Development and validation of an MR-driven dose-of-the-day procedure for online adaptive radiotherapy in upper gastrointestinal cancer patients.

Objective.To develop and validate a dose-of-the-day (DOTD) treatment plan verification procedure for liver and pancreas cancer patients treated with an magnetic resonance (MR)-Linac system.Approach.DOTD was implemented as an automated process that uses 3D datasets collected during treatment delivery. Particularly, the DOTD pipeline's input included the adapt-to-shape (ATS) plan-i.e. 3D-MR dataset acquired at beginning of online session, anatomical contours, dose distribution-and 3D-MR dataset acquired during beam-on (BON). The DOTD automated analysis included (a) ATS-to-BON image intensity-based deformable image registration (DIR), (b) ATS-to-BON contours mapping via DIR, (c) BON-to-ATS contours copying through rigid registration, (d) determining ATS-to-BON dosimetric differences, and (e) PDF report generation. The DIR process was validated by two expert reviewers. ATS-plans were recomputed on BON datasets to assess dose differences. DOTD analysis was performed retrospectively for 75 treatment fractions (12-liver and 5-pancreas patients).Main results.The accuracy of DOTD process relied on DIR and mapped contours quality. Most DIR-generated contours (99.6%) were clinically acceptable. DICE correlated with depreciation of DIR-based region of interest mapping process. The ATS-BON plan difference was found negligible (<1%). The duodenum and large bowel exhibited highest variations, 24% and 39% from fractional values, for 5-fraction liver and pancreas. For liver 1-fraction, a 62% variation was observed for duodenum.Significance.The DOTD methodology provides an automated approach to quantify 3D dosimetric differences between online plans and their delivery. This analysis offers promise as a valuable tool for plan quality assessment and decision-making in the verification stage of the online workflow.

The Radiation Therapist profession through the lens of new technology: A practice development paper based on the ESTRO Radiation Therapist Workshops.

Technological advances in radiation therapy impact on the role and scope of practice of the radiation therapist. The European Society of Radiotherapy and Oncology (ESTRO) recently held two workshops on this topic and this position paper reflects the outcome of this workshop, which included radiation therapists from all global regions. Workflows, quality assurance, research, IGRT and ART as well as clinical decision making are the areas of radiation therapist practice that will be highly influenced by advancing technology in the near future. This position paper captures the opportunities that this will bring to the radiation therapist profession, to the practice of radiation therapy and ultimately to patient care.

A joint ESTRO and AAPM guideline for development, clinical validation and reporting of artificial intelligence models in radiation therapy.

BACKGROUND AND PURPOSE: Artificial Intelligence (AI) models in radiation therapy are being developed with increasing pace. Despite this, the radiation therapy community has not widely adopted these models in clinical practice. A cohesive guideline on how to develop, report and clinically validate AI algorithms might help bridge this gap. METHODS AND MATERIALS: A Delphi process with all co-authors was followed to determine which topics should be addressed in this comprehensive guideline. Separate sections of the guideline, including Statements, were written by subgroups of the authors and discussed with the whole group at several meetings. Statements were formulated and scored as highly recommended or recommended. RESULTS: The following topics were found most relevant: Decision making, image analysis, volume segmentation, treatment planning, patient specific quality assurance of treatment delivery, adaptive treatment, outcome prediction, training, validation and testing of AI model parameters, model availability for others to verify, model quality assurance/updates and upgrades, ethics. Key references were given together with an outlook on current hurdles and possibilities to overcome these. 19 Statements were formulated. CONCLUSION: A cohesive guideline has been written which addresses main topics regarding AI in radiation therapy. It will help to guide development, as well as transparent and consistent reporting and validation of new AI tools and facilitate adoption.

Prospective evaluation of patient-reported anxiety and experiences with adaptive radiation therapy on an MR-linac.

Purpose: An integrated magnetic resonance scanner and linear accelerator (MR-linac) was implemented with daily online adaptive radiation therapy (ART). This study evaluated patient-reported experiences with their overall hospital care as well as treatment in the MR-linac environment. Methods: Patients pre-screened for MR eligibility and claustrophobia were referred to simulation on a 1.5 T MR-linac. Patient-reported experience measures were captured using two validated surveys. The 15-item MR-anxiety questionnaire (MR-AQ) was administered immediately after the first treatment to rate MR-related anxiety and relaxation. The 40-item satisfaction with cancer care questionnaire rating doctors, radiation therapists, the services and care organization and their outpatient experience was administered immediately after the last treatment using five-point Likert responses. Results were analyzed using descriptive statistics. Results: 205 patients were included in this analysis. Multiple sites were treated across the pelvis and abdomen with a median treatment time per fraction of 46 and 66 min respectively. Patients rated MR-related anxiety as "not at all" (87%), "somewhat" (11%), "moderately" (1%) and "very much so" (1%). Positive satisfaction responses ranged from 78 to 100% (median 93%) across all items. All radiation therapist-specific items were rated positively as 96-100%. The five lowest rated items (range 78-85%) were related to general provision of information, coordination, and communication. Overall hospital care was rated positively at 99%. Conclusion: In this large, single-institution prospective cohort, all patients had low MR-related anxiety and completed treatment as planned despite lengthy ART treatments with the MR-linac. Patients overall were highly satisfied with their cancer care involving ART using an MR-linac.

Paediatric radiation therapy without anaesthesia - Are the children moving?

PURPOSE: Children who require radiation therapy (RT) should ideally be treated awake, without anaesthesia, if possible. Audiovisual distraction is a known method to facilitate awake treatment, but its effectiveness at keeping children from moving during treatment is not known. The aim of this study was to evaluate intrafraction movement of children receiving RT while awake. METHODS: In this prospective study, we measured the intrafraction movement of children undergoing treatment with fractionated RT, using pre- and post-RT cone beam CT (CBCT) with image matching on bony anatomy. Study CBCTs were acquired at first fraction, weekly during RT, and at last fraction. The primary endpoint was the magnitude of vector change between the pre- and post-RT scans. Our hypothesis was that 90 % of CBCT acquisitions would have minimal movement, defined as <3 mm for head-and-neck (HN) treatments and <5 mm for non-HN treatments. RESULTS: A total of 65 children were enrolled and had evaluable data across 302 treatments with CBCT acquisitions. Median age was 11 years (range, 2-18; 1st and 3rd quartiles 7 and 14 years, respectively). Minimal movement was observed in 99.4 % of HN treatments and 97.2 % of non-HN treatments. The study hypothesis of >90 % of evaluations having minimal movement was met. Children who were age >11 years moved less at initial evaluation but tended to move more as a course of radiation progressed, as compared to children who were younger. CONCLUSION: Children receiving RT with audiovisual distraction while awake had small magnitudes of observed intrafraction movement, with minimal movement in >97 % of observed RT fractions. This study validates methods of anaesthesia avoidance using audiovisual distraction for selected children.

Deformable image registration for composite planned doses during adaptive radiation therapy.

INTRODUCTION: Some patients have significant anatomic changes during radiotherapy, necessitating an adaptive repeat CT-simulation and re-planning. This yields two unique planning datasets that introduce uncertainty into total dose records. This study explored the impact of using deformable image registration (DIR) to spatially align repeat CT-simulation images and calculate total planned dose distributions. MATERIALS & METHODS: Data from 5 head-and-neck, 5 lung, and 5 sarcoma patients who had unanticipated re-planning during radiotherapy were analyzed in a treatment planning system (RayStation v6.1 RaySearch Laboratories). Total planned doses to normal tissues were calculated using two methods and the previously generated manual contours defined on each CT. The first method, termed 'parameter addition', simply sums the relevant DVH metrics from the initial and re-planned distributions without spatially registering the CTs. The second, termed 'dose accumulation', uses a validated hybrid contour/intensity-based DIR algorithm to deform initial CT and dose distribution onto the repeat CT and re-planning dose distribution. DVH metrics from the summed distribution on the repeat CT are then calculated. Dose differences for organs-at-risk between parameter addition and dose accumulation ≥100 cGy were assumed to be clinically relevant. To elucidate whether relevant differences were due to registration accuracy or contouring variability between CTs, the analysis was repeated using contours on the first CT and the same contours deformed to the repeat CT with DIR. RESULTS: For all patients, high overall DIR accuracy was verified visually (qualitatively) and numerically (quantitatively) using image similarity and contour-based metrics. All head-and-neck and lung patients, and one sarcoma patient (11 of 15 total) had dose differences between parameter addition and dose accumulation ≥100 cGy, with absolute mean differences of 160 cGy (range 101-436 cGy) seen in 41 of 205 total DVH criteria. In 22 of these 41 criteria, these differences were attributed to contouring variability between CTs. After correcting for contouring variations using DIR, the mean absolute differences in 7 of these 22 criteria with a relevant result (across 6 patients) was 146 cGy (range 100-502 cGy). In only 4 DVH criteria, the DIR mapped contours had higher variations than the original contours. One lung patient had a DVH criteria exceeding the clinical dose constraint by 125 cGy with parameter addition, and with accurate DIR and dose accumulation, the criteria was actually 97 cGy lower than the constraint. CONCLUSIONS: The use of DIR to generate total planned dose records revealed substantial dose differences in most cases compared to commonly used clinical methods (i.e. parameter addition), and altered the planned acceptance criteria in a minority. DIR is recommended to be used for future adaptive re-plans to generate total planned dose records and facilitate accurate re-contouring. More accurate dose records may also improve our understanding of clinical outcomes.