Automated planning for image-guided radiotherapy

Advanced radiotherapy delivery approaches have substantially increased opportunities for sparing organs at risk with proven clinical impact. Ideally, for each individual patient, the treatment plan maximally exploits the full potential of the applied delivery technique. Currently, most treatment plans are generated with interactive trial-and-error planning (‘manual planning’). It is well-known that plan quality in manual planning may be sub-optimal, e.g. depending on experience and ambition of the planner, and on allotted planning time. In recent years, several systems for automated plan generation have been developed, often resulting in enhanced plan quality compared to manual planning. Both in manual and automated planning, human evaluation and judgement of treatment plans is crucial. During plan generation, planners usually develop a range of plans, but generally only one or two competing plans are discussed with the radiation oncologist (RO). A necessary assumption for this process to work well, is that (unknown) disparity between planners and ROs on characteristics of good/optimal plans is absent or minor. Radiotherapy is gradually evolving towards real-time adaptive radiotherapy (ART). ART has the clinical rationale of reducing normal tissue toxicity and improving tumour control through plan adaptation. In this thesis the research in ART was focused on automated methods to standardize ART in predicting the eventual need for re-planning and to assess the goodness of the process. In this thesis the differences between users in perceived quality of plans has been quantified and analysed. Inter-observer differences in plan quality scores were substantial and may result in inconsistencies in generated treatment plans. A method for ART verification, with the ability to quantify registration spatial errors and assess their dose impact at the voxel level, is presented. A systematic workflow to identify effective OAR sparing in re planning using knowledge-based methods, has been established as a step toward an on-line ART process

A tool for radiotherapy plan evaluation analysis: generalise Uniform Ideal Dose (gUIDE)

In radiotherapy, treatment planning is the process in which the appropriate dose distribution is planned for a specific patient. However, there is no consensus on what the ‘optimal’ plan should be and on how to measure plan quality. The purpose of this study was to develop a tool called a ‘generalized Uniform Ideal Dose’ (gUIDE) that produces an ‘ideal’ dose distribution based on single patient anatomy and dose prescription. By comparing the clinical achieved dose distribution with gUIDE a quantitative measure of plan quality can be derived. gUIDE is based on an exponential function of dose fall-off outside the tumor volume. The algorithm does not require any specification of the treatment machine but only patient geometry information. gUIDE fall-off parameter was properly derived in a simple geometry dose profile. Overall, gUIDE showed a lower DVH than the DVH generated using the clinical treatment planning system, as it was expected for a baseline ideal condition. In the clinical validation, although the statistical test showed significant differences between the two groups, overall values were similar for all structures between gUIDE and PlanIQ. A baseline dose gUIDE was implemented, optimised and evaluated. gUIDE could be accurate enough to be used as baseline to help in the plan evaluation process

Variations in head and neck treatment plan quality assessment among radiation oncologists and medical physicists in a single radiotherapy department

Background: Agreement between planners and treating radiation oncologists (ROs) on plan quality criteria is essential for consistent planning. Differences between ROs and planning medical physicists (MPs) in perceived quality of head and neck cancer plans were assessed. Materials and methods: Five ROs and 4 MPs scored 65 plans for in total 15 patients. For each patient, the clinical plan (CLIN) and 2 or 4 alternative plans, generated with automated multi-criterial optimization (MCO), were included. There was always one MCO plan aiming at maximally adhering to clinical plan requirements, while the other MCO plan(s) had a lower aimed quality. Scores were given from 1-7, 1-2: not acceptable, 3-5: acceptable if further planning would not resolve perceived weaknesses, 6-7: straightway acceptable. One MP and one RO repeated plan scoring for intra-observer variation assessment. Results: For the 36 unique observer pairs, the median percentage of plans for which the two observers agreed on plan score (100%=65 plans) was 27.7% [6.2,40.0]. In the repeat scoring, agreements between first and second scoring were 52.3% and 40.0%. With a binary division between unacceptable (scores 1,2) and acceptable (3-7) plans, the median inter-observer agreement percentage was 78.5% [63.1,86.2], while intra-observer agreements were 96.9% and 86.2%. There were no differences in observed agreements between RO-RO, MP-MP and RO-MP pairs. Agreements for the highest-quality, automatically generated MCO plans were higher than for the CLIN plans. Conclusions: Inter-observer differences in plan quality scores were substantial and could result in inconsistencies in generated treatment plans. Agreements among ROs were not better than between ROs and MPs, despite large differences in training and clinical role. High-quality automatically generated plans showed best score agreements

Evaluating the quality of patient-specific deformable image registration in adaptive radiotherapy using a digitally enhanced head and neck phantom

This paper presents a deformable image registration-based method for the quality assurance of head and neck adaptive radiotherapy using digitally post-processed anthropomorphic phantom image datasets. One of the main findings of this work is that spatial and dose errors are a function of the magnitude of the deformation and of the gradient of the dose distribution. This emphasizes the importance of performing patient-specific deformable image registration verification and, consequently, the need to develop and make available tools that are for this purpose

A multi-center output factor intercomparison to uncover systematic inaccuracies in small field dosimetry

Large uncertainties in output factor (OF) small fields dosimetry motivated multicentric studies. The focus of the study was the determination of the OFs, for different linacs and radiosurgery units, using new-generation detectors. Intercomparison studies between radiotherapy centers improved quality dosimetry practices. Results confirmed the effectiveness of the studies to uncover large systematic inaccuracies in small field dosimetry. Keywords: Multicentric studies, Small field dosimetry, Output factor

Understanding Barriers Impacting upon Patient Wellbeing: A Nationwide Italian Survey and Expert Opinion of Dermatologists Treating Patients with Moderate-to-Severe Psoriasis

A nationwide cross-sectional online survey was administered to dermatologists managing patients with moderate-to-severe plaque psoriasis across Italy to obtain real-world dermatologists' perspectives on the impact of psoriasis and its treatment on patients' daily lives and quality of life (QoL). A total of 91 dermatologists (aged 39.1 +/- 11.2 years) completed a 31-question survey and workshop sessions were undertaken in order to identify the best management approach to achieve patient wellbeing. Social (4.2 +/- 0.1), physical (4.26 +/- 0.2) and mental components (4.1 +/- 0.3) were rated by dermatologists as contributing to patient wellbeing to similar extents. While a high proportion (85.4%; rating of 4.3 out of 5) of dermatologists felt that they considered the QoL of patients, a lower proportion (69.6%; rating of 3.7 out of 5) felt that patients were satisfied in this regard. The psoriasis area and severity index and body surface area were the instruments most frequently used to assess the physical domain, while interviews/questions and the dermatology life quality index were used to assess social and mental domains, with only 60% of dermatologists following up on these aspects. The importance of investigating the presence of comorbidities was recognized but not always carried out by many dermatologists, (>70%), particularly for obesity and anxiety/depression. This survey identified key components contributing to barriers impacting on the QoL of patients with moderate-to-severe psoriasis from the perspective of the dermatologist

Use of knowledge based DVH predictions to enhance automated re-planning strategies in head and neck adaptive radiotherapy

This study aimed to investigate if a commercial, knowledge-based tool for radiotherapy planning could be used to estimate potential organs at risk (OARs), which would spare the re-planning strategy for adaptive radiotherapy (ART). Sixty-four head and neck (HN) VMAT Pareto plans from our institute's database were used to train a knowledge-based planning (KBP) model. An evaluation set of 10 HN patients was randomly selected. For each patient in the evaluation set, the planning computed tomography (CT) and 2 sets of on-board cone-beam CT (CBCT), corresponding to the 16th and the 26th radiotherapy treatment fraction, were extracted. The original plan was re-calculated on a daily deformed CT (delivered DVH) and compared with the KBP DVH predictions and with the final KBP DVH after optimisation of the plan, which was performed on the same image sets. To evaluate the feasibility of this method, the range of KBP DVH estimation uncertainties were compared with the gains obtained from re-planning. DVH differences and ROC curve analysis were used for this purpose. On average, KBP uncertainties shared the same order of magnitude as the gain in re-planning. However, statistical tests confirmed significant differences between the two groups (p=0.02). There were statistically significant differences between the predicted and true values (after optimisation) (p<0.01). Overall, for 48% of cases, KBP predicted a desirable outcome from re-planning, and the final dose confirmed an effective gain in 67% of cases. We established a systematic workflow to identify effective OAR sparing in re-planning based on KBP predictions that can be implemented in an on-line, adaptive radiotherapy process. However, inaccuracies in KBP predictions were observed even when Pareto optimal plans were used for model training, which should be the subject of further investigations