Cost-effectiveness of proton radiotherapy versus photon radiotherapy for non-small cell lung cancer patients: Exploring the model-based approach.

INTRODUCTION: Proton radiotherapy (PT) is a promising but more expensive strategy than photon radiotherapy (XRT) for the treatment of non-small cell lung cancer (NSCLC). PT is probably not cost-effective for all patients. Therefore, patients can be selected using normal tissue complication probability (NTCP) models with predefined criteria. This study aimed to explore the cost-effectiveness of three treatment strategies for patients with stage III NSCLC: 1. photon radiotherapy for all patients (XRTAll); 2. PT for all patients (PTAll); 3. PT for selected patients (PTIndividualized). METHODS: A decision-analytical model was constructed to estimate and compare costs and QALYs of all strategies. Three radiation-related toxicities were included: dyspnea, dysphagia and cardiotoxicity. Costs and QALY's were incorporated for grade 2 and ≥ 3 toxicities separately. Incremental Cost-Effectiven Ratios (ICERs) were calculated and compared to a threshold value of €80,000. Additionally, scenario, sensitivity and value of information analyses were performed. RESULTS: PTAll yielded most QALYs, but was also most expensive. XRTAll was the least effective and least expensive strategy, and the most cost-effective strategy. For thresholds higher than €163,467 per QALY gained, PTIndividualized was cost-effective. When assuming equal minutes per fraction (15 minutes) for PT and XRT, PTIndividualized was considered the most cost-effective strategy (ICER: €76,299). CONCLUSION: Currently, PT is not cost-effective for all patients, nor for patient selected on the current NTCP models used in the Dutch indication protocol. However, with improved clinical experience, personnel and treatment costs of PT can decrease over time, which potentially leads to PTIndividualized, with optimal patient selection, will becoming a cost-effective strategy.

Radiation-induced oesophagitis in lung cancer patients. Is susceptibility for neutropenia a risk factor?

BACKGROUND: Radiation-induced oesophagitis is a major side effect of concurrent chemotherapy and radiotherapy. A strong association between neutropenia and oesophagitis was previously shown, but external validation and further elucidation of the possible mechanisms are lacking. METHODS AND PATIENTS: A total of 119 patients were included at two institutions. The concurrent group comprised 34 SCLC patients treated with concurrent carboplatin and etoposide, and concurrent chest irradiation, and 36 NSCLC patients with concurrent cisplatin and etoposide, and concurrent radiotherapy, while the sequential group comprised 49 NSCLC patients received sequential cisplatin and gemcitabine, and radiotherapy. RESULTS: Severe neutropenia was very frequent during concurrent chemoradiation (grade: 4 41.4%) and during induction chemotherapy in sequentially treated patients (grade 4: 30.6%), but not during radiotherapy (only 4% grade 1). In the concurrent group, the odds ratios of grade 3 oesophagitis vs. neutropenia were the following: grade 2 vs. grade 0/1: 5.60 (95% CI 1.55-20.26), p = 0.009; grade 3 vs. grade 0/1: 10.40 (95% CI 3.19-33.95); p = 0.0001; grade 4 vs. grade 0/1: 12.60 (95% CI 4.36-36.43); p < 0.00001. There was no correlation between the occurrence of neutropenia during induction chemotherapy and acute oesophagitis during or after radiotherapy alone. In the univariate analysis, total radiation dose (p < 0.001), overall treatment time of radiotherapy (p < 0.001), mean oesophageal dose (p = 0.038) and neutropenia (p < 0.001) were significantly associated with the development of oesophagitis. In a multivariate analysis, only neutropenia remained significant (p = 0.023). CONCLUSION: We confirm that neutropenia is independently correlated with oesophagitis in concurrent chemoradiation, but that the susceptibility for chemotherapy-induced neutropenia is not associated with radiation-induced oesophagitis. Further studies focusing on the underlying mechanisms are thus warranted.

Comparison of Bayesian network and support vector machine models for two-year survival prediction in lung cancer patients treated with radiotherapy.

PURPOSE: Classic statistical and machine learning models such as support vector machines (SVMs) can be used to predict cancer outcome, but often only perform well if all the input variables are known, which is unlikely in the medical domain. Bayesian network (BN) models have a natural ability to reason under uncertainty and might handle missing data better. In this study, the authors hypothesize that a BN model can predict two-year survival in non-small cell lung cancer (NSCLC) patients as accurately as SVM, but will predict survival more accurately when data are missing. METHODS: A BN and SVM model were trained on 322 inoperable NSCLC patients treated with radiotherapy from Maastricht and validated in three independent data sets of 35, 47, and 33 patients from Ghent, Leuven, and Toronto. Missing variables occurred in the data set with only 37, 28, and 24 patients having a complete data set. RESULTS: The BN model structure and parameter learning identified gross tumor volume size, performance status, and number of positive lymph nodes on a PET as prognostic factors for two-year survival. When validated in the full validation set of Ghent, Leuven, and Toronto, the BN model had an AUC of 0.77, 0.72, and 0.70, respectively. A SVM model based on the same variables had an overall worse performance (AUC 0.71, 0.68, and 0.69) especially in the Ghent set, which had the highest percentage of missing the important GTV size data. When only patients with complete data sets were considered, the BN and SVM model performed more alike. CONCLUSIONS: Within the limitations of this study, the hypothesis is supported that BN models are better at handling missing data than SVM models and are therefore more suitable for the medical domain. Future works have to focus on improving the BN performance by including more patients, more variables, and more diversity.