The role of radiotherapy in cancer treatment
Estimating optimal utilization from a review of evidence-based clinical guidelines
Abstract
Radiotherapy utilization rates for cancer vary widely internationally. It has previously been suggested that approximately 50% of all cancer patients should receive radiation. However, this estimate was not evidence-based. The aim of this study was to estimate the ideal proportion of new cases of cancer that should receive radiotherapy at least once during the course of their illness based on the best available evidence. An optimal radiotherapy utilization tree was constructed for each cancer based upon indications for radiotherapy taken from evidence-based treatment guidelines. The proportion of patients with clinical attributes that indicated a possible benefit from radiotherapy was obtained by adding epidemiologic data to the radiotherapy utilization tree. The optimal proportion of patients with cancer that should receive radiotherapy was then calculated using TreeAge (TreeAge Software, Williamstown, MA) software. Sensitivity analyses using univariate analysis and Monte Carlo simulations were performed. The proportion of patients with cancer in whom external beam radiotherapy is indicated according to the best available evidence was calculated to be 52%. Monte Carlo analysis indicated that the 95% confidence limits were from 51.7% to 53.1%. The tightness of the confidence interval suggests that the overall estimate is robust. Comparison with actual radiotherapy utilization data suggests a shortfall in actual radiotherapy delivery. This methodology allows comparison of optimal rates with actual rates to identify areas where improvements in the evidence-based use of radiotherapy can be made. It provides valuable data for radiotherapy service planning. Actual rates need to be addressed to ensure better radiotherapy utilization. Cancer 2005. © 2005 American Cancer Society.
The planning of efficient and equitable treatment services for a population requires a rational and defensible estimate of demand. This has particular relevance for planning services that require significant capital expenditure such as radiotherapy. Radiotherapy is an essential mode of cancer treatment and contributes to the cure or palliation of many cancer patients. Radiotherapy facilities have high capital costs and their operation is staff intensive. In this project, we have undertaken to calculate a rational estimate of need for radiotherapy, based on the occurrence of each type of cancer, the evidence-based indication for radiotherapy in the treatment of each type of cancer, and the probability that radiotherapy will be chosen as a form of treatment.
The radiotherapy utilization rate is defined as the proportion of a defined population of patients with cancer that receives at least one course of radiotherapy during their lifetime. Previous reports from Australian Commonwealth and State agencies have proposed that 50% of all new cases of notifiable cancer in Australia should be treated with external beam radiotherapy.1-5(Notifiable cancers are cancers for which registry data are available.) Although this figure is based almost entirely on expert opinion, it is currently accepted as the guide for estimating utilization and is used to plan for the distribution and number of linear accelerators. However, its validity is questionable, and it is not responsive to changing clinical indications. There are significant variations in actual radiotherapy utilization rates reported in Australia, the United States, Canada, and the Nordic countries, where utilization ranges from 20–55% of all new cancer cases.6-11 These variations stress the importance of using rigorous evidence-based methods to estimate an optimal radiotherapy utilization rate that can act as a benchmark against which actual utilization rates can be compared.
This report estimates an ideal rate of radiotherapy utilization for cancer in Australia based on the incidence of each type of cancer, the evidence-based indication for radiotherapy in the treatment of that cancer, and the proportion of cancer patients included in that indication for radiotherapy.
The authors of the current study have previously published optimal radiotherapy utilization rates for breast carcinoma,12 lung carcinoma,13 melanoma,14 gastrointestinal cancers,15 genitourinary cancers,16 head and neck cancers,17 gynecologic cancers,18, 19 hematologic malignancies,20, 21 central nervous system tumors, unknown primary cancers, and thyroid carcinomas.22 This article reports the estimated overall optimal radiotherapy utilization rate for all registered cancers in Australia and compares the optimal rate with known actual rates of radiotherapy utilization.
Objectives
-
To estimate the ideal proportion of new cases of notifiable cancer that should receive megavoltage external-beam radiotherapy at some time during the course of their illness using the best available evidence.
-
To develop a model of radiotherapy utilization that can be used to estimate the effect of future changes in the relative distribution of tumor sites, changes in stage at presentation, and changes in indications for radiotherapy on the optimal radiotherapy utilization rate.
-
To compare the estimated optimal rates with actual rates of radiotherapy use.
MATERIALS AND METHODS
In this study, an “indication for radiotherapy” was defined as a clinical situation in which radiotherapy was recommended as the treatment of choice on the basis of published evidence that radiotherapy has a superior clinical outcome compared to alternative treatment modalities (including no treatment) and where the patient was suitable to undergo radiotherapy based on an assessment of performance status indicators and the presence or absence of comorbidities. The superiority of radiotherapy over other treatment options could be because of better survival, local control, or toxicity profiles. The study was limited to all notifiable cancers with an incidence of > 1% of the Australian cancer population. Notifiable cancers are cancers for which registry data are available. In Australia this includes ductal carcinoma in situ of the breast but does not include nonmelanomatous skin cancers and benign tumors.
The indications for radiotherapy for each cancer site were derived from treatment guidelines issued by national and international institutions or specialist groups and published (including on the Internet) before December 2003. If guidelines did not exist for particular cancer types and tumor sites, or where the guidelines did not adequately address radiotherapy use, other sources of evidence were identified. These included treatment reviews, randomized controlled trials, population-based studies of care, and single-institution studies.
The evidence for indications for radiotherapy was classified using the Australian National Health and Medical Research Council (NHMRC) hierarchy of levels of evidence (Table 1), with only the highest level of evidence being used for each indication for radiotherapy.23 As our purpose was to make recommendations for radiotherapy services in Australia, the highest priority was given to Australian evidence-based clinical practice guidelines issued by national institutions such as the NHMRC or the National Breast Cancer Centre. If these did not exist, then guidelines from other countries were used wherever possible.
| Level of evidence | Description |
|---|---|
| I | Systematic review of all relevant randomized studies |
| II | At least 1 properly conducted randomized trial |
| III | Well designed controlled trials without randomizationa |
| IV | Case series |
- a These include trials with pseudo-randomization where a flawed randomization method occurred (e.g., alternate allocation of treatments) or comparative studies with either comparative or historical controls.
Radiotherapy utilization trees for individual cancer sites were constructed based upon the treatment recommendations obtained from evidence-based treatment guidelines. We used decision analysis software (TreeAge Data version 3.5, TreeAge Software, Williamstown, MA) to illustrate the indications for radiotherapy in a diagrammatic form (as a tree), to perform basic calculations such as multiplication of factors and summation of the results, and to perform sensitivity analyses of variability. Parameters can be readily adjusted in the tree if indications for radiotherapy or epidemiologic data distributions change in the future and the software can then rapidly calculate the adjusted utilization rates.
The utilization trees depict the clinical conditions for which radiotherapy is indicated. Each terminal branch of the tree shows whether or not radiotherapy is recommended for a particular type of cancer in individuals with specific clinical attributes. In some circumstances, the indication for radiotherapy occurred in the initial stages of management. In other circumstances, radiotherapy was given later in the disease course (for instance, in patients who developed a local recurrence and who had not previously had an indication for treatment with radiotherapy). Similar methodology has been used by others.6, 24-26 This is the first published report of an analysis of all cancers.
The purpose of our project was to determine the proportion of all cancer patients who have at least one indication for radiotherapy at some time in the course of their illness. Patients requiring radiotherapy were therefore counted only once, even if they had multiple indications at different stages in their illness.
The radiotherapy utilization trees also depict the proportion of patients represented by each branch point of the tree. The relative quality of epidemiologic data from various sources was ranked according to a scoring system that gave greatest importance to Australian population-based data. Population-based datasets from other countries were also used. Population-based databases were preferred because they were considered less likely to be affected by referral or selection bias (compared with hospital-based databases) and, therefore, were more likely to be representative of the entire population of patients with cancer. Table 2 shows the hierarchy of quality of epidemiologic data used.
| Quality of source | Source Type |
|---|---|
| α | Australian National Epidemiological data |
| β | Australian State Cancer Registry |
| γ | Epidemiologic databases from other large international groups (e.g. SEER) |
| δ | Results from reports of a random sample from a population |
| ε | Comprehensive multiinstitution database |
| ζ | Comprehensive single-institution database |
| θ | Multiinstitution reports on selected groups (e.g. multiinstitution clinical trials) |
| λ | Single-institution reports on selected groups of cases |
| μ | Expert opinion |
- a Modified from Tyledsley et al.6
The proportion of patients for whom radiotherapy would be recommended was calculated for each cancer site by calculating the frequency of each indication for radiotherapy and then summing the frequencies to give the total optimal rate of use. The overall optimal radiotherapy utilization rate was calculated by summing the optimal utilization rates derived for each cancer site, calculated as a proportion of all cancers.
As this project involved determining estimates for optimal radiotherapy utilization for all notifiable cancers with an incidence of > 1%, the remaining cancers that have an incidence of < 1% have been called “other cancers” in the radiotherapy utilization tree and comprise 2% of the entire cancer population according to the Australian Institute of Health and Welfare report.27 These cancers include pediatric cancers, sarcomas of soft tissue and bone, cancers of the mediastinum, orbit, peritoneum, retroperitoneum, penis, and pleura as well as other rare malignancies. Some of these malignancies are commonly treated with radiotherapy (such as soft tissue sarcomas), and others are rarely treated with radiation (e.g., peritoneal and pleural tumors). For the purpose of the current study, specific radiotherapy utilization trees were not constructed for each of these uncommon cancers. We assumed that the requirement for radiotherapy for this miscellaneous group was 50% and then performed sensitivity analysis where the use of radiotherapy for other cancers ranges between 0% and 100%. This is included in the sensitivity analysis performed for the entire radiotherapy decision tree and is described later.
For some branches of the trees, there was a relative lack of high quality epidemiologic data, and, for some other branches, epidemiologic data differed significantly across different data sources of equal quality. Monte Carlo simulations were performed to assess the impact on the radiotherapy utilization rate that would result from variations in epidemiologic data, different probabilities of benefit from treatment, or uncertainty in the indication for radiotherapy. Monte Carlo simulations are based upon random sampling of variables from discrete and continuous distributions using individual trial data. Observing the statistical properties of many trials using random sampled values allows additional insight into performance of a model. The main weakness of the Monte Carlo analysis, in the current study, is that the relative importance of all of data used is weighted by study size and may not necessarily be ranked by study quality, which was impossible to assess for each dataset.
Funding and Peer Review
The project was funded by the Department of Health and Ageing of the Australian Government and supervised by the National Cancer Control Initiative (NCCI). An expert steering committee was convened for this project by the NCCI with representation from major nongovernmental cancer organizations, consumers, epidemiologists, radiation and medical oncologists, surgeons, palliative care specialists, and experts in evidence and treatment guidelines.
A multidisciplinary panel of expert reviewers was established, comprising ninety-one nationally recognized oncology experts from the fields of medical, surgical, and radiation oncology, palliative care, and oncology nursing. Forty-two of these reviewers provided comments, and 43% of reviewers were from nonradiation oncology specialties also commented.
Comparison with Actual Radiotherapy Utilization Rates
Actual radiotherapy utilization rates were obtained from published and unpublished sources covering the years 1990 to 2001. These actual rates were tabulated and compared with estimated optimal radiotherapy utilization rates.
RESULTS
Recommended optimal radiotherapy utilization rates and optimal radiotherapy utilization trees for breast,12 lung,13 skin (melanoma),14 genitourinary,16 gastrointestinal,15 gynecologic,18, 19 and head and neck cancers,17 hematologic malignancies,20, 21 and central nervous system, thyroid, and unknown primary site tumors22 have been reported in detail elsewhere. A summary of the calculated ideal radiotherapy utilization rates for the various tumor sites are presented in Table 3 along with the proportion of cancer that each tumor site composes in Australia.
| Tumor type | Proportion of all cancers | Proportion of patients receiving radiotherapy | Patients receiving radiotherapy (% of all cancers) | Reference |
|---|---|---|---|---|
| Breast | 0.13 | 83 | 10.8 | Delaney et al.12 |
| Lung | 0.10 | 76 | 7.6 | Delaney et al.13 |
| Melanoma | 0.11 | 23 | 2.5 | Delaney et al.14 |
| Prostate | 0.12 | 60 | 7.2 | Delaney et al.16 |
| Gynecologic | 0.05 | 35 | 1.8 | Delaney et al.18, 19 |
| Colon | 0.09 | 14 | 1.3 | Delaney et al.15 |
| Rectum | 0.05 | 61 | 3.1 | Delaney et al.15 |
| Head and neck | 0.04 | 78 | 3.1 | Delaney et al.17 |
| Gall bladder | 0.01 | 13 | 0.1 | Delaney et al.15 |
| Liver | 0.01 | 0 | 0.0 | Delaney et al.15 |
| Esophageal | 0.01 | 80 | 0.8 | Delaney et al.15 |
| Stomach | 0.02 | 68 | 1.4 | Delaney et al.15 |
| Pancreas | 0.02 | 57 | 1.1 | Delaney et al.15 |
| Lymphoma | 0.04 | 65 | 2.6 | Featherstone et al.20 |
| Leukemia | 0.03 | 4 | 0.1 | Featherstone et al.21 |
| Myeloma | 0.01 | 38 | 0.4 | Featherstone et al.21 |
| Central nervous system | 0.02 | 92 | 1.8 | Delaney et al.22 |
| Renal | 0.03 | 27 | 0.8 | Delaney et al.16 |
| Bladder | 0.03 | 58 | 1.7 | Delaney et al.16 |
| Testis | 0.01 | 49 | 0.5 | Delaney et al.16 |
| Thyroid | 0.01 | 10 | 0.1 | Delaney et al.22 |
| Unknown primary | 0.04 | 61 | 2.4 | Delaney et al.22 |
| Other | 0.02 | 50 | 1.0 | See citations in text |
| Total | 1.00 | - | 52.3 |
Overall Optimal Radiotherapy Utilization Rate
The optimal radiotherapy utilization rates in Table 3 varied from a low recommended rate of 0% for liver cancer patients to a high rate of 92% for patients with central nervous system tumors. The recommended overall optimal radiotherapy utilization rate was calculated to be 52.3%.
Sensitivity Analysis
- 1
Uncertainty in the data where the values of epidemiologic data obtained from multiple sources differed significantly. Typically these were near the terminal ends of the tree where large studies on incidence rates were lacking.
- 2
Uncertainty in the indication for radiotherapy where guidelines had no specific criteria, or conflicting criteria, for consideration of radiotherapy. For example, one guideline for breast cancer recommended radiotherapy for postmastectomy patients with > 3 axillary nodes involved, but also advocated “consideration” of radiotherapy in all patients with nodal involvement.28 Other guidelines either mention that radiotherapy in patients with involvement of less than 4 axillary lymph nodes is controversial29 or avoid the issue completely.30
- 3
Uncertainty in the choice between radiotherapy and other treatment options of equal efficacy, such as surgery, observation, or radiotherapy for localized prostate adenocarcinoma.
The actual branchpoints where these uncertainties existed have been described in reports of each specific cancer site's radiotherapy utilization trees. Sensitivity analysis allows an assessment of the effect that data uncertainty may have on the overall radiotherapy utilization estimate. Two different types of sensitivity analyses were performed. One-way sensitivity analyses allowed assessment of the effect of varying the value of each variable on the overall model in a univariate fashion. One-way sensitivities were presented for each of the decision trees where uncertainty existed and are not repeated here.12-17, 19-22 For a more global multivariate-type assessment, Monte Carlo simulations can be performed to assess the effect of multiple uncertainties on the overall radiotherapy utilization rate. Monte Carlo simulations are based upon random sampling of variables from discrete and continuous distributions using individual trial data. Multivariate sensitivity analysis using Monte Carlo analysis on 104 simulations indicates that the 95% confidence limits for our optimal radiotherapy estimate were 51.7% and 53.1%.
Comparison with Actual Radiotherapy Utilization Rates
Table 4 shows the actual rates of radiotherapy utilization from population-based reports from Sweden, the United Kingdom, the United States and some national and state patterns of care studies in Australia.5, 31-39
| Cancer site | % Optimal radiotherapy utilization rate | Actual radiotherapy utilization rates | |||||||
|---|---|---|---|---|---|---|---|---|---|
| % Sweden National 200131 | % USA | % UK (NYCRIS) 199934 | % Australia | ||||||
| SEER 1995–200032 | ACSa 200133 | National 199535 200036 | NSW 20005 | VIC 200037 199338 | SAa 1990–199439 | ||||
| Breast cancer | 83 | 81 | 42 | 44 | 54 | 41 | 71 | 24 | 40 |
| Lung cancer | 76 | 71 | 39 | 36 | - | - | 49 | 44 | 38 |
| Melanoma | 23 | 23 | 2 | 1 | - | - | 13 | - | 2 |
| Prostate | 60 | 51 | 27 | 41 | 16 | - | - | - | 44 |
| Kidney | 27 | 63 | 8 | 4 | 9 | - | - | - | 11 |
| Urinary bladder | 58 | 17 | 4 | 3 | 26 | - | - | - | 26 |
| Testis | 49 | 48 | 40 | - | NR | - | - | - | 43 |
| Esophagus | 80 | 73 | 54 | - | 31 | - | - | - | 47 |
| Stomach | 68 | 7 | 15 | - | 4 | - | - | - | 6 |
| Pancreas | 57 | 6 | 16 | - | 4 | - | - | - | 4 |
| Liver | 0 | - | 3 | - | 3 | - | - | - | 3 |
| Gall bladder | 13 | 9 | 14 | - | 9 | - | - | - | 5 |
| Colon | 14 | 6 | 2 | 1 | 2 | 3 | - | - | 3 |
| Rectum | 61 | 56 | 40 | 41 | 33 | 38 | - | - | 17 |
| Oral cavity | 74 | 94b | NR | - | - | - | - | - | 44c |
| Lip | 20 | 22 | 8 | - | - | - | - | - | 2 |
| Larynx | 100 | 100 | 75 | - | - | - | - | - | 80 |
| Oropharynx | 100 | 100 | 70 | - | - | - | - | - | - |
| Salivary gland | 87 | 60 | 55 | - | - | - | - | - | - |
| Hypopharynx | 100 | 39 | 74 | - | - | - | - | - | - |
| Paranasal sinuses | 100 | 100 | NR | - | - | - | - | - | - |
| Nasopharynx | 100 | 100 | 84 | - | - | - | - | - | - |
| Unknown primary (head & neck) | 90 | NR | - | - | - | - | - | - | - |
| Uterus | 46 | 64 | 22 | 25 | - | - | - | - | 26 |
| Cervix | 58 | 83 | 44 | 33 | - | - | - | - | 41 |
| Central nervous system | 92 | 37 | 59 | - | - | - | - | - | 52 |
| Lymphoma | 65 | 40 | - | - | - | - | - | - | 24 |
| Leukemia | 4 | 8 | - | - | - | - | - | - | 6 |
| Myeloma | 38 | 82 | - | - | - | - | - | - | 34 |
| All cancers | 52 | 43 | 24 | - | - | - | - | - | 25 |
- NR: Not reported; ACS: American College of Surgeons; SEER: Surveillance, Epidemiology and End Results database (National Cancer Institute); NYCRIS: Northern and Yorkshire Cancer Registry and Information Service; NSW: the state of New South Wales; VIC: the state of Victoria; SA: the state of South Australia.
- a First treatment only.
- b Includes brachytherapy.
- c Includes salivary glands.
DISCUSSION
We have used an evidence-based technique to calculate an overall estimate of optimal radiotherapy utilization of 52.3% for all notifiable cancer in Australia. This final estimate is remarkably precise (as measured by the tight confidence limits) despite uncertainty existing in relation to data for some indications for radiotherapy and occasional uncertainty between treatment options of approximately equal efficacy. The tight confidence interval may be explained by the fact that good quality data existed for the initial branches of the tree (for example, data such as tumor type and stage at presentation). Most of the uncertainty existed in the distal or near-terminal branches of the tree and, therefore, affected only very small proportions of the cancer population and had little effect on the overall estimate. In addition, the effect of these variations was such that some would increase the overall utilization rate whereas others would reduce it, so that, to a large extent, they cancelled out each other.
- 1
It provides a benchmark for planning radiotherapy services on a population basis.The results from this study can be useful in the planning of appropriate radiotherapy services for a given population using the following calculations.
For every 1000 cancer cases in a population, 523 patients would need radiation as an optimal part of their management based upon the results of this project (calculated optimal radiotherapy utilization rate of 52.3%). A further 120 patients, of the above 523 patients, will require retreatment (based upon an actual retreatment rate of 23%).40 This means that an estimated 643 courses of treatment will be required for every 1000 cancer patients diagnosed with a registered cancer. These calculations are summarized in Table 5.
| Percentage | Total no. | |
|---|---|---|
| New registered cancers | N/A | 1000 |
| Patients requiring radiation | 52.3 | 523 |
| Retreatments | 23 | 120 |
| Total number of courses of radiotherapy required | 643 |
- 2
Modeling the effect that changes to a particular cancer incidence or changes in stage distribution have on the overall recommended radiotherapy utilization rate is another benefit.
- 3
This model provides a benchmark for service delivery.
The radiotherapy utilization trees that have been developed for each of the tumor sites are a diagrammatic representation of optimal evidence-based cancer care from a radiotherapy perspective. Epidemiologic data from patterns of care studies will allow comparisons to be made between the actual rates of radiotherapy delivery and the evidence-based ideal rate. Analysis of the distributions of tumor stage, histology, age, performance status, and other factors will better define any discrepancy between the actual and ideal utilization rates.
- 4
This model can determine optimal rates and resources for other treatment modalities.
- 5
This model may be used to predict future radiotherapy workload.
The radiotherapy utilization tree predicts whether patients should receive any radiotherapy but does not assess whether the treatment intent would be palliative or radical, and the tree predicts neither the number of fractions of treatment required nor the complexity of the patient's care. Various models of complexity have been reported in the literature that may be used in future studies so that even more accurate predictions of radiotherapy workload could be determined by calculating the actual number of treatment fractions that may be expected for a given population.
Some Limitations of the Study Were Identified
Quality of data
The current study has identified areas where good quality epidemiologic data (based on stage, performance status, etc.) were lacking. We have overcome the problem by performing modeling and sensitivity analyses to indicate the relatively minor effect that any of these uncertainties could have on overall utilization rate.12-17, 19-22
Skin cancer and benign diseases provide workload for radiation oncology departments but are not included as registered cancers and, therefore, have not been factored into the model
Notifiable cancers are cancers for which statutory requirements exist to notify a state cancer registry. Statutory notification in Australia excludes nonmelanomatous skin cancers and benign tumors but includes ductal carcinoma in situ of the breast. A limitation of the study is that there are other uses for radiotherapy that are not included in this estimate and that will need consideration when planning radiotherapy resources. Radiotherapy has an established role in management of nonmalignant conditions (benign tumors and noncancerous conditions) as well as a role in the management of nonregistered cancers such as nonmelanomatous skin cancers. The overall need for radiotherapy resources is difficult to estimate for these nonregistered conditions, as the overall incidence of these conditions is unknown, and evidence-based treatment guidelines do not exist for most of these conditions. Data obtained from selected hospitals in Australia show that around 11% of patients who receive external beam radiotherapy are treated for nonnotifiable conditions.41 It remains important to consider this additional workload in resource planning.
Other forms of radiotherapy have not been considered
Inclusion of other forms of radiotherapy such as brachytherapy (interstitial and intracavitary) and/or with radioactive isotopes (iodine, yttrium, samarium, strontium, etc.) are beyond the scope of this article. However, these other forms of radiotherapy should be considered when planning radiotherapy resources and could be the subject of further study.
Controversies in the recommended use of radiotherapy
Despite using treatment guidelines to determine indications for radiotherapy, there are many areas where the role of radiotherapy remains poorly defined or where the indications for the use of radiotherapy remain vague. This is mainly due to poor evidence and the lack of good quality trials. We have identified some areas where future research would be useful. The model is easily amended should new evidence for or against the use of radiotherapy for a specific clinical situation emerge.
The effect of patient choice considerations
We did not consider the effect of patient choice because of the risk that the studies reporting patient preference might have been confounded by availability of radiotherapy to the study population. Little or no data are presented in these studies to judge whether access to resources was factored into the decision for or against radiotherapy when alternative treatment options were available.
Rare indications for radiotherapy have not been included in the overall estimate
In many cancers there will be a small proportion of patients who may appropriately receive radiotherapy for rare indications, usually for metastases such as symptomatic lung, soft tissue, or subcutaneous metastases. They were not included because incidence data were not available and the proportions of patients with symptoms that required radiotherapy could not be estimated. Although only of small overall impact in their own right, the cumulative total of these indications could increase the overall radiotherapy utilization estimate by 1–2% at the most.
Conclusions
The overall estimate for radiotherapy utilization is 52.3% based upon the best available evidence. Although the scope of this study is confined to exploring the optimal utilization of external beam megavoltage radiotherapy for notifiable cancers, the overall estimate provides a useful tool for assisting in planning adequate radiotherapy resources. Population-based data from the United States, the United Kingdom, Sweden, and Australia suggest that there is a significant shortfall between the optimal rate and the proportion of patients currently treated with radiotherapy that warrants further research and action.
Acknowledgements
The authors thank the members of the steering committee of the Australian National Cancer Control Initiative and the forty-two reviewers involved in this project for their comments on the study design and decision trees.
