Morton, Marilyn J.

49 Background: Predicting nodal positivity can guide surgical planning in breast cancer. Memorial Sloan-Kettering Cancer Center (MSKCC) and MD Anderson Cancer Center (MDACC) have established nomograms to predict sentinel node positivity. We propose that addition of distance of tumor from the nipple (DFN) can improve their performance. Methods: With IRB approval clinical T1/T2 tumors with pre-biopsy ultrasound were reviewed. MSKCC and MDACC nomogram predictions and the AUC-ROC were calculated. The addition of DFN was examined using multiple logistic regression and comparison of AUC-ROC values. Results: 401 breast cancers with clinical T1 (85%) or T2 (15%) tumors in 395 patients were included; of which 79/401 (19.7%) were node positive. Tumors were significantly closer to the nipple in those with positive nodes. 17/33 (51.5%) tumors within 2 cm of the nipple were node-positive versus 62/368 (16.8%) tumors > 2 cm from the nipple (p<0.0001). The MSKCC and MDACC nomograms each demonstrated good discrimination between node positive and negative patients with AUC-ROC values of 0.71 (95% CI: 0.64-0.77) and 0.74 (95% CI: 0.68-0.81), respectively. When added to the MSKCC nomogram, DFN ≤2 cm contributed significantly (OR 4.78, p=0.0001) to prediction of node positivity and improved the AUC-ROC to 0.73 (95% CI: 0.67-0.80). Similarly, DFN ≤2 cm was significant (OR 4.73, p=0.0002) when added to the MDACC nomogram and improved the AUC to 0.76 (95% CI: 0.70-0.82). Within nomogram probability categories, the proportion with positive nodes was consistently higher with DFN ≤2 cm. Conclusions: Tumor distance from nipple is associated with nodal positivity. When added to nomograms it improves prediction of node positivity. DFN should be considered when considering likelihood of nodal positivity for treatment planning. [Table: see text]

Background: The use of breast magnetic resonance imaging (MRI) for screening high-risk patients is well established. However, the role of MRI as a diagnostic problem-solving tool is less well studied. With the increasing availability of MRI, its use for problem solving has increased. This small retrospective study examines the use and utility of breast MRI in evaluating palpable breast masses with negative diagnostic mammogram and ultrasound studies. Methods: We reviewed our breast MRI database, selecting breast MRI studies performed to assess palpable abnormalities with negative mammogram and ultrasound findings. Evidence of cancer was determined by biopsy. Results: Seventy-seven studies were included, comprising 1.3% of all breast MRI studies performed at our institution during the study period (2005–2011). Twenty-two patients underwent biopsy, and 55 were followed clinically without biopsy. Approximately half (27 of 55) of the patients without biopsy were lost to follow-up after negative MRI, and the rest had no evidence of cancer on imaging or clinical examination at 1 year. Of the 22 patients who underwent biopsy, 2 were diagnosed with cancer, both with positive MRI studies. Sensitivity of MRI when compared to tissue diagnosis was 100%, and specificity was 70%. Positive and negative predictive values were 25% and 100%, respectively. Conclusions: When used for evaluation of a palpable breast mass with negative traditional imaging, breast MRI likely offers low yield of cancer diagnosis and low specificity. Negative MRI results may cause a low compliance rate for recommended follow-up. Because a biopsy is indicated for persistent palpable masses, the addition of diagnostic MRI only adds another step, with associated costs and burdens.

Abstract INTRODUCTION: Mammographic breast density is a strong risk factor for breast cancer (BC). We established the Mayo Mammography Health Study (MMHS) cohort study at the Mayo Clinic in Rochester, Minnesota (MN) to examine the association of breast density with BC. We evaluated the influence of the image acquisition technique on the density and BC association. METHODS: From October 2003 to September 2006, all women scheduled for screening mammography at the Mayo Clinic were invited to participate. Eligible women were residents of MN, Iowa (IA) or Wisconsin (WI); age 35+; and had no personal history of BC. A risk factor questionnaire, consent form and permission to link to tumor registries were obtained. Incident BC was identified through 2009 by linkage to the Mayo and state cancer registries. A case-cohort of all incident BCs and 2300 randomly selected women (the subcohort) were used to examine the association of breast density and BC using digitized film mammograms at the time of enrollment while controlling for the influence of the acquisition parameters (peak kilovoltage, milliampere-second, and compressed breast thickness). Two density measures were considered: a quantitative percent density (PD) measure estimated using the computer-assisted thresholding program, Cumulus (University of Toronto), and the 4-category clinical BI-RADS measure. Proportional hazards regression was used to calculate hazards ratios (HR) and 95% confidence intervals (CI) for BC associated with quartiles of PD (0-6.2%,6.3-14.9%, 15.0-25.7% and 25.8%+) and BI-RADS categories 1-4 (almost entirely fatty to extremely dense). All models included age, postmenopausal hormone use (PMH), BMI, and menopausal status. The influence of acquisition parameters was evaluated by examining models with and without their inclusion. RESULTS: A total of 20,982 (50%) women participated in MMHS; 1058 (5%) with a prior history of BC were excluded, for a total cohort of 19,924. Compared to nonresponders, responders were younger (57.5 vs. 58.4 yrs), more likely to have ever used PMH (45% vs. 33%), and more likely to have a BC family history (19% vs 16%). The case-cohort consisted of 317 incident cases and 2300 women in the subcohort. Women were excluded from the current analysis if PD could not be estimated or if acquisition parameters were not available, leaving 249 cases and 1937 in the subcohort. As expected, PD was associated with BC [HR (95% CI): 1.0 (REF), 2.1 (1.4-3.1), 3.0 (2.0-4.5), and 4.6 (3.0-7.0) for quartiles; p-trend&lt;0.001]. Controlling for acquisition parameters attenuated the association [HR (95% CI): 1.0 (REF), 2.3 (1.5-3.4), 2.4 (1.6-3.7), and 3.0 (1.8-5.0) for quartiles; p-trend&lt;0.001]. Results for BI-RADS density were similar to those for PD. CONCLUSION: This study confirms that breast density is a significant risk factor for BC and demonstrates that the acquisition technique confounds the density and BC risk association. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 3716. doi:10.1158/1538-7445.AM2011-3716

Abstract Introduction: Mammographic breast density is one of the strongest risk factors for breast cancer and appears to be modifiable. However, whether longitudinal changes in density are associated with breast cancer risk is unknown. We hypothesize that decreases in breast density are associated with reduced breast cancer risk. Methods: The Mayo Mammography Health Study cohort enrolled 19,924 women having a screening mammogram (mgm) at the Mayo Clinic, Rochester, between 2003 and 2006. Participants were 35 years and older, had no prior breast cancer and lived in the tri-state area (MN, IA and WI). Risk factor data were collected through self-administered questionnaire and clinic databases. Incident breast cancers were identified through tumor registries (Mayo Clinic, MN, IA, WI). The BI-RADS, 4-category clinical density measure (fatty, scattered density, heterogeneous density and extreme density) was available on all mgms performed after 1996. A case-cohort design consisting of incident cases (n=241) and a random selection of the cohort (n=2300, 28 developed cancer) was used to examine the association between change in BI-RADS density and breast cancer. BI-RADS densities measured from the earliest available, and enrollment mgms (average 6 years apart) were used to assess density change. Proportional hazards regression was performed to estimate the hazard ratio (HR) associated with a change in one or more BI-RADS density categories over time, relative to staying within the same category. Age was used as the time scale, and the subcohort was weighted by the inverse of the sampling proportion. Analyses were adjusted for age, years between mgms, BI-RADS density at earliest mgm, and changes in BMI, postmenopausal hormone use, and menopausal status. Results: Longitudinal analyses were conducted on the 219 cases and 1900 non-cases in the subcohort with at least one mgm available prior to enrollment mgm. Compared to the subcohort, cases were older (56.8 vs. 52.9 years), more likely postmenopausal (73.4% vs. 61.6%), had higher BMI (29.0 vs 28.1) and were more frequently screened (8.0 vs. 6.9 pre-enrollment mgms). As expected, cases were more likely to have extreme density (BI-RADS=4) at earliest mgm than non-cases (16.2% vs 14.3%). Cases were less likely than non-cases to experience a reduction of one density category or more (37.0% vs 38.6%) following earliest mgm. Adjusting for potential confounders, women who decreased one BI-RADS category or more over an average 6 years were at reduced risk of breast cancer (HR=0.72, 95%CI: 0.50-0.99) compared to those whose density was unchanged. However, women who increased one or more BI-RADS categories had suggestion of increased risk (HR=1.52, 95%CI: 0.97-2.4). Conclusion: Women with a decrease in BI-RADS density category over 6 years may have decreased breast cancer risk relative to women whose breast density category remains stable. Two measures of breast density may inform women's risk beyond a measure at one point in time. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 4828.