Prediction and clinical utility of a contralateral breast cancer risk model

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  • Daniele Giardiello
  • Ewout W. Steyerberg
  • Michael Hauptmann
  • Muriel A. Adank
  • Delal Akdeniz
  • Carl Blomqvist
  • 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 Garciá-Closas
  • Lothar Haeberle
  • Christopher 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

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.

TidsskriftBreast Cancer Research
Antal sider13
StatusUdgivet - dec. 2019

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