Prediction and clinical utility of a contralateral breast cancer risk model

Show simple item record Giardiello, Daniele Steyerberg, Ewout W Hauptmann, Michael Adank, Muriel A Akdeniz, Delal Blomqvist, Carl Bojesen, Stig E Bolla, Manjeet K Brinkhuis, Mariël Chang-Claude, Jenny Czene, Kamila Devilee, Peter Dunning, Alison M Easton, Douglas F Eccles, Diana M Fasching, Peter A Figueroa, Jonine Flyger, Henrik García-Closas, Montserrat Haeberle, Lothar Haiman, Christopher A Hall, Per Hamann, Ute Hopper, John L Jager, Agnes Jakubowska, Anna Jung, Audrey Keeman, Renske Kramer, Iris Lambrechts, Diether Le Marchand, Loic Lindblom, Annika Lubiński, Jan Manoochehri, Mehdi Mariani, Luigi Nevanlinna, Heli Oldenburg, Hester S A Pelders, Saskia Pharoah, Paul D P Shah, Mitul Siesling, Sabine Smit, Vincent T H B M Southey, Melissa C Tapper, William J Tollenaar, Rob A E M van den Broek, Alexandra J van Deurzen, Carolien H M van Leeuwen, Flora E van Ongeval, Chantal Van’t Veer, Laura J Wang, Qin Wendt, Camilla Westenend, Pieter J Hooning, Maartje J Schmidt, Marjanka K 2019-12-22T12:28:40Z 2019-12-22T12:28:40Z 2019-12-17
dc.identifier.citation Breast Cancer Research. 2019 Dec 17;21(1):144
dc.description.abstract Abstract Background Breast cancer survivors are at risk for contralateral breast cancer (CBC), with the consequent burden of further treatment and potentially less favorable prognosis. We aimed to develop and validate a CBC risk prediction model and evaluate its applicability for clinical decision-making. Methods We included data of 132,756 invasive non-metastatic breast cancer patients from 20 studies with 4682 CBC events and a median follow-up of 8.8 years. We developed a multivariable Fine and Gray prediction model (PredictCBC-1A) including patient, primary tumor, and treatment characteristics and BRCA1/2 germline mutation status, accounting for the competing risks of death and distant metastasis. We also developed a model without BRCA1/2 mutation status (PredictCBC-1B) since this information was available for only 6% of patients and is routinely unavailable in the general breast cancer population. Prediction performance was evaluated using calibration and discrimination, calculated by a time-dependent area under the curve (AUC) at 5 and 10 years after diagnosis of primary breast cancer, and an internal-external cross-validation procedure. Decision curve analysis was performed to evaluate the net benefit of the model to quantify clinical utility. Results In the multivariable model, BRCA1/2 germline mutation status, family history, and systemic adjuvant treatment showed the strongest associations with CBC risk. The AUC of PredictCBC-1A was 0.63 (95% prediction interval (PI) at 5 years, 0.52–0.74; at 10 years, 0.53–0.72). Calibration-in-the-large was -0.13 (95% PI: -1.62–1.37), and the calibration slope was 0.90 (95% PI: 0.73–1.08). The AUC of Predict-1B at 10 years was 0.59 (95% PI: 0.52–0.66); calibration was slightly lower. Decision curve analysis for preventive contralateral mastectomy showed potential clinical utility of PredictCBC-1A between thresholds of 4–10% 10-year CBC risk for BRCA1/2 mutation carriers and non-carriers. Conclusions We developed a reasonably calibrated model to predict the risk of CBC in women of European-descent; however, prediction accuracy was moderate. Our model shows potential for improved risk counseling, but decision-making regarding contralateral preventive mastectomy, especially in the general breast cancer population where limited information of the mutation status in BRCA1/2 is available, remains challenging.
dc.publisher BioMed Central
dc.subject Contralateral breast cancer
dc.subject Risk prediction model
dc.subject Clinical decision-making
dc.subject BRCA mutation carriers
dc.title Prediction and clinical utility of a contralateral breast cancer risk model 2019-12-22T12:28:41Z
dc.language.rfc3066 en
dc.rights.holder The Author(s).

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