The EPTN consensus-based atlas for CT- and MR-based contouring in neuro-oncology

Biological, physical and clinical aspects of cancer treatment with ionising radiatio

The relationship between cellular radiation sensitivity and normal tissue response to radiotherapy Prospects for individualising radiotherapy prescriptions

SIGLEAvailable from British Library Document Supply Centre- DSC:D61316 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Mathematical modelling of survival of glioblastoma patients suggests a role for radiotherapy dose escalation and predicts poorer outcome after delay to start treatment

Aims: The outcome of patients with glioblastoma (GBM) remains extremely poor. We have developed a mathematical model, using pathological and radiation biology concepts, to assess the detrimental effect of delay to start radiotherapy, the possible benefit from dose escalation, and to extract biological data from clinical data.Materials and methods: Survival data were available for 154 adult patients with GBM treated in our centre with curative intent to a dose of 60 Gy in 30 fractions between 1996 and 2002. Survival data for 129 patients from the 60 Gy arm of the MRC BR02 randomised trial of radiotherapy dose were obtained for comparison. The model generates the equivalent of individual patients with a brain tumour, and produces an explicit outcome, either death or survival. The tumour assumed to be growing exponentially, causes normal cell damage in the brain, and death occurs when the number of normal brain cells falls below a critical level. The outcome for an individual patient is determined by values of the variables assigned by the model. Parameters for the single patient include tumour doubling time, surviving fraction of tumour cells after each fraction of radiotherapy, and a waiting time from presentation to the start of radiotherapy. A surrogate for performance status is implemented, using a rule that rejects patients whose tumours are too advanced at presentation to be suitable for radical radiotherapy. Values for the parameters that determine individual patient outcome are randomly assigned from a set of probability distributions, using Monte Carlo simulation. The simulation constructs survival results for a population, typically 2000 individuals. The descriptors of the probability distributions that are used to determine the parameters that define the patient characteristics are adjusted to optimise the fit of the modelled population to real clinical data, using a combination of folding polygon and simulated annealing techniques.Results: The model fits the clinical data well. The results suggest that the surviving fraction of tumour cells after a radiation dose of 2 Gy (SF2) does influence patient outcome. The mean in vivo SF2 for the Addenbrooke's data is 0.80, implying that hypoxia is a serious problem in radiotherapy for GBM. The Addenbrooke's data suggest a mean tumour doubling time of 24 days, so that a delay to start radiotherapy would be expected to have an adverse effect. Considering patients by treatment intent, median survival plummets as delay increases, and almost no patients survive long term after a 70-day delay. Radiotherapy dose escalation has an important predicted effect on survival. Assuming that the treatment could be delivered safely, a dose of 74 Gy, given at 2 Gy/fraction, would extend the survival of all patients. The proportion of long-term survivors would increase, from 2.4% with 60 Gy. to 6.4% with 74 Gy. The model can be used to derive gamma(50), which has a value of 0.42, lower than the typical value of 1-2.Conclusion: Using the model, we have extracted biological information from clinical data. The model could be used to assess the potential benefit, or lack of benefit, from a proposed radiotherapy trial, and to estimate the necessary size. It shows that a single modality is unlikely to achieve a major improvement in long-term survival, although radiotherapy dose escalation should have a role, provided it can be given safely. The model could be extended to include chemotherapy, bio-reductive drugs, or gene therapy

The use of radiotherapy, surgery and chemotherapy in the curative treatment of cancer: results from the FORTY (Favourable Outcomes from RadioTherapY) project

Objectives Radiotherapy, surgery and chemotherapy play key roles in the curative treatment of cancer, alone and in combination. Quantifying their roles is essential for equipment provision and workforce planning. The estimate that 40% of cancer patients are cured by RT has been used extensively to inform and influence policy but is relatively old and warrants&nbsp;review. Methods Patient, tumour and treatment event data was obtained for the 5 year period from 2009 to 2013, allowing a further 5 years for survival outcomes to be known. We analysed patient-level data on utilisation of surgery, radiotherapy, and chemotherapy in cancer patients in England. Data were sourced from Public Health England, using National Cancer Registrations, the National Radiotherapy Dataset (RTDS) and the Systemic Anti-Cancer Therapy Dataset (SACT). All tumour sites (excluding C44) and ages were included. We analysed three cohorts: all patients [n = 1,029,569], patients who survived 5 years or more [n = 537,970] and patients who survived &lt;5 years [n =&nbsp;491,599]. Results Overall cancer-specific 5-year survival was 52%, and in those patients, surgery was the most common curative treatment, with 80% receiving surgery, alone or in combination; radiotherapy was delivered to 39% and chemotherapy to 29%; 45% received two and 13% all three&nbsp;modalities. Conclusions The high proportion receiving multimodality treatment emphasises the importance of integrated, resourced, multidisciplinary cancer care. Radiotherapy was delivered to almost 40% of patients who survived 5 years which underlines its importance in cancer management. Advances in knowledge The results are essential in planning cancer services. They also inform the public health&nbsp;narrative.</p

A novel algorithm for the morphometric assessment of radiotherapy treatment planning volumes

Quantitative assessment of target volume contouring in radiotherapy treatment planning is an important aspect of quality assessment and educational exercises. The Conformity Index (CI) is a volume-based statistic frequently used for this purpose. Although the CI is relatively simple to understand and can be calculated using most treatment planning systems, it does not provide any information on the differences in shape between the two volumes. We present a new morphometric (shape-based) statistic known as the “mean distance to conformity” (MDC). For a specific volume that is being evaluated against a reference volume, the MDC represents the average distance that all outlying points in the volume must be moved in order to achieve perfect conformity with the reference volume. The MDC comprises a component related to under-contouring (where the evaluation volume is smaller than the reference volume) and a component related to over-contouring (where the evaluation extends beyond the reference volume). Furthermore, voxel-by-voxel information on conformity errors can also be displayed using a volume–error histogram. Calculation of MDC statistics is achieved using a three-dimensional grid search algorithm. By using a range of scenarios comprising both theoretical and actual clinical volumes, we demonstrate the increased utility of the MDC for the detection of contouring errors