TY - JOUR
T1 - Prognostic models in male breast cancer
AU - van der Pol, Carmen C.
AU - Lacle, Miangela M.
AU - Witkamp, Arjen J.
AU - Kornegoor, Robert
AU - Miao, Hui
AU - Bouchardy, Christine
AU - Borel Rinkes, Inne
AU - van der Wall, Elsken
AU - Verkooijen, Helena M.
AU - van Diest, Paul J.
N1 - Publisher Copyright:
© 2016, The Author(s).
PY - 2016/11/1
Y1 - 2016/11/1
N2 - Purpose: Breast cancer in men is uncommon; it accounts for 1 % of all patients with primary breast cancer. Its treatment is mostly extrapolated from its female counterpart. Accurate predictions are essential for adjuvant systemic treatment decision-making and informing patients. Several predictive models are available for female breast cancer (FBC) including the Morphometric Prognostic Index (MPI), Nottingham Prognostic Index (NPI), Adjuvant! Online and Predict. The aim of this study was to examine and compare the prognostic performance of these models for male breast cancer (MBC). Methods: The population of this study consists of 166 MBC patients. The prognostic scores of the patients are categorized by good, (moderate) and poor, defined by the test itself (MPI and NPI) or based on tertiles (Adjuvant! Online and Predict). Survival according to prognostic score was compared by Kaplan–Meier analysis and differences were tested by logRank. The prognostic performances were evaluated with C-statistics. Calibration was done with the aim to estimate to what extent the survival rates predicted by Predict were similar to the observed survival rates. Results: All prediction models were capable of discriminating between good, moderate and poor survivors. P-values were highly significant. Comparison between the models using C-statistics (n = 88) showed equal performance of MPI (0.67), NPI (0.68), Adjuvant! Online (0.69) and Predict (0.69). Calibration of Predict showed overestimation for MBC patients. Conclusion: In conclusion, MPI, NPI, Adjuvant! and Predict prognostic models, originally developed and validated for FBC patients, also perform quite well for MBC patients.
AB - Purpose: Breast cancer in men is uncommon; it accounts for 1 % of all patients with primary breast cancer. Its treatment is mostly extrapolated from its female counterpart. Accurate predictions are essential for adjuvant systemic treatment decision-making and informing patients. Several predictive models are available for female breast cancer (FBC) including the Morphometric Prognostic Index (MPI), Nottingham Prognostic Index (NPI), Adjuvant! Online and Predict. The aim of this study was to examine and compare the prognostic performance of these models for male breast cancer (MBC). Methods: The population of this study consists of 166 MBC patients. The prognostic scores of the patients are categorized by good, (moderate) and poor, defined by the test itself (MPI and NPI) or based on tertiles (Adjuvant! Online and Predict). Survival according to prognostic score was compared by Kaplan–Meier analysis and differences were tested by logRank. The prognostic performances were evaluated with C-statistics. Calibration was done with the aim to estimate to what extent the survival rates predicted by Predict were similar to the observed survival rates. Results: All prediction models were capable of discriminating between good, moderate and poor survivors. P-values were highly significant. Comparison between the models using C-statistics (n = 88) showed equal performance of MPI (0.67), NPI (0.68), Adjuvant! Online (0.69) and Predict (0.69). Calibration of Predict showed overestimation for MBC patients. Conclusion: In conclusion, MPI, NPI, Adjuvant! and Predict prognostic models, originally developed and validated for FBC patients, also perform quite well for MBC patients.
KW - Adjuvant! Online
KW - Male breast cancer
KW - NPI
KW - Predict
KW - Prognosis
KW - Survival
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U2 - 10.1007/s10549-016-3991-9
DO - 10.1007/s10549-016-3991-9
M3 - Article
C2 - 27671991
AN - SCOPUS:84991290208
SN - 0167-6806
VL - 160
SP - 339
EP - 346
JO - Breast Cancer Research and Treatment
JF - Breast Cancer Research and Treatment
IS - 2
ER -