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 (P trends < 0.001). Percentage density from the ipsilateral side [craniocaudal (CC): odds ratios (ORs), 1.0 (ref), 1.7, 3

Between 1988 and 1999, 127 patients with poor-risk acute lymphoblastic leukemia (ALL) received a matched unrelated donor transplant using marrow procured by National Marrow Donor Program (NMDP) collection centers and sent out to 46 transplant centers worldwide. Poor risk was defined by the presence of the translocations t(9;22) (n ‫؍‬ 97), or t(4;11) (n ‫؍‬ 25), or t(1;19) (n ‫؍‬ 5). Sixty-four patients underwent transplantation in first remission (CR1), 16 in CR2 or CR3, and 47 patients had relapsed ALL or primary induction failure (PIF). Overall survival at 2 years from transplant was 40% for patients in CR1, 17% in CR2/3, and 5% in PIF or relapse. Treatment-related mortality (TRM) and relapse mortality, estimated as competing risk factors, were 54% and 6%, respectively, in CR1, 75% and 8% in CR2/3, and 64% and 31% in PIF or relapse. Currently 23 CR1 patients are alive and free of disease with a median follow-up of 24 months (range, 3-97). Multivariable analysis showed that CR1, shorter interval from diagnosis to transplantation, DRB1 match, negative cytomegalovirus (CMV) serology (patient and donor), and presence of the Philadelphia chromosome, t(9;22), were independently associated with better disease-free survival (DFS). Transplantation in CR and presence of t(9;22) were associated with lower risk of relapse. Shorter interval from diagnosis to transplantation, DRB1-match, negative CMV, higher marrow cell dose, and Karnofsky score of 90 or higher were associated with less TRM. These results indicate that, despite a relatively high TRM, the low relapse rate resulted in a 37%

Mammographic percent density (PD) is a strong risk factor for breast cancer, but there has been relatively little systematic evaluation of other features in mammographic images that might additionally predict breast cancer risk. We evaluated the association of a large number of image texture features with risk of breast cancer using a clinic-based case-control study of digitized film mammograms, all with screening mammograms before breast cancer diagnosis. The sample was split into training (123 cases and 258 controls) and validation (123 cases and 264 controls) data sets. Age-adjusted and body mass index (BMI) -adjusted odds ratios (OR) per SD change in the feature, 95% confidence intervals, and the area under the receiver operator characteristic curve (AUC) were obtained using logistic regression. A bootstrap approach was used to identify the strongest features in the training data set, and results for features that validated in the second half of the sample were reported using the full data set. The mean age at mammography was 64.0 F 10.2 years, and the mean time from mammography to breast cancer was 3.7 F 1.0 (range, 2.0-5.9 years). PD was associated with breast cancer risk (OR, 1.49; 95% confidence interval, 1.25-1.78). The strongest features that validated from each of several classes (Markovian, run length, Laws, wavelet, and Fourier) showed similar ORs as PD and predicted breast cancer at a similar magnitude (AUC = 0.58-0.60) as PD (AUC = 0.58). All of these features were automatically calculated (unlike PD) and measure texture at a coarse scale. These features were moderately correlated with PD (r = 0.39-0.76), and after adjustment for PD, each of the features attenuated only slightly and retained statistical significance. However, simultaneous inclusion of these features in a model with PD did not significantly improve the ability to predict breast cancer. (Cancer Epidemiol Biomarkers Prev 2009;18(3):837 -45)