TY - JOUR
T1 - Improved risk estimation of locoregional recurrence, secondary contralateral tumors and distant metastases in early breast cancer
T2 - the INFLUENCE 2.0 model
AU - Völkel, Vinzenz
AU - Hueting, Tom A.
AU - Draeger, Teresa
AU - van Maaren, Marissa C.
AU - de Munck, Linda
AU - Strobbe, Luc J.A.
AU - Sonke, Gabe S.
AU - Schmidt, Marjanka K.
AU - van Hezewijk, Marjan
AU - Groothuis-Oudshoorn, Catharina G.M.
AU - Siesling, Sabine
N1 - Funding Information:
Tom Hueting, Teresa Draeger, Marissa C. van Maaren, Linda de Munck, Luc J.A. Strobbe, Gabe S. Sonke, Marjanka K. Schmidt, Marjan van Hezewijk, Catharina GM Groothuis-Oudshoorn, and Sabine Siesling received no funding for the drafting of the manuscript, nor did they receive any form of compensation from any public institution or private company for participating in this project. Vinzenz Voelkel received funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—Project Number 417891978.
Publisher Copyright:
© 2021, The Author(s).
PY - 2021/10
Y1 - 2021/10
N2 - 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.
AB - 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.
KW - UT-Hybrid-D
U2 - 10.1007/s10549-021-06335-z
DO - 10.1007/s10549-021-06335-z
M3 - Article
SN - 0167-6806
VL - 189
SP - 817
EP - 826
JO - Breast cancer research and treatment
JF - Breast cancer research and treatment
ER -
