Calculating Future 10-Year Breast Cancer Risks in Risk-Adapted Surveillance: A Method Comparison and Application in Clinical Practice
Year: 2024
Type: article
Abstract: The German Consortium for Hereditary Breast and Ovarian Cancer (GC-HBOC) has successfully implemented risk-adapted breast cancer surveillance for women at high breast cancer risk in Germany. Women with a family history of breast and ovarian cancer but without pathogenic germline variants in recognized breast cancer risk genes are recommended annual breast imaging if their predicted 10-year breast cancer risk is 5% or higher, using the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) breast cancer risk model, as outlined in the current GC-HBOC guideline. However, women who initially do not meet this risk threshold may do so later, even if there is no new cancer in their family. To determine when this threshold is crossed, one could annually repeat BOADICEA calculations using an aging pedigree: the "prediction by aging pedigree" (AP) approach. Alternatively, we propose a simplified and more practical "'conditional probability" (CP) approach, which calculates future risks based on the initial BOADICEA assessment. Using data from 6,661 women registered with GC-HBOC, both methods were compared. Initially, 74% of women, ages 30 to 48 years, had a 10-year breast cancer risk below 5%, but 53% exceeded this threshold at an older age based on the AP approach. Among the women with an initial risk below the threshold, the CP approach revealed that 99% of women exceeded the 5% threshold at the same or an earlier age compared with the AP approach (88% of cases were within the same year or 1 year earlier). The CP approach has been implemented as a user-friendly web application. Prevention Relevance: The German Consortium for Hereditary Breast Cancer recommends annual breast imaging for women if their 10-year breast cancer risk is 5% or higher. Women who initially do not meet this risk threshold may do so later. We propose a simple method to determine future risks based on initial risk assessments.
Source: Cancer Prevention Research
Authors: Silke Zachariae, Anne S. Quante, Marion Kiechle, Kerstin Rhiem, Tanja N. Fehm, Jörg-Gunther Schröder, Judit Horvath, Elena Leinert, Nicola Dikow, Joelle Ronez, Mirjam Schönfeld, Marion T. van Mackelenbergh, Ulrich A. Schatz, Cornelia Meisel, Bahriye Aktas, Dennis Witt, Yasmin Mehraein, Bernhard H.F. Weber, Christine Solbach, Dorothee Speiser, Juliane Hoyer, Gesine Faigle-Krehl, Christiane D. Much, Alma-Verena Müller-Rausch, Pablo Villavicencio-Lorini, Maggie Banys-Paluchowski, Daniel Pieh, Rita K. Schmutzler, Christine Fischer, Christoph Engel
Institutions: Leipzig University, TUM Klinikum, Technical University of Munich, University of Cologne, University Hospital Cologne, Düsseldorf University Hospital, University of Würzburg, Institut für Humangenetik, University of Münster, University Hospital Münster, Universität Ulm, University Hospital Ulm, Heidelberg University, University Hospital Heidelberg, Medizinische Hochschule Hannover, Ludwig-Maximilians-Universität München, University Hospital Schleswig-Holstein, University Hospital Carl Gustav Carus, Technische Universität Dresden, University Hospital Leipzig, University of Tübingen, Universitätsmedizin Göttingen, University of Göttingen, University Hospital Regensburg, University of Regensburg, Goethe University Frankfurt, University Hospital Frankfurt, Charité - Universitätsmedizin Berlin, Friedrich-Alexander-Universität Erlangen-Nürnberg, University of Freiburg, University Medical Center Freiburg, Universitätsmedizin Greifswald, Universität Greifswald, University Medical Center Hamburg-Eppendorf, University Hospital in Halle, Martin Luther University Halle-Wittenberg, University of Lübeck, Johannes Gutenberg University Mainz, University Medical Center of the Johannes Gutenberg University Mainz
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: hybrid
OpenAlex ID: W4404613901