Quantifying uncertainty in radiotherapy demand at the local and national level using the Malthus model

An Update to the Malthus Model for Radiotherapy Utilisation in England

AIMS

The Malthus Programme predicts national and local radiotherapy demand by combining cancer incidence data with decision trees detailing the indications, and appropriate dose fractionation, for radiotherapy. Since the last model update in 2017, technological advancements and the COVID-19 pandemic have led to increasing hypofractionation of radiotherapy schedules. Indications for radiotherapy have also evolved, particularly in the context of oligometastatic disease. Here we present a brief update on the model for 2021. We have updated the decision trees for breast, prostate, lung and head and neck cancers, and incorporated recent cancer incidence data into our model, generating a current estimate of fraction demand for these four cancer sites across England.

MATERIALS AND METHODS

The decision tree update was based on evidence from practice-changing randomised controlled trials, published guidelines, audit data and expert opinion. Site- and stage-specific incidence data were taken from the National Disease Registration Service. We used the updated model to estimate the proportion of patients who would receive radiotherapy (appropriate rate of radiotherapy) and the fraction demand per million population at a national and Clinical Commissioning Group level in 2021.

RESULTS

The total predicted fraction demand has decreased by 11.4% across all four cancer sites in our new model, compared with the 2017 version. This reduction can be explained primarily by greater use of hypofractionated treatments (including stereotactic ablative radiotherapy) and a shift towards earlier stage presentation. The only large change in appropriate rate of radiotherapy was an absolute decrease of 3% for lung cancer.

CONCLUSIONS

Compared with our previous model, the current version predicts a reduction in fraction demand across England. This is driven principally by hypofractionation of radiotherapy regimens, using technology that requires increasingly complex planning. Treatment complexity and local service factors need to be taken into account when translating fraction burden into linear accelerator demand or throughput.

Analysis and Management of COVID-19 Using Computational Intelligence Technologies

After the outbreak of COVID-19, the world economy and people’s health have been greatly challenged. What is the law of the spread of COVID-19, when will it reach its peak, and when will it be effectively controlled? These have all become major issues of common concern throughout China and the world. Based on this background, this article introduces a variety of classic computational intelligence technologies to predict the spread of COVID-19. Computational intelligence technology mainly includes support vector machine regression (SVR), Takagi-Sugeuo-Kang fuzzy system (TSK-FS), and extreme learning machine (ELM). Compare the predictions of the infection rate, mortality rate, and recovery rate of the COVID-19 epidemic in China by each intelligent model in 5 and 10 days, the effectiveness of the computational intelligence algorithm used in epidemic prediction is verified. Based on the prediction results, the patients are classified and managed. According to the time of illness, physical fitness and other factors, patients are divided into three categories: Severe, moderate, and mild. In the case of serious shortage of medical equipment and medical staff, auxiliary medical institutions take corresponding treatment measures for different patients.

Variations in Demand across England for the Magnetic Resonance-Linac Technology, Simulated Utilising Local-level Demographic and Cancer Data in the Malthus Project

AIMS

Cancer incidence varies across England, which affects the local-level demand for treatments. The magnetic resonance-linac (MR-linac) is a new radiotherapy technology that combines imaging and treatment. Here we model the demand and demand variations for the MR-linac across England.

MATERIALS AND METHODS

Initial clinical indications were provided by the MR-linac consortium and introduced into the Malthus radiotherapy clinical decision trees. The Malthus model contains Clinical Commissioning Group (CCG) population, cancer incidence and stage presentation data (for lung and prostate) and simulated the demand for the MR-linac for all CCGs and Radiotherapy Operational Delivery Networks (RODN) across England.

RESULTS

Based on the initial target clinical indications, the MR-linac could service 16% of England's fraction burden. The simulated fractions/million population demand/annum varies between 3000 and 10 600 fractions/million at the CCG level. Focussing only on the cancer population, the simulated fractions/1000 cancer cases demand/annum ranges from 1028 to 1195 fractions/1000 cases. If a national average for fractions/million demand was then used, at the RODN level, the variation from actual annual demand ranges from an overestimation of 8400 fractions to an underestimation of 5800 fractions. When using the national average fractions/1000 cases, the RODN demand varies from an overestimation of 3200 fractions to an underestimation of 3000 fractions.

CONCLUSIONS

Planning cancer services is complex due to regional variations in cancer burden. The variations in simulated demand of the MR-linac highlight the requirement to use local-level data when planning to introduce a new technology.

A population perspective on the use of external beam radiotherapy in Catalonia, Spain

PURPOSE

To assess the use of external beam radiotherapy in Catalonia (Spain), overall and by health management area.

METHODS

We assessed radiotherapy treatments in a cohort of patients diagnosed with cancer from 2009 to 2011, using the population-based cancer registries in Girona and Tarragona. Participants had to have a minimum follow-up of 5 years from the time the cancer registry database was linked to the catalan health service database for financing radiation oncology. Outcomes included the proportion of patients receiving radiotherapy within 1 and 5 years of diagnosis. A log-binomial model was used to assess age-related trends in the use of radiotherapy by tumour site. Finally, we calculated the standardized utilization rate and 95% confidence intervals by health management area covered by the radiation oncology services, using indirect methods.

RESULTS

At 1 and 5 years from diagnosis, 21.4 and 24.4% of patients, respectively, had received external beam radiotherapy. Patients aged 40-64 years had the most indications for the treatment, and there was a negative correlation between the patients' age and the use of radiotherapy for most tumour sites (exceptions were cervical, thyroid, and uterine cancers). There were no statistically significant differences in the use of radiotherapy according to th health management area.

CONCLUSIONS

Population-based data show that external beam radiotherapy is underutilized in Catalonia. This situation requires a careful analysis to understand the causes, as well as an improvement of the available resources, oriented toward achieving realistic targets for the optimal use of external beam radiotherapy in our country.

Mechanistic modelling supports entwined rather than exclusively competitive DNA double-strand break repair pathway

Following radiation induced DNA damage, several repair pathways are activated to help preserve genome integrity. Double Strand Breaks (DSBs), which are highly toxic, have specified repair pathways to address them. The main repair pathways used to resolve DSBs are Non-Homologous End Joining (NHEJ) and Homologous Recombination (HR). Cell cycle phase determines the availability of HR, but the repair choice between pathways in the G2 phases where both HR and NHEJ can operate is not clearly understood. This study compares several in silico models of repair choice to experimental data published in the literature, each model representing a different possible scenario describing how repair choice takes place. Competitive only scenarios, where initial protein recruitment determines repair choice, are unable to fit the literature data. In contrast, the scenario which uses a more entwined relationship between NHEJ and HR, incorporating protein co-localisation and RNF138-dependent removal of the Ku/DNA-PK complex, is better able to predict levels of repair similar to the experimental data. Furthermore, this study concludes that co-localisation of the Mre11-Rad50-Nbs1 (MRN) complexes, with initial NHEJ proteins must be modeled to accurately depict repair choice.

Mathematical Modelling for Patient Selection in Proton Therapy

Proton beam therapy (PBT) is still relatively new in cancer treatment and the clinical evidence base is relatively sparse. Mathematical modelling offers assistance when selecting patients for PBT and predicting the demand for service. Discrete event simulation, normal tissue complication probability, quality-adjusted life-years and Markov Chain models are all mathematical and statistical modelling techniques currently used but none is dominant. As new evidence and outcome data become available from PBT, comprehensive models will emerge that are less dependent on the specific technologies of radiotherapy planning and delivery.

Using the Malthus programme to predict the recruitment of patients to MR-linac research trials in prostate and lung cancer

In this study, we used evidence-based mathematical modelling to predict the patient cohort for MR-linac to assess its feasibility in a time of austerity. We discuss our results and the implications of evidence-based radiotherapy demand modelling tools such as Malthus on the implementation of new technology and value-based healthcare.

A Population-based Model of Local Control and Survival Benefit of Radiotherapy for Lung Cancer

AIMS

To estimate the population-based locoregional control and overall survival benefits of radiotherapy for lung cancer if the whole population were treated according to evidence-based guidelines. These estimates were based on a published radiotherapy utilisation (RTU) model that has been used to estimate the demand and planning of radiotherapy services nationally and internationally.

MATERIALS AND METHODS

The lung cancer RTU model was extended to incorporate an estimate of benefits of radiotherapy alone, and of radiotherapy in conjunction with concurrent chemotherapy (CRT). Benefits were defined as the proportional gains in locoregional control and overall survival from radiotherapy over no radiotherapy for radical indications, and from postoperative radiotherapy over surgery alone for adjuvant indications. A literature review (1990-2015) was conducted to identify benefit estimates of individual radiotherapy indications and summed to estimate the population-based gains for these outcomes. Model robustness was tested through univariate and multivariate sensitivity analyses.

RESULTS

If evidence-based radiotherapy recommendations are followed for the whole lung cancer population, the model estimated that radiotherapy alone would result in a gain of 8.3% (95% confidence interval 7.4-9.2%) in 5 year locoregional control, 11.4% (10.8-12.0%) in 2 year overall survival and 4.0% (3.6-4.4%) in 5 year overall survival. For the use of CRT over radiotherapy alone, estimated benefits would be: locoregional control 1.7% (0.8-2.4%), 2 year overall survival 1.7% (0.5-2.8%) and 5 year overall survival 1.2% (0.7-1.9%).

CONCLUSIONS

The model provided estimates of radiotherapy benefit that could be achieved if treatment guidelines are followed for all cancer patients. These can be used as a benchmark so that the effects of a shortfall in the utilisation of radiotherapy can be better understood and addressed. The model can be adapted to other populations with known epidemiological parameters to ensure the planning of equitable radiotherapy services.