Karthik Ghosh

Mammographic density, breast cancer risk and risk prediction

In this review, we examine the evidence for mammographic density as an independent risk factor for breast cancer, describe the risk prediction models that have incorporated density, and discuss the current and future implications of using mammographic density in clinical practice. Mammographic density is a consistent and strong risk factor for breast cancer in several populations and across age at mammogram. Recently, this risk factor has been added to existing breast cancer risk prediction models, increasing the discriminatory accuracy with its inclusion, albeit slightly. With validation, these models may replace the existing Gail model for clinical risk assessment. However, absolute risk estimates resulting from these improved models are still limited in their ability to characterize an individuals probability of developing cancer. Promising new measures of mammographic density, including volumetric density, which can be standardized using full-field digital mammography, will likely result in a stronger risk factor and improve accuracy of risk prediction models.

Mammographic Breast Density as a General Marker of Breast Cancer Risk

Mammographic breast density is a strong risk factor for breast cancer but whether breast density is a general marker of susceptibility or is specific to the location of the eventual cancer is unknown. A study of 372 incident breast cancer cases and 713 matched controls was conducted within the Mayo Clinic mammography screening practice. Mammograms on average 7 years before breast cancer were digitized, and quantitative measures of percentage density and dense area from each side and view were estimated. A regional density estimate accounting for overall percentage density was calculated from both mammogram views. Location of breast cancer and potential confounders were abstracted from medical records. Conditional logistic regression was used to estimate associations, and C-statistics were used to evaluate the strength of risk prediction. There were increasing trends in breast cancer risk with increasing quartiles of percentage density and dense area, irrespective of the side of the breast with cancer (Ptrends < 0.001). Percentage density from the ipsilateral side [craniocaudal (CC): odds ratios (ORs), 1.0 (ref), 1.7, 3.1, and 3.1; mediolateral oblique (MLO): ORs, 1.0 (ref), 1.5, 2.2, and 2.8] and the contralateral side [CC: ORs, 1.0 (ref), 1.8, 2.2, and 3.7; MLO: ORs, 1.0 (ref), 1.6, 1.9, and 2.5] similarly predicted case-control status (C-statistics, 0.64-65). Accounting for overall percentage density, density in the region where the cancer subsequently developed was not a significant risk factor [CC: 1.0 (ref), 1.3, 1.0, and 1.2; MLO: 1.0 (ref), 1.1, 1.0, and 1.1 for increasing quartiles]. Results did not change when examining mammograms 3 years on average before the cancer. Overall mammographic density seems to represent a general marker of breast cancer risk that is not specific to breast side or location of the eventual cancer. (Cancer Epidemiol Biomarkers Prev 2007;16(1):43–9)

Atypical Hyperplasia of the Breast — Risk Assessment and Management Options

Some benign breast lesions have a greatly increased risk of becoming invasive cancers. Atypical hyperplasia is a common high-risk benign lesion, and measures to prevent its progression to cancer are available but underutilized.

Increased prevalence of antimitochondrial antibodies in first-degree relatives of patients with primary biliary cirrhosis†

Primary biliary cirrhosis (PBC) is a chronic cholestatic liver disorder that can progress to cirrhosis, shortening life expectancy. PBC patients are often asymptomatic, present with biochemical cholestasis, and test positive (≥90%) for antimitochondrial antibodies (AMAs) in serum. Although AMA positivity without biochemical cholestasis may indicate increased risk of future PBC development, the contribution of these antibodies to pathogenesis remains enigmatic. Environmental risks and genetic determinants are likely implicated in PBC etiology. Given the familial aggregation of PBC, we hypothesized that AMAs also aggregate among relatives of PBC probands. We investigated the prevalence of AMAs in first‐degree relatives (FDRs) of PBC probands to examine whether AMAs aggregate in such pedigrees. Using a PBC family registry, we prospectively screened for AMAs in the serum of 306 FDRs in 145 pedigrees, 350 PBC probands, and 196 controls who were age‐matched, sex‐matched, race‐matched, and residence‐matched to probands. The prevalence of AMA in FDRs and controls was 13.1% and 1%, respectively. Greater prevalence of AMA was found in female FDRs of PBC probands [sisters (20.7%), mothers (15.1%), and daughters (9.8%)] than in male FDRs [brothers (7.8%), fathers (3.7%), and sons (0%)]. Conclusions: AMAs aggregate among FDRs of PBC probands. Our data have clinical implications for FDRs of PBC probands because AMA positivity may suggest susceptibility to PBC. Thus, the identification and follow‐up of these relatives may lead to earlier disease diagnosis and treatment. Furthermore, if AMA development is heritable, this trait will provide a basis to dissect the genetic predisposition to PBC. (HEPATOLOGY 2007.)

Understanding the premalignant potential of atypical hyperplasia through its natural history: a longitudinal cohort study.

Atypical hyperplasia is a high-risk premalignant lesion of the breast, but its biology is poorly understood. Many believe that atypical ductal hyperplasia (ADH) is a direct precursor for low-grade ductal breast cancer, whereas atypical lobular hyperplasia (ALH) serves as a risk indicator. These assumptions underlie current clinical recommendations. We tested these assumptions by studying the characteristics of the breast cancers that develop in women with ADH or ALH. Using the Mayo Benign Breast Disease Cohort, we identified all women with ADH or ALH from 1967 to 2001 and followed them for later breast cancers, characterizing side of breast cancer versus side of atypia; time to breast cancer; type, histology, and grade of breast cancer, looking for patterns consistent with precursors versus risk indicators. A total of 698 women with atypical hyperplasia were followed a mean of 12.5 years; 143 developed breast cancer. For both ADH and ALH, there is a 2:1 ratio of ipsilateral to contralateral breast cancer. The ipsilateral predominance is marked in the first 5 years, consistent with a precursor phenotype for both ADH and ALH. For both, there is a predominance of invasive ductal cancers with 69% of moderate or high grade. Twenty-five percent are node positive. Both ADH and ALH portend risk for ductal carcinoma in situ and invasive breast cancers, predominantly ductal, with two thirds moderate or high grade. The ipsilateral breast is at especially high risk for breast cancer in the first 5 years after atypia, with risk remaining elevated in both breasts long term. ADH and ALH behave similarly in terms of later breast cancer endpoints. Cancer Prev Res; 7(2); 211–7. ©2014 AACR.

Longitudinal Trends in Mammographic Percent Density and Breast Cancer Risk

Background: Mammographic density is a strong risk factor for breast cancer. However, whether changes in mammographic density are associated with risk remains unclear. Materials and Methods: A study of 372 incident breast cancer cases and 713 matched controls was conducted within the Mayo Clinic mammography screening practice. Controls were matched on age, exam date, residence, menopause, interval between, and number of mammograms. All serial craniocaudal mammograms 10 years before ascertainment were digitized, and quantitative measures of percent density (PD) were estimated using a thresholding method. Data on potential confounders were abstracted from medical records. Logistic regression models with generalized estimating equations were used to evaluate the interactions among PD at earliest mammogram, time from earliest to each serial mammogram, and absolute change in PD between the earliest and subsequent mammograms. Analyses were done separately for PD measures from the ipsilateral and contralateral breast and also by use of hormone therapy (HT). Results: Subjects had an average of five mammograms available, were primarily postmenopausal (83%), and averaged 61 years at the earliest mammogram. Mean PD at earliest mammogram was higher for cases (31%) than controls (27%; ipsilateral side). There was no evidence of an association between change in PD and breast cancer risk by time. Compared with no change, an overall reduction of 10% PD (lowest quartile of change) was associated with an odds ratio of 0.9997 and an increase of 6.5% PD (highest quartile of change) with an odds ratio of 1.002. The same results held within the group of 220 cases and 340 controls never using HT. Among the 124 cases and 337 controls known to use HT during the interval, there was a statistically significant interaction between change in PD and time since the earliest mammogram (P = 0.01). However, in all groups, the risk associated with the earliest PD remained a stronger predictor of risk than change in PD. Conclusion: We observed no association between change in PD with breast cancer risk among all women and those never using HT. However, the interaction between change in PD and time should be evaluated in other populations. (Cancer Epidemiol Biomarkers Prev 2007;16(5):921–8)

Assessment of the Accuracy of the Gail Model in Women With Atypical Hyperplasia

PURPOSE An accurate estimate of a womans breast cancer risk is essential for optimal patient counseling and management. Women with biopsy-confirmed atypical hyperplasia of the breast (atypia) are at high risk for breast cancer. The Gail model is widely used in these women, but has not been validated in them. PATIENTS AND METHODS Women with atypia were identified from the Mayo Benign Breast Disease (BBD) cohort (1967 to 1991). Their risk factors for breast cancer were obtained, and the Gail model was used to predict 5-year-and follow-up-specific risks for each woman. The predicted and observed numbers of breast cancers were compared, and the concordance between individual risk levels and outcomes was computed. RESULTS Of the 9,376 women in the BBD cohort, 331 women had atypia (3.5%). At a mean follow-up of 13.7 years, 58 of 331 (17.5%) patients had developed invasive breast cancer, 1.66 times more than the 34.9 predicted by the Gail model (95% CI, 1.29 to 2.15; P < .001). For individual women, the concordance between predicted and observed outcomes was low, with a concordance statistic of 0.50 (95% CI, 0.44 to 0.55). CONCLUSION The Gail model significantly underestimates the risk of breast cancer in women with atypia. Its ability to discriminate women with atypia into those who did and did not develop breast cancer is limited. Health care professionals should be cautious when using the Gail model to counsel individual patients with atypia.

Association Between Mammographic Density and Age-Related Lobular Involution of the Breast

PURPOSE Mammographic density and lobular involution are both significant risk factors for breast cancer, but whether these reflect the same biology is unknown. We examined the involution and density association in a large benign breast disease (BBD) cohort. PATIENTS AND METHODS Women in the Mayo Clinic BBD cohort who had a mammogram within 6 months of BBD diagnosis were eligible. The proportion of normal lobules that were involuted was categorized by an expert pathologist as no (0%), partial (1% to 74%), or complete involution (>or= 75%). Mammographic density was estimated as the four-category parenchymal pattern. Statistical analyses adjusted for potential confounders and evaluated modification by parity and age. We corroborated findings in a sample of women with BBD from the Mayo Mammography Health Study (MMHS) with quantitative percent density (PD) and absolute dense and nondense area estimates. RESULTS Women in the Mayo BBD cohort (n = 2,667) with no (odds ratio, 1.7; 95% CI, 1.2 to 2.3) or partial (odds ratio, 1.3; 95% CI, 1.0 to 1.6) involution had greater odds of high density (DY pattern) than those with complete involution (P trend < .01). There was no evidence for effect modification by age or parity. Among 317 women with BBD in the MMHS study, there was an inverse association between involution and PD (mean PD, 22.4%, 21.6%, 17.2%, for no, partial, and complete, respectively; P trend = .04) and a strong positive association of involution with nondense area (P trend < .01). No association was seen between involution and dense area (P trend = .56). CONCLUSION We present evidence of an inverse association between involution and mammographic density.

Independent Association of Lobular Involution and Mammographic Breast Density With Breast Cancer Risk

Background Lobular involution, or age-related atrophy of breast lobules, is inversely associated with breast cancer risk, and mammographic breast density (MBD) is positively associated with breast cancer risk. Methods To evaluate whether lobular involution and MBD are independently associated with breast cancer risk in women with benign breast disease, we performed a nested cohort study among women (n = 2666) with benign breast disease diagnosed at Mayo Clinic between January 1, 1985, and December 31, 1991 and a mammogram available within 6 months of the diagnosis. Women were followed up for an average of 13.3 years to document any breast cancer incidence. Lobular involution was categorized as none, partial, or complete; parenchymal pattern was classified using the Wolfe classification as N1 (nondense), P1, P2 (ductal prominence occupying <25%, or >25% of the breast, respectively), or DY (extremely dense). Hazard ratios (HRs) and 95% confidence intervals (CIs) to assess associations of lobular involution and MBD with breast cancer risk were estimated using adjusted Cox proportional hazards model. All tests of statistical significance were two-sided. Results After adjustment for MBD, having no or partial lobular involution was associated with a higher risk of breast cancer than having complete involution (none: HR of breast cancer incidence = 2.62, 95% CI = 1.39 to 4.94; partial: HR of breast cancer incidence = 1.61, 95% CI = 1.03 to 2.53; Ptrend = .002). Similarly, after adjustment for involution, having dense breasts was associated with higher risk of breast cancer than having nondense breasts (for DY: HR of breast cancer incidence = 1.67, 95% CI = 1.03 to 2.73; for P2: HR of breast cancer incidence = 1.96, 95% CI = 1.20 to 3.21; for P1: HR of breast cancer incidence = 1.23, 95% CI = 0.67 to 2.26; Ptrend = .02). Having a combination of no involution and dense breasts was associated with higher risk of breast cancer than having complete involution and nondense breasts (HR of breast cancer incidence = 4.08, 95% CI = 1.72 to 9.68; P = .006). Conclusion Lobular involution and MBD are independently associated with breast cancer incidence; combined, they are associated with an even greater risk for breast cancer.

Breast Density and Breast Cancer Risk: A Practical Review

New legislation in several states requiring breast density notification in all mammogram reports has increased awareness of breast density. Estimates indicate that up to 50% of women undergoing mammography will have high breast density; thus, with increased attention and high prevalence of increased breast density, it is crucial that primary care clinicians understand the implications of dense breasts and are able to provide appropriate counseling. This review provides an overview of breast density, specifically by defining breast density, exploring the association between breast density and breast cancer risk, both from masking and as an independent risk factor, and reviewing supplemental screening options as part of a larger framework for counseling patients with dense breasts.