BackgroundWhile intensity modulated radiotherapy (IMRT) has been widely adopted for the treatment of anal cancer (AC), the added contour complexity poses potential risks. This study investigates the impact of contour variation on tumour control probability (TCP) when using IMRT for AC.MethodsNine Australian centres contoured a single computed tomography dataset of a patient with AC. The same optimised template-based IMRT planning protocol was applied to each contour set to generate nine representative treatment plans and their corresponding dose volume histograms. A geometric analysis was performed on all contours. The TCP was calculated for each plan using the linear quadratic and logitEUD model.ResultsThe median concordance index (CI) for the bladder, head and neck of femur, bone marrow, small bowel and external genitalia was 0.94, 0.88, 0.84, 0.65 and 0.65, respectively. The median CI for the involved nodal, primary tumour and elective clinical target volumes were 0.85, 0.77 and 0.71, respectively. Across the nine plans, the TCP was not significantly different. Variation in TCP between plans increased as tumour cell load increased or radiation dose decreased.ConclusionsWhen using IMRT for AC, contour variations generated from a common protocol within the limits of minor deviations do not appear to have a significant impact on TCP. Contouring variations may be more critical with increasing tumour cell load or reducing radiotherapy dose.

Background: RTOG 0933 reported significant benefit in memory preservation and quality of life as compared to historical controls in using hippocampal-avoidance whole brain radiation therapy (HA-WBRT) in the treatment of multiple brain metastases. With the publication of the NRG CC001 randomised trial showing better preservation of cognitive function and patient-reported symptoms with no difference in intracranial progression or overall survival, HA-WBRT with memantine is now established as a new standard of care for treatment of multiple brain metastases. However, the planning aspect is significantly more labourintensive than traditional WBRT. To streamline workflow, we evaluated MRI-atlas based auto-contouring of the hippocampus generated in Elements Treatment Planning System (TPS) compared with manual contouring by three radiation oncologists utilizing the RTOG 0933 Hippocampal Atlas and contours done by a neuroradiologist.Methods: Ten patient datasets were contoured by three radiation oncologists following the RTOG atlas and inter-clinician conformality was assessed using the Dice co-efficient for overlap and Hausdorff maximal and average distances for variability. Auto-contours were generated for the same 10 patient datasets in Elements TPS and compared against all radiation oncologists' contours. The RTOG-based clinician and MRI-atlas based Elements auto-contours were then compared to those of a neuroradiologist's. Results:The manual contours by the radiation oncologists had reasonable conformality with each other with an average Dice co-efficient of 0.766 for both the left and right hippocampi. Hausdorff maximum distance was 4.8 mm for the left hippocampus and 5.2 mm for the right hippocampus. When comparing Elements auto-contours with clinician contours, there was less spatial overlap with a lower average Dice coefficient of 0.537 for the left hippocampus and 0.574 for the right. Average maximum Hausdorff distance was almost double that between clinicians at 9.016 mm for the left and 9.359 mm for the right hippocampus.When compared with neuroradiologist's contours, clinician contours performed better than Elements autocontours numerically with an average DICE co-efficient of 0.655 vs. 0.598 on the left hippocampus and 0.671 vs. 0.632 on the right hippocampus respectively but these differences were not found to be statistically significant.

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Background: Hippocampal-avoidance whole brain radiotherapy (HA-WBRT) has emerged as an approach to retain intracranial tumour control while minimizing cognitive decline. However, the contouring and planning requirements are more complex and time consuming compared to standard WBRT. RapidPlan is an automated treatment planning software that is designed to increase planning efficiency whilst maintaining plan quality. Our group developed an automated HA-WBRT RapidPlan model (Auto) and compared it to a manually optimised standard template (Manual) to assess plan quality and planning efficiency.Methods: A Radiation Oncologist first contoured the hippocampi on 31 patient CT brain data sets fused with MRI with a brain planning target volume (PTV) minus hippocampal avoidance structure, optic chiasm, optic nerve, and lens structures also created. Manual standard template plans were created by an experienced radiation therapist for the first 21 patients with all plans needing to achieve Radiation Therapy Oncology Group (RTOG) 0933 protocol requirements prior to inclusion for creation of the automated RapidPlan model. This Auto model was then tested on a set of 10 separate patients and compared with a Manual plan.The dosimetric parameters and number of optimisations required to achieve protocol requirements were recorded for both.Results: Both the Auto and Manual plans achieved protocol requirements with Auto plans able to achieve these requirements on 1st optimisation for all 10 patients. In contrast, Manual plans were only able to produce acceptable plans in a single optimisation for 5 patients, with 4 patients requiring 2 optimisations and 1 patient requiring 3 optimisations. PTV coverage met RTOG recommendations for all plans but Auto plans were able to achieve lower doses to the organs at risk (OARs) compared to Manual plans, including significantly lower doses to the hippocampi. Independent dose calculation and patient specific dosimetry measurement had a greater than 99% pass rate.Conclusions: A department-created automated HA-WBRT RapidPlan model is feasible and allows for more efficient plan creation with significantly better hippocampal doses compared manually optimised plans and physics check confirming deliverability. Auto plans were able to be created with reduced planning time and resource utilization compared to manual plan creation, allowing for streamlining of workflow and reduced time to treatment for patients.