Evidence-based optimal number of radiotherapy fractions for cancer: A useful tool to estimate radiotherapy demand. Issue 1 (April 2016)

• Record Type: Journal Article
• Authors: Wong, Karen; Delaney, Geoff P.; Barton, Michael B.
• Is Part Of: Radiotherapy and oncology. Volume 119:Issue 1(2016:Apr.)
• Page Start: 145
• Page End: 149
• Publication Date: 2016-04
• Subjects: Optimal -- Radiotherapy -- Fractions -- Cancer; Oncology -- Periodicals; Radiotherapy -- Periodicals; Tumors -- Periodicals; Medical Oncology -- Periodicals; Neoplasms -- radiotherapy -- Periodicals; Radiotherapy -- Periodicals; Radiothérapie -- Périodiques; Cancérologie -- Périodiques; Tumeurs -- Périodiques; Electronic journals; 616.9940642
• DOI: 10.1016/j.radonc.2015.12.001
• Languages: English
• ISSNs: 0167-8140

Abstract

Abstract: Background and purpose: The recently updated optimal radiotherapy utilisation model estimated that 48.3% of all cancer patients should receive external beam radiotherapy at least once during their disease course. Adapting this model, we constructed an evidence-based model to estimate the optimal number of fractions for notifiable cancers in Australia to determine equipment and workload implications. Materials and methods: The optimal number of fractions was calculated based on the frequency of specific clinical conditions where radiotherapy is indicated and the evidence-based recommended number of fractions for each condition. Sensitivity analysis was performed to assess the impact of variables on the model. Results: Of the 27 cancer sites, the optimal number of fractions for the first course of radiotherapy ranged from 0 to 23.3 per cancer patient, and 1.5 to 29.1 per treatment course. Brain, prostate and head and neck cancers had the highest average number of fractions per course. Overall, the optimal number of fractions was 9.4 per cancer patient (range 8.7–10.0) and 19.4 per course (range 18.0–20.7). Conclusions: These results provide valuable data for radiotherapy services planning and comparison with actual practice. The model can be easily adapted by inserting population-specific epidemiological data thus making it applicable to other jurisdictions.