Prediction and clinical utility of a contralateral breast cancer risk model

Daniele Giardiello, Ewout W Steyerberg, Michael Hauptmann, Muriel A Adank, Delal Akdeniz, Carl Blomqvist, Stig E Bojesen, Manjeet K Bolla, Mariël Brinkhuis, Jenny Chang-Claude, Kamila Czene, Peter Devilee, Alison M Dunning, Douglas F Easton, Diana M Eccles, Peter A Fasching, Jonine Figueroa, Henrik Flyger, Montserrat García-Closas, Lothar HaeberleChristopher A Haiman, Per Hall, Ute Hamann, John L Hopper, Agnes Jager, Anna Jakubowska, Audrey Jung, Renske Keeman, Iris Kramer, Diether Lambrechts, Loic Le Marchand, Annika Lindblom, Jan Lubiński, Mehdi Manoochehri, Luigi Mariani, Heli Nevanlinna, Hester S A Oldenburg, Saskia Pelders, Paul D P Pharoah, Mitul Shah, Sabine Siesling, Vincent T H B M Smit, Melissa C Southey, William J Tapper, Rob A E M Tollenaar, Alexandra J van den Broek, Carolien H M van Deurzen, Flora E van Leeuwen, Chantal van Ongeval, Laura J Van't Veer, Qin Wang, Camilla Wendt, Pieter J Westenend, Maartje J Hooning, Marjanka K Schmidt

28 Citationer (Scopus)

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.

OriginalsprogEngelsk
TidsskriftBreast Cancer Research
Vol/bind21
Udgave nummer1
Sider (fra-til)144
ISSN1465-542X
DOI
StatusUdgivet - 17 dec. 2019

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