TY - JOUR
T1 - External validation of NTCP-models for radiation pneumonitis in lung cancer patients treated with chemoradiotherapy
AU - Niezink, Anne G H
AU - van der Schaaf, Arjen
AU - Wijsman, Robin
AU - Chouvalova, Olga
AU - van der Wekken, Anthonie J
AU - Rutgers, Steven R
AU - Pieterman, Remge M
AU - van Putten, John W G
AU - de Hosson, Sander M
AU - van der Leest, Annija H D
AU - Ubbels, Jan F
AU - Woltman-van Iersel, Marleen
AU - Widder, Joachim
AU - Langendijk, Johannes A
AU - Muijs, Christina T
N1 - Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.
PY - 2023/9
Y1 - 2023/9
N2 - PURPOSE: Normal tissue complication probability (NTCP) models can be used to estimate the risk of radiation pneumonitis (RP). The aim of this study was to externally validate the most frequently used prediction models for RP, i.e., the QUANTEC and APPELT models, in a large cohort of lung cancer patients treated with IMRT or VMAT. [1-2] METHODS AND MATERIALS: This prospective cohort study, included lung cancer patients treated between 2013 and 2018. A closed testing procedure was performed to test the need for model updating. To improve model performance, modification or removal of variables was considered. Performance measures included tests for goodness of fit, discrimination, and calibration.RESULTS: In this cohort of 612 patients, the incidence of RP ≥ grade 2 was 14.5%. For the QUANTEC-model, recalibration was recommended which resulted in a revised intercept and adjusted regression coefficient (from 0.126 to 0.224) of the mean lung dose (MLD),. The APPELT-model needed revision including model updating with modification and elimination of variables. After revision, the New RP-model included the following predictors (and regression coefficients): MLD (B = 0.250), age (B = 0.049, and smoking status (B = 0.902). The discrimination of the updated APPELT-model was higher compared to the recalibrated QUANTEC-model (AUC: 0.79 vs. 0.73).CONCLUSIONS: This study demonstrated that both the QUANTEC- and APPELT-model needed revision. Next to changes of the intercept and regression coefficients, the APPELT model improved further by model updating and performed better than the recalibrated QUANTEC model. This New RP-model is widely applicable containing non-tumour site specific variables, which can easily be collected.
AB - PURPOSE: Normal tissue complication probability (NTCP) models can be used to estimate the risk of radiation pneumonitis (RP). The aim of this study was to externally validate the most frequently used prediction models for RP, i.e., the QUANTEC and APPELT models, in a large cohort of lung cancer patients treated with IMRT or VMAT. [1-2] METHODS AND MATERIALS: This prospective cohort study, included lung cancer patients treated between 2013 and 2018. A closed testing procedure was performed to test the need for model updating. To improve model performance, modification or removal of variables was considered. Performance measures included tests for goodness of fit, discrimination, and calibration.RESULTS: In this cohort of 612 patients, the incidence of RP ≥ grade 2 was 14.5%. For the QUANTEC-model, recalibration was recommended which resulted in a revised intercept and adjusted regression coefficient (from 0.126 to 0.224) of the mean lung dose (MLD),. The APPELT-model needed revision including model updating with modification and elimination of variables. After revision, the New RP-model included the following predictors (and regression coefficients): MLD (B = 0.250), age (B = 0.049, and smoking status (B = 0.902). The discrimination of the updated APPELT-model was higher compared to the recalibrated QUANTEC-model (AUC: 0.79 vs. 0.73).CONCLUSIONS: This study demonstrated that both the QUANTEC- and APPELT-model needed revision. Next to changes of the intercept and regression coefficients, the APPELT model improved further by model updating and performed better than the recalibrated QUANTEC model. This New RP-model is widely applicable containing non-tumour site specific variables, which can easily be collected.
U2 - 10.1016/j.radonc.2023.109735
DO - 10.1016/j.radonc.2023.109735
M3 - Article
C2 - 37327975
SN - 0167-8140
VL - 186
JO - Radiotherapy and Oncology
JF - Radiotherapy and Oncology
M1 - 109735
ER -