Prediction and clinical utility of a contralateral breast cancer risk model
Creators: name: Giardiello, Daniele
name: Steyerberg, Ewout W.
name: Hauptmann, Michael
name: Adank, Muriel A.
name: Akdeniz, Delal
name: Blomqvist, Carl
name: Bojesen, Stig E.
name: Bolla, Manjeet K.
name: Brinkhuis, Mariël
name: Chang-Claude, Jenny
name: Czene, Kamila
name: Devilee, Peter
name: Dunning, Alison M.
name: Easton, Douglas F.
name: Eccles, Diana M.
name: Fasching, Peter A.
name: Figueroa, Jonine
name: Flyger, Henrik
name: García-Closas, Montserrat
name: Haeberle, Lothar
name: Haiman, Christopher A.
name: Hall, Per
name: Hamann, Ute
name: Hopper, John L.
name: Jager, Agnes
name: Jakubowska, Anna
name: Jung, Audrey
name: Keeman, Renske
name: Kramer, Iris
name: Lambrechts, Diether
name: Le Marchand, Loic
name: Lindblom, Annika
name: Lubiński, Jan
name: Manoochehri, Mehdi
name: Mariani, Luigi
name: Nevanlinna, Heli
name: Oldenburg, Hester S. A.
name: Pelders, Saskia
name: Pharoah, Paul D. P.
name: Shah, Mitul
name: Siesling, Sabine
name: Smit, Vincent T. H. B. M.
name: Southey, Melissa C.
name: Tapper, William J.
name: Tollenaar, Rob A. E. M.
name: Van Den Broek, Alexandra J.
name: Van Deurzen, Carolien H. M.
name: Van Leeuwen, Flora E.
name: Van Ongeval, Chantal
name: Van’t Veer, Laura J.
name: Wang, Qin
name: Wendt, Camilla
name: Westenend, Pieter J.
name: Hooning, Maartje J.
name: Schmidt, Marjanka K.
Publisher: name: Apollo - University of Cambridge Repository
Year: 2019
Subjects: Research Article
Contralateral breast cancer
Risk prediction model
Clinical decision-making
BRCA mutation carriers

description: 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.