Citations

Abstract Abstract #3103 Introduction: Lymphedema is well-known in the arm but can also occur in the breast. Diagnosis is currently non-standardized and based on clinical impression. We sought to determine if skin thickness could serve as a useful measure in the diagnosis of breast lymphedema (BLE).
 Methods: Patients undergoing unilateral non-mastectomy procedures were enrolled on this prospective clinical study preoperatively and evaluated for signs and symptoms of lymphedema in the operated breast in the postoperative period. Patients with established BLE were also studied. BLE diagnosis was independent of ultrasound findings and was based on a graded physical exam targeting clinical signs of edema and erythema. Skin thickness was measured with ultrasound at the 6 o'clock position of both breasts. Differences in skin thickness were assessed by the ratio of skin thickness in the operated and contralateral breasts, with comparison of means by two-sample t-test. Receiver operating characteristic curves were constructed to estimate the area under the curve (AUC) and to determine optimal cutpoints.
 Results: Ninety-seven women were studied; 85 were enrolled preoperatively and 12 postoperatively after a diagnosis of BLE. Median length of follow-up overall was 8 months. Of the 97 women, 46 had BLE at one or more follow-up visit with median time to first diagnosis of 3 months. Mean measured skin thickness was 2.3 ± 0.5mm in unaffected breasts and 3.2 ± 1.0 mm in operated breasts. Women with BLE had significantly greater skin thickness in the operated breast compared to the contralateral breast, with a mean skin thickness ratio of 1.83 ± 0.57 in patients with BLE compared to 1.18 ± 0.40 among those without BLE (p<0.0001). Skin thickness ratio provided excellent discrimination for BLE, with an area under the curve (AUC) of 0.86. At a ratio of 1.32 or greater (32% greater skin thickness in operated versus contralateral breast), sensitivity and specificity for diagnosing BLE were 81% and 82% respectively. A single measure of skin thickness in the operated breast also correlated well with diagnosis of BLE, with skin thickness of 3.2 mm or greater demonstrating sensitivity of 78% and specificity of 82%, AUC 0.82.
 Conclusions: Breast lymphedema results in skin edema and increased skin thickness that can be quantified with ultrasound. Measuring breast skin thickness- either as a single reading or as a ratio to the unaffected breast- may be useful in the diagnosis of breast lymphedema. Citation Information: Cancer Res 2009;69(2 Suppl):Abstract nr 3103.

Abstract Abstract #1005 Background: Several models for the prediction of non-sentinel lymph node metastasis (+NSLN) in sentinel lymph node positive (+SLN) breast cancer patients have been published. Multiple variables have been shown to be important in prediction of +NSLN. To this date, models that utilize 7-8 variables have been significantly more discriminatory than models using only 2-3 variables. In this study, we evaluate the performance of the newly created Stanford online calculator (SOC) to predict the likelihood of +NSLN. The SOC uses 3 variables: primary tumor size, sentinel lymph node metastasis size, and presence/absence of lymphovascular invasion.
 Methods: From 1997-2004, 465 breast cancer patients with clinically negative axillae were found to have a +SLN and underwent completion axillary lymph node dissection (ALND) at the Mayo Clinic. Using the SOC the probability of having +NSLN was determined for each patient. Complete variable data was available for 464 patients which make up the cohort reported here. A receiver-operating characteristic (ROC) curve was created and the area-under-the-curve (AUC) was calculated. Mean probabilities of patients with and without +NSLN were compared. Additionally, patients with a low Stanford probability were examined to determine the model's accuracy in discriminating patients who could potentially be spared ALND. These results were compared to the results of the Mayo Clinic and Memorial Sloan Kettering Cancer Center (MSKCC) nomograms which have been previously applied to this same cohort of patients.
 Results: The AUC of the Stanford model was 0.72 (95% CI 0.67-0.77). The mean Stanford probabilities for patients with and without NSLN disease were 0.75 (range 0.06 to 1.0) and 0.50 (range 0.05 to 1.0), respectively (p < 0.0001). Examining patients with a Stanford probability <= 10%, we found that 47 patients met this criterion; 6 had +NSLN and 41 did not, for a false negative (FN) prediction rate of 13%. There was only 1 patient with Stanford probability <= 5%, and this patient did not have +NSLN. The AUC of the MSKCC and Mayo nomograms in this same patient population was 0.74 and 0.77, respectively. The AUCs did not differ significantly (p = 0.13) among the 3 models.
 
 Conclusion: Despite using only three variables, the Stanford nomogram appears to perform as well as, but not better than the MSKCC and Mayo nomograms in our patient cohort. Further validation in other patient populations is needed prior to widespread utilization of this nomogram. Citation Information: Cancer Res 2009;69(2 Suppl):Abstract nr 1005.

Purpose Accurate breast cancer risk assessment is vital to personalize screening and risk reduction strategies. Women with atypical hyperplasia have a four-fold higher risk of breast cancer. We evaluated the performance of the Tyrer-Cuzick model, which was designed to predict 10-year risk of breast cancer development, in a well-defined cohort of women with atypia. Patients and Methods The Mayo Benign Breast Disease cohort includes 9,376 women who had a benign breast biopsy between 1967 and 1991. Among those, 331 women with atypia were identified by our study pathologists. Risk factor data for the Tyrer-Cuzick model were collated for each woman and used to predict individual risk of developing invasive breast cancer within 10 years. Results Over a median follow-up of 14.6 years, 64 (19%) of the 331 women developed invasive breast cancer. In the first 10 years after biopsy, 31 women developed invasive breast cancer whereas the Tyrer-Cuzick model predicted 58.9. The observed-to-predicted ratio was 0.53 (95% CI, 0.37 to 0.75). The concordance statistic was 0.540, revealing that the Tyrer-Cuzick model did not accurately distinguish, on an individual level, between women who developed invasive breast cancer and those who did not. Conclusion The Tyrer-Cuzick model significantly overestimated risk of breast cancer for women with atypia, and individual risk estimates showed poor concordance between predicted risk and invasive breast cancer development. Thus, we cannot recommend the use of the Tyrer-Cuzick model to predict 10-year breast cancer risk in women with atypical hyperplasia.

Purpose Accurate, individualized risk prediction for breast cancer is lacking. Tissue-based features may help to stratify women into different risk levels. Breast lobules are the anatomic sites of origin of breast cancer. As women age, these lobular structures should regress, which results in reduced breast cancer risk. However, this does not occur in all women. Methods We have quantified the extent of lobule regression on a benign breast biopsy in 85 patients who developed breast cancer and 142 age-matched controls from the Mayo Benign Breast Disease Cohort, by determining number of acini per lobule and lobular area. We also calculated Gail model 5-year predicted risks for these women. Results There is a step-wise increase in breast cancer risk with increasing numbers of acini per lobule (P = .0004). Adjusting for Gail model score, parity, histology, and family history did not attenuate this association. Lobular area was similarly associated with risk. The Gail model estimates were associated with risk of breast cancer (P = .03). We examined the individual accuracy of these measures using the concordance (c) statistic. The Gail model c statistic was 0.60 (95% CI, 0.50 to 0.70); the acinar count c statistic was 0.65 (95% CI, 0.54 to 0.75). Combining acinar count and lobular area, the c statistic was 0.68 (95% CI, 0.58 to 0.78). Adding the Gail model to these measures did not improve the c statistic. Conclusion Novel, tissue-based features that reflect the status of a woman's normal breast lobules are associated with breast cancer risk. These features may offer a novel strategy for risk prediction.

Purpose Recent changes have occurred in the presurgical planning for breast cancer, including the introduction of preoperative breast magnetic resonance imaging (MRI). We sought to analyze the trends in mastectomy rates and the relationship to preoperative MRI and surgical year at Mayo Clinic, Rochester, MN. Patients and Methods We identified 5,405 patients who underwent surgery between 1997 and 2006. Patients undergoing MRI were identified from a prospective database. Trends in mastectomy rate and the association of MRI with surgery type were analyzed. Multiple logistic regression was used to assess the effect of surgery year and MRI on surgery type, while adjusting for potential confounding variables. Results Mastectomy rates differed significantly across time (P < .0001), and decreased from 45% in 1997% to 31% in 2003, followed by increasing rates for 2004 to 2006. The use of MRI increased from 10% in 2003% to 23% in 2006 (P < .0001). Patients with MRI were more likely to undergo mastectomy than those without MRI (54% v 36%; P < .0001). However, mastectomy rates increased from 2004 to 2006 predominantly among patients without MRI (29% in 2003% to 41% in 2006; P < .0001). In a multivariable model, both MRI (odds ratio [OR], 1.7; P < .0001) and surgical year (compared to 2003 OR: 1.4 for 2004, 1.8 for 2005, and 1.7 for 2006; P < .0001) were independent predictors of mastectomy. Conclusion After a steady decline, mastectomy rates have increased in recent years with both surgery year and MRI as significant predictors for type of surgery. Further studies are needed to evaluate the role of MRI and other factors influencing surgical planning.

Purpose An accurate estimate of a woman's 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.