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Evaluating the Effectiveness of Using Standard Mammogram Form to Predict Breast Cancer Risk: Case-Control Study

¹ Department of Radiology, University of Cambridge, Addenbrooke's Hospital; ² Strangeways Research Laboratory, Cambridge, United Kingdom; ³ Department of Engineering Science, University of Oxford, Oxford, United Kingdom; and ⁴ Highnam Associates Limited, Wellington, New Zealand

Requests for reprints: Jane Ding, Department of Radiology, University of Cambridge, Addenbrooke's Hospital, Box 218, Hills Road, Cambridge CB2 0QQ, United Kingdom. Phone: 44-1223-256119; Fax: 44-1223-330915. E-mail: jjd32{at}cam.ac.uk

Breast density is a well-known breast cancer risk factor. Most current methods of measuring breast density are area based and subjective. Standard mammogram form (SMF) is a computer program using a volumetric approach to estimate the percent density in the breast. The aim of this study is to evaluate the current implementation of SMF as a predictor of breast cancer risk by comparing it with other widely used density measurement methods. The case-control study comprised 634 cancers with 1,880 age-matched controls combined from the Cambridge and Norwich Breast Screening Programs. Data collection involved assessing the films based both on Wolfe's parenchymal patterns and on visual estimation of percent density and then digitizing the films for computer analysis (interactive threshold technique and SMF). Logistic regression was used to produce odds ratios associated with increasing categories of breast density. Density measures from all four methods were strongly associated with breast cancer risk in the overall population. The stepwise rises in risk associated with increasing density as measured by the threshold method were 1.37 [95% confidence interval (95% CI), 1.03-1.82], 1.80 (95% CI, 1.36-2.37), and 2.45 (95% CI, 1.86-3.23). For each increasing quartile of SMF density measures, the risks were 1.11 (95% CI, 0.85-1.46), 1.31 (95% CI, 1.00-1.71), and 1.92 (95% CI, 1.47-2.51). After the model was adjusted for SMF results, the threshold readings maintained the same strong stepwise increase in density-risk relationship. On the contrary, once the model was adjusted for threshold readings, SMF outcome was no longer related to cancer risk. The available implementation of SMF is not a better cancer risk predictor compared with the thresholding method. (Cancer Epidemiol Biomarkers Prev 2008;17(5):1074–81)