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Prediction and clinical utility of a contralateral breast cancer risk model

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. M...

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Autores principales: 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.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6918633/
https://www.ncbi.nlm.nih.gov/pubmed/31847907
http://dx.doi.org/10.1186/s13058-019-1221-1
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author 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.
author_facet 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.
author_sort Giardiello, Daniele
collection PubMed
description 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.
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spelling pubmed-69186332019-12-20 Prediction and clinical utility of a contralateral breast cancer risk model 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. Breast Cancer Res Research Article 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. BioMed Central 2019-12-17 2019 /pmc/articles/PMC6918633/ /pubmed/31847907 http://dx.doi.org/10.1186/s13058-019-1221-1 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
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.
Prediction and clinical utility of a contralateral breast cancer risk model
title Prediction and clinical utility of a contralateral breast cancer risk model
title_full Prediction and clinical utility of a contralateral breast cancer risk model
title_fullStr Prediction and clinical utility of a contralateral breast cancer risk model
title_full_unstemmed Prediction and clinical utility of a contralateral breast cancer risk model
title_short Prediction and clinical utility of a contralateral breast cancer risk model
title_sort prediction and clinical utility of a contralateral breast cancer risk model
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6918633/
https://www.ncbi.nlm.nih.gov/pubmed/31847907
http://dx.doi.org/10.1186/s13058-019-1221-1
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