Decision analytic modeling for the economic analysis of proton radiotherapy for non-small cell lung cancer

Health Economic Evaluation of Proton Therapy for Lung Cancer: A Systematic Review

BACKGROUND

To our knowledge, there have been no systematic reviews of health economic evaluations of proton therapy specific to lung cancer.

METHODS

We conducted this systematic review according to the predefined protocol [PROSPERO CRD42022365869]. We summarized the results of the included studies via structured narrative synthesis.

RESULTS

We identified four studies (all used passively scattered proton therapy) from 787 searches. Two cost analyses reported that proton therapy was more costly than photon therapy for early- or locally advanced-stage non-small cell lung cancer, one cost-utility analysis reported that proton therapy was dominated by nonproton therapy in early-stage non-small cell lung cancer, and one cost-utility analysis reported that proton therapy was not cost-effective (vs. photon) in locally advanced non-small cell lung cancer.

CONCLUSIONS

Passively scattered proton therapy was more costly and not cost-effective than photon therapy for early- and locally advanced-stage non-small cell lung cancer. Further health economic evaluations regarding modern proton therapy (such as scanning beam) for common radiotherapy indications of lung cancer are eagerly awaited.

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 (XRT_All); 2. PT for all patients (PT_All); 3. PT for selected patients (PT_{Individualized}).

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

PT_All yielded most QALYs, but was also most expensive. XRT_All was the least effective and least expensive strategy, and the most cost-effective strategy. For thresholds higher than €163,467 per QALY gained, PT_{Individualized} was cost-effective. When assuming equal minutes per fraction (15 minutes) for PT and XRT, PT_{Individualized} 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 PT_{Individualized}, with optimal patient selection, will becoming a cost-effective strategy.

Clinical Outcomes After Proton Beam Therapy for Locally Advanced Non-Small Cell Lung Cancer: Analysis of a Multi-institutional Prospective Registry

Purpose

For most disease sites, level 1 evidence is lacking for proton beam therapy (PBT). By identifying target populations that would benefit most from PBT, prospective registries could overcome many of the challenges in clinical trial enrollment. Herein, we report clinical outcomes of patients treated with PBT for locally advanced non-small cell lung cancer (LA-NSCLC).

Methods and Materials

Data were obtained from the multi-institutional prospective database of the Proton Collaborative Group (PCG). Inclusion criteria of our study were stage III de novo or recurrent LA-NSCLC, use of PBT, and availability of follow-up data. Overall survival (OS) time was calculated from the start of treatment until death or last follow-up. Kaplan-Meier curves were generated for groups of interest and compared with log-rank tests. Cox regression modeling was used to evaluate the multivariate association between selected covariates and OS.

Results

A total of 195 patients were included in the analysis. PBT was given with a median equivalent dose in 2 Gy fractions (EQD2) of 63.8 Gy (relative biological effectiveness). Pencil beam scanning was used in 20% of treatments. Treatment-related grade 3 adverse events were rare: 1 pneumonitis, 2 dermatitis, and 3 esophagitis. No grade 4 events were reported. Two cardiac-related grade 5 events occurred in patients with multiple risk factors. The median follow-up time for living patients was 37.1 months and the median OS was 19.0 months. On multivariate analysis, good performance status (hazard ratio, 0.27; [95% confidence interval, 0.15-0.46]; P < .0001), pencil beam scanning use (0.55; [0.31-0.97]; P = .04), and increased EQD2 (0.80; [0.71-0.90] - per 10 Gy increase; P = .0002) were associated with decreased mortality.

Conclusions

PBT appears to yield low rates of adverse events with an OS similar to other retrospective studies on PBT for LA-NSCLC. PBS use and increased EQD2 can potentially improve OS.

Dosimetry, Efficacy, Safety, and Cost-Effectiveness of Proton Therapy for Non-Small Cell Lung Cancer

Non-small cell lung cancer (NSCLC) is the most common malignancy which requires radiotherapy (RT) as an important part of its multimodality treatment. With the advent of the novel irradiation technique, the clinical outcome of NSCLC patients who receive RT has been dramatically improved. The emergence of proton therapy, which allows for a sharper dose of build-up and drop-off compared to photon therapy, has potentially improved clinical outcomes of NSCLC. Dosimetry studies have indicated that proton therapy can significantly reduce the doses for normal organs, especially the lung, heart, and esophagus while maintaining similar robust target volume coverage in both early and advanced NSCLC compared with photon therapy. However, to date, most studies have been single-arm and concluded no significant changes in the efficacy for early-stage NSCLC by proton therapy over stereotactic body radiation therapy (SBRT). The results of proton therapy for advanced NSCLC in these studies were promising, with improved clinical outcomes and reduced toxicities compared with historical photon therapy data. However, these studies were also mainly single-arm and lacked a direct comparison between the two therapies. Currently, there is much emerging evidence focusing on dosimetry, efficacy, safety, and cost-effectiveness of proton therapy for NSCLC that has been published, however, a comprehensive review comparing these therapies is, to date, lacking. Thus, this review focuses on these aspects of proton therapy for NSCLC.

A scoping review of patient selection methods for proton therapy

The aim was to explore various national and international clinical decision‐making tools and dose comparison methods used for selecting cancer patients for proton versus X‐ray radiation therapy. To address this aim, a literature search using defined scoping review methods was performed in Medline and Embase databases as well as grey literature. Articles published between 1 January 2015 and 4 August 2020 and those that clearly stated methods of proton versus X‐ray therapy patient selection and those published in English were eligible for inclusion. In total, 321 studies were identified of which 49 articles met the study’s inclusion criteria representing 13 countries. Six different clinical decision‐making tools and 14 dose comparison methods were identified, demonstrating variability within countries and internationally. Proton therapy was indicated for all paediatric patients except those with lymphoma and re‐irradiation where individualised model‐based selection was required. The most commonly reported patient selection tools included the Normal Tissue Complication Probability model, followed by cost‐effectiveness modelling and dosimetry comparison. Model‐based selection methods were most commonly applied for head and neck clinical indications in adult cohorts (48% of studies). While no ‘Gold Standard’ currently exists for proton therapy patient selection with variations evidenced globally, some of the patient selection methods identified in this review can be used to inform future practice in Australia. As literature was not identified from all countries where proton therapy centres are available, further research is needed to evaluate patient selection methods in these jurisdictions for a comprehensive overview.

Clinical Outcomes of Pencil Beam Scanning Proton Therapy in Locally Advanced Non-Small Cell Lung Cancer: Propensity Score Analysis

This study compared the efficacy and safety of pencil beam scanning proton therapy (PBSPT) versus intensity-modulated (photon) radiotherapy (IMRT) in patients with stage III non-small cell lung cancer (NSCLC). We retrospectively reviewed 219 patients with stage III NSCLC who received definitive concurrent chemoradiotherapy between November 2016 and December 2018. Twenty-five patients (11.4%) underwent PBSPT (23 with single-field optimization) and 194 patients (88.6%) underwent IMRT. Rates of locoregional control (LRC), overall survival, and acute/late toxicities were compared between the groups using propensity score-adjusted analyses. Patients treated with PBSPT were older (median: 67 vs. 62 years) and had worse pulmonary function at baseline (both FEV1 and DLCO) compared to those treated with IMRT. With comparable target coverage, PBSPT exhibited superior sparing of the lung, heart, and spinal cord to radiation exposure compared to IMRT. At a median follow-up of 21.7 (interquartile range: 16.8-26.8) months, the 2-year LRC rates were 72.1% and 84.1% in the IMRT and PBSPT groups, respectively (p = 0.287). The rates of grade ≥ 3 esophagitis were 8.2% and 20.0% after IMRT and PBSPT (p = 0.073), respectively, while corresponding rates of grade ≥ 2 radiation pneumonitis were 28.9% and 16.0%, respectively (p = 0.263). PBSPT appears to be an effective and safe treatment technique even for patients with poor lung function, and it does not jeopardize LRC.

Medical expenditure estimation by Bayesian network for lung cancer patients at different severity stages

Lung cancer is one of the leading causes of mortality, and its medical expenditure has increased dramatically. Estimating the expenditure for this disease has become an urgent concern of the supporting families, medial institutes, and government. In this study, a conditional Gaussian Bayesian network (CGBN) model was developed to incorporate the comprehensive risk factors to estimate the medical expenditure of a lung cancer patient at different stages. A total of 961 patients were surveyed by the four severity stages of lung cancer. The proposed CGBN model identified the correlation and association of 15 risk factors to the medical expenditure of different severity stages of lung cancer patients. The relationships among the demographic, diagnosed-based, and prior-utilization variables are constructed. The model predicted the lung cancer-related medical expenditure with high accuracy of 32.63%, 50.30%, 50.36%, and 66.58%, respectively for stages 1-4, as compared with the reported models. A greedy search was also applied to find the upper threshold of R², while our model also shows that it approached the upper threshold.

Performance/outcomes data and physician process challenges for practical big data efforts in radiation oncology

It is an exciting time for big data efforts in radiation oncology. The use of big data to help aid both outcomes and decision-making research is becoming a reality. However, there are true challenges that exist in the space of gathering and utilizing performance and outcomes data. Here, we summarize the current state of big data in radiation oncology with respect to outcomes and discuss some of the efforts and challenges in radiation oncology big data.