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Improved risk estimation of locoregional recurrence, secondary contralateral tumors and distant metastases in early breast cancer: the INFLUENCE 2.0 model

PURPOSE: To extend the functionality of the existing INFLUENCE nomogram for locoregional recurrence (LRR) of breast cancer toward the prediction of secondary primary tumors (SP) and distant metastases (DM) using updated follow-up data and the best suitable statistical approaches. METHODS: Data on wo...

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Autores principales: Völkel, Vinzenz, Hueting, Tom A., Draeger, Teresa, van Maaren, Marissa C., de Munck, Linda, Strobbe, Luc J. A., Sonke, Gabe S., Schmidt, Marjanka K., van Hezewijk, Marjan, Groothuis-Oudshoorn, Catharina G. M., Siesling, Sabine
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8505302/
https://www.ncbi.nlm.nih.gov/pubmed/34338943
http://dx.doi.org/10.1007/s10549-021-06335-z
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author Völkel, Vinzenz
Hueting, Tom A.
Draeger, Teresa
van Maaren, Marissa C.
de Munck, Linda
Strobbe, Luc J. A.
Sonke, Gabe S.
Schmidt, Marjanka K.
van Hezewijk, Marjan
Groothuis-Oudshoorn, Catharina G. M.
Siesling, Sabine
author_facet Völkel, Vinzenz
Hueting, Tom A.
Draeger, Teresa
van Maaren, Marissa C.
de Munck, Linda
Strobbe, Luc J. A.
Sonke, Gabe S.
Schmidt, Marjanka K.
van Hezewijk, Marjan
Groothuis-Oudshoorn, Catharina G. M.
Siesling, Sabine
author_sort Völkel, Vinzenz
collection PubMed
description PURPOSE: To extend the functionality of the existing INFLUENCE nomogram for locoregional recurrence (LRR) of breast cancer toward the prediction of secondary primary tumors (SP) and distant metastases (DM) using updated follow-up data and the best suitable statistical approaches. METHODS: Data on women diagnosed with non-metastatic invasive breast cancer were derived from the Netherlands Cancer Registry (n = 13,494). To provide flexible time-dependent individual risk predictions for LRR, SP, and DM, three statistical approaches were assessed; a Cox proportional hazard approach (COX), a parametric spline approach (PAR), and a random survival forest (RSF). These approaches were evaluated on their discrimination using the Area Under the Curve (AUC) statistic and on calibration using the Integrated Calibration Index (ICI). To correct for optimism, the performance measures were assessed by drawing 200 bootstrap samples. RESULTS: Age, tumor grade, pT, pN, multifocality, type of surgery, hormonal receptor status, HER2-status, and adjuvant therapy were included as predictors. While all three approaches showed adequate calibration, the RSF approach offers the best optimism-corrected 5-year AUC for LRR (0.75, 95%CI: 0.74–0.76) and SP (0.67, 95%CI: 0.65–0.68). For the prediction of DM, all three approaches showed equivalent discrimination (5-year AUC: 0.77–0.78), while COX seems to have an advantage concerning calibration (ICI < 0.01). Finally, an online calculator of INFLUENCE 2.0 was created. CONCLUSIONS: INFLUENCE 2.0 is a flexible model to predict time-dependent individual risks of LRR, SP and DM at a 5-year scale; it can support clinical decision-making regarding personalized follow-up strategies for curatively treated non-metastatic breast cancer patients. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10549-021-06335-z.
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spelling pubmed-85053022021-10-19 Improved risk estimation of locoregional recurrence, secondary contralateral tumors and distant metastases in early breast cancer: the INFLUENCE 2.0 model Völkel, Vinzenz Hueting, Tom A. Draeger, Teresa van Maaren, Marissa C. de Munck, Linda Strobbe, Luc J. A. Sonke, Gabe S. Schmidt, Marjanka K. van Hezewijk, Marjan Groothuis-Oudshoorn, Catharina G. M. Siesling, Sabine Breast Cancer Res Treat Epidemiology PURPOSE: To extend the functionality of the existing INFLUENCE nomogram for locoregional recurrence (LRR) of breast cancer toward the prediction of secondary primary tumors (SP) and distant metastases (DM) using updated follow-up data and the best suitable statistical approaches. METHODS: Data on women diagnosed with non-metastatic invasive breast cancer were derived from the Netherlands Cancer Registry (n = 13,494). To provide flexible time-dependent individual risk predictions for LRR, SP, and DM, three statistical approaches were assessed; a Cox proportional hazard approach (COX), a parametric spline approach (PAR), and a random survival forest (RSF). These approaches were evaluated on their discrimination using the Area Under the Curve (AUC) statistic and on calibration using the Integrated Calibration Index (ICI). To correct for optimism, the performance measures were assessed by drawing 200 bootstrap samples. RESULTS: Age, tumor grade, pT, pN, multifocality, type of surgery, hormonal receptor status, HER2-status, and adjuvant therapy were included as predictors. While all three approaches showed adequate calibration, the RSF approach offers the best optimism-corrected 5-year AUC for LRR (0.75, 95%CI: 0.74–0.76) and SP (0.67, 95%CI: 0.65–0.68). For the prediction of DM, all three approaches showed equivalent discrimination (5-year AUC: 0.77–0.78), while COX seems to have an advantage concerning calibration (ICI < 0.01). Finally, an online calculator of INFLUENCE 2.0 was created. CONCLUSIONS: INFLUENCE 2.0 is a flexible model to predict time-dependent individual risks of LRR, SP and DM at a 5-year scale; it can support clinical decision-making regarding personalized follow-up strategies for curatively treated non-metastatic breast cancer patients. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10549-021-06335-z. Springer US 2021-08-02 2021 /pmc/articles/PMC8505302/ /pubmed/34338943 http://dx.doi.org/10.1007/s10549-021-06335-z Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Epidemiology
Völkel, Vinzenz
Hueting, Tom A.
Draeger, Teresa
van Maaren, Marissa C.
de Munck, Linda
Strobbe, Luc J. A.
Sonke, Gabe S.
Schmidt, Marjanka K.
van Hezewijk, Marjan
Groothuis-Oudshoorn, Catharina G. M.
Siesling, Sabine
Improved risk estimation of locoregional recurrence, secondary contralateral tumors and distant metastases in early breast cancer: the INFLUENCE 2.0 model
title Improved risk estimation of locoregional recurrence, secondary contralateral tumors and distant metastases in early breast cancer: the INFLUENCE 2.0 model
title_full Improved risk estimation of locoregional recurrence, secondary contralateral tumors and distant metastases in early breast cancer: the INFLUENCE 2.0 model
title_fullStr Improved risk estimation of locoregional recurrence, secondary contralateral tumors and distant metastases in early breast cancer: the INFLUENCE 2.0 model
title_full_unstemmed Improved risk estimation of locoregional recurrence, secondary contralateral tumors and distant metastases in early breast cancer: the INFLUENCE 2.0 model
title_short Improved risk estimation of locoregional recurrence, secondary contralateral tumors and distant metastases in early breast cancer: the INFLUENCE 2.0 model
title_sort improved risk estimation of locoregional recurrence, secondary contralateral tumors and distant metastases in early breast cancer: the influence 2.0 model
topic Epidemiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8505302/
https://www.ncbi.nlm.nih.gov/pubmed/34338943
http://dx.doi.org/10.1007/s10549-021-06335-z
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