% of biopsies) was associated with elevated risk that varied little across levels of density: average (HR = 1.37; 95% CI = 1.11 to 1.69, P = .003), high (HR = 2.02; 95% CI = 1.68 to 2.44, P < .001), or very high (HR = 2.05; 95% CI = 1.54 to 2.72, P < .001). Low breast density (4.5% of biopsies) was associated with low risk (HRs

Community health centers are uniquely positioned to address disparities in colorectal cancer (CRC) screening as they have addressed other disparities. In 2012, the federal Health Resources and Services Administration, which is the funding agency for the health center program, added a requirement that health centers report CRC screening rates as a standard performance measure. These annually reported, publically available data are a major strategic opportunity to improve screening rates for CRC. The Patient Protection and Affordable Care Act enacted provisions to expand the capacity of the federal health center program. The recent report of the Institute of Medicine on integrating public health and primary care included an entire section devoted to CRC screening as a target for joint work. These developments make this the ideal time to integrate lifesaving CRC screening into the preventive care already offered by health centers. This article offers 5 strategies that address the challenges health centers face in increasing CRC screening rates. The first 2 strategies focus on improving the processes of primary care. The third emphasizes working productively with other medical providers and institutions. The fourth strategy is about aligning leadership. The final strategy is focused on using tools that have been derived from models that work.

BackgroundWomen with elevated mammographic density have an increased risk of developing breast cancer. However, among women diagnosed with breast cancer, it is unclear whether higher density portends reduced survival, independent of other factors.MethodsWe evaluated relationships between mammographic density and risk of death from breast cancer and all causes within the US Breast Cancer Surveillance Consortium. We studied 9232 women diagnosed with primary invasive breast carcinoma during 1996-2005, with a mean follow-up of 6.6 years. Mammographic density was assessed using the Breast Imaging Reporting and Data System (BI-RADS) density classification. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated by Cox proportional hazards regression; women with scattered fibroglandular densities (BI-RADS 2) were the referent group. All statistical tests were two-sided.ResultsA total of 1795 women died, of whom 889 died of breast cancer. In multivariable analyses (adjusted for site, age at and year of diagnosis, American Joint Committee on Cancer stage, body mass index, mode of detection, treatment, and income), high density (BI-RADS 4) was not related to risk of death from breast cancer (HR = 0.92, 95% CI = 0.71 to 1.19) or death from all causes (HR = 0.83, 95% CI = 0.68 to 1.02). Analyses stratified by stage and other prognostic factors yielded similar results, except for an increased risk of breast cancer death among women with low density (BI-RADS 1) who were either obese (HR = 2.02, 95% CI = 1.37 to 2.97) or had tumors of at least 2.0 cm (HR = 1.55, 95% CI = 1.14 to 2.09).ConclusionsHigh mammographic breast density was not associated with risk of death from breast cancer or death from any cause after accounting for other patient and tumor characteristics. Thus, risk factors for the development of breast cancer may not necessarily be the same as factors influencing the risk of death after breast cancer has developed.