External validation of NTCP-models for radiation pneumonitis in lung cancer patients treated with chemoradiotherapy.

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.

An instrument dedicated for modelling of pulmonary radiotherapy.

BACKGROUND AND PURPOSE: Radiotherapy plays a pivotal role in lung cancer treatment. Selection of patients for new (radio)therapeutic options aiming at improving outcomes requires reliable and validated prediction models. We present the implementation of a prospective platform for evaluation and development of lung radiotherapy (proPED-LUNG) as an instrument enabling multidimensional predictive modelling. MATERIALS AND METHODS: ProPED-LUNG was designed to comprise relevant baseline and follow up data of patients receiving pulmonary radiotherapy with curative intent. Patient characteristics, diagnostic and staging information, treatment parameters including full dose-volume-histograms, tumour control, survival, and toxicity are scored. Besides physician-rated data, a range of patient-rated data regarding symptoms and health-related quality-of-life are collected. RESULTS: After 18 months of accrual, 315 patients have been included (accrual rate, 18 per month). Of the first hundred patients included, 70 received conformal (chemo)radiotherapy and 30 underwent stereotactic radiotherapy. Compliance at 3 and 6 months follow-up was 96-100% for patient-rated, and 81-94% for physician-rated assessments. For data collection, 0.4 FTE were allocated in a 183 FTE department (0.2%). CONCLUSIONS: ProPED-LUNG is feasible with high compliance rates and yields a large amount of high quality prospective disease-related, treatment-related, patient- and physician-rated data which can be used to evaluate new developments in pulmonary radiotherapy.