Prediction and clinical utility of a contralateral breast cancer risk model

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dc.contributor.author Giardiello, Daniele
dc.contributor.author Steyerberg, Ewout W
dc.contributor.author Hauptmann, Michael
dc.contributor.author Adank, Muriel A
dc.contributor.author Akdeniz, Delal
dc.contributor.author Blomqvist, Carl
dc.contributor.author Bojesen, Stig E
dc.contributor.author Bolla, Manjeet K
dc.contributor.author Brinkhuis, Mariël
dc.contributor.author Chang-Claude, Jenny
dc.contributor.author Czene, Kamila
dc.contributor.author Devilee, Peter
dc.contributor.author Dunning, Alison M
dc.contributor.author Easton, Douglas F
dc.contributor.author Eccles, Diana M
dc.contributor.author Fasching, Peter A
dc.contributor.author Figueroa, Jonine
dc.contributor.author Flyger, Henrik
dc.contributor.author García-Closas, Montserrat
dc.contributor.author Haeberle, Lothar
dc.contributor.author Haiman, Christopher A
dc.contributor.author Hall, Per
dc.contributor.author Hamann, Ute
dc.contributor.author Hopper, John L
dc.contributor.author Jager, Agnes
dc.contributor.author Jakubowska, Anna
dc.contributor.author Jung, Audrey
dc.contributor.author Keeman, Renske
dc.contributor.author Kramer, Iris
dc.contributor.author Lambrechts, Diether
dc.contributor.author Le Marchand, Loic
dc.contributor.author Lindblom, Annika
dc.contributor.author Lubiński, Jan
dc.contributor.author Manoochehri, Mehdi
dc.contributor.author Mariani, Luigi
dc.contributor.author Nevanlinna, Heli
dc.contributor.author Oldenburg, Hester S A
dc.contributor.author Pelders, Saskia
dc.contributor.author Pharoah, Paul D P
dc.contributor.author Shah, Mitul
dc.contributor.author Siesling, Sabine
dc.contributor.author Smit, Vincent T H B M
dc.contributor.author Southey, Melissa C
dc.contributor.author Tapper, William J
dc.contributor.author Tollenaar, Rob A E M
dc.contributor.author van den Broek, Alexandra J
dc.contributor.author van Deurzen, Carolien H M
dc.contributor.author van Leeuwen, Flora E
dc.contributor.author van Ongeval, Chantal
dc.contributor.author Van’t Veer, Laura J
dc.contributor.author Wang, Qin
dc.contributor.author Wendt, Camilla
dc.contributor.author Westenend, Pieter J
dc.contributor.author Hooning, Maartje J
dc.contributor.author Schmidt, Marjanka K
dc.date.accessioned 2019-12-22T12:28:40Z
dc.date.available 2019-12-22T12:28:40Z
dc.date.issued 2019-12-17
dc.identifier.citation Breast Cancer Research. 2019 Dec 17;21(1):144
dc.identifier.uri http://hdl.handle.net/10138/308717
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
dc.date.updated 2019-12-22T12:28:41Z
dc.language.rfc3066 en
dc.rights.holder The Author(s).
dc.type.uri http://purl.org/eprint/entityType/ScholarlyWork
dc.type.uri http://purl.org/eprint/entityType/Expression
dc.type.uri http://purl.org/eprint/type/JournalArticle

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