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Der in den letzten Jahrzenten rasant zunehmende Dokumentationsaufwand in der Medizin hat sich auch auf die gynakologische Onkologie ausgewirkt. Speziell die Notwendigkeit, maligne Erkrankungen sowohl longitudinal als auch horizontal zu dokumentieren, wie auch Zentralisierung, Leitlinienkonformitat und Qualitatskontrolle zwingen zur Dokumentation groser Datenmengen, oft mehrfach in unterschiedlichen Systemen (Mammographie-Screening, Patientinnenakte, DMP, BQS/AQUA, Studien etc.). Durch redundante Erfassungen zu dokumentierender Fakten entstehen Datenmengen gigantischen Ausmases. Die zunehmende Digitalisierung sowie Normierung und Zentralisierung der Dokumentation, wie sie im Rahmen des Nationalen Krebsplans gefordert werden, konnen moglicherweise, v. a. bei den Mehrfachdokumentationen, Abhilfe schaffen.

Purpose: Mammographic characteristics are known to be correlated to breast cancer risk. Percent mammographic density (PMD), as assessed by computer-assisted methods, is an established risk factor for breast cancer. Along with this assessment the absolute dense area (DA) of the breast is reported as well. Aim of this study was to assess the predictive value of DA concerning breast cancer risk in addition to other risk factors and in addition to PMD. Methods: We conducted a case control study with hospital-based patients with a diagnosis of invasive breast cancer and healthy women as controls. A total of 561 patients and 376 controls with available mammographic density were included into this study. We describe the differences concerning the common risk factors BMI, parital status, use of hormone replacement therapy (HRT) and menopause between cases and controls and estimate the odds ratios for PMD and DA, adjusted for the mentioned risk factors. Furthermore we compare the prediction models with each other to find out whether the addition of DA improves the model. Results: Mammographic density and DA were highly correlated with each other. Both variables were as well correlated to the commonly known risk factors with an expected direction and strength, however PMD (ρ = −0.56) was stronger correlated to BMI than DA (ρ = −0.11). The group of women within the highest quartil of PMD had an OR of 2.12 (95 % CI: 1.25–3.62). This could not be seen for the fourth quartile concerning DA. However the assessment of breast cancer risk could be improved by including DA in a prediction model in addition to common risk factors and PMD. Conclusions: The inclusion of the parameter DA into a prediction model for breast cancer in addition to established risk factors and PMD could improve the breast cancer risk assessment. As DA is measured together with PMD in the process of computer-assisted assessment of PMD it might be considered to include it as one additional breast cancer risk factor that is obtained from breast imaging.