Technical Innovations and Patient Support in Radiation Oncology最新文献

Purpose: Reirradiation for patients with new, recurrent or metastatic tumors is complex and requires intensive collaboration between Radiation Oncologists, Medical Physicists, and Radiation Therapists (RTT). Aside from dosimetry, little has been reported on the role of the RTT in reirradiation. The study characterized the reirradiation patterns-of-practice of RTTs to understand the knowledge and skills being applied in this increasingly important area of cancer care.

Materials and methods: A cross-sectional, survey was conducted of all RTTs practicing in Canada over a 3-month period. The 48-item questionnaire asked RTTs the frequency of performing a range of reirradiation activities, to self-rate their competency levels, and to identify enablers and barriers to reirradiation practice. The survey was distributed by email and data were analyzed with descriptive statistics or thematic analysis for free-text responses.

Results: Responses from 214 RTTs revealed frequent and significant involvement in all steps of reirradiation pathway, ranging from pre-treatment imaging and positioning to patient supportive care. There was lower involvement in advanced reirradiation dosimetry techniques, which coincided lower competency self-ratings and knowledge gaps in this area. Access to prior patient records, standardized reirradiation workflows and multi-disciplinary communication were the most common elements reported as important for reirradiation practice.

Conclusions: RTT reported frequent and significant involvement in all steps of the reirradiation care pathway. Providing focused education and training for RTTs on reirradiation, coupled with team workflow optimization may enable more effective, safe and streamlined reirradiation care for patients.

Background and purpose: Precise gross tumour volume definition is essential for radiotherapy. Neural networks may improve tumour delineation and reduce manual workload. However, clinical evaluation is crucial for understanding their precision and limitations.

Materials and methods: Two neural network-based models were evaluated for glioblastoma delineation in 70 clinical cases: one developed by Cercare Medical Inc (CMN) and the publicly available Raidionics model. Delineations were compared using Hausdorff 95% (HD95) distance, Dice similarity coefficient (DSC) and the prevalence of false-positive and false-negative volumes. Additionally, interobserver variability between clinicians and the dosimetric consequences of differences in delineation were assessed.

Results: The Raidionics model achieved a mean HD95 of 5.61 mm, with a 5th and 95th percentile range of 2.13-14.8 mm, and a mean DSC of 0.80 [0.62, 0.92]. The CMN model achieved a mean HD95 of 4.24 mm [2.05, 10.2] and mean DSC of 0.83 [0.65, 0.93]. For both metrics the Wilcoxon rank test showed a significant difference (p < 0.002). Both models produced small false-positive volumes, averaging less than 10 % of the true volume. The false-negative volumes averaged around 20 % of the true tumour volume for both models. The HD95 and DSC of interobserver variability were found to be 2.91 mm and 0.89 respectively.

Conclusion: The CMN performed significantly better than the Raidionics model. Both models demonstrated a low occurrence of false-positive delineations and acceptable robustness in preserving dose coverage. However, their performance remained inferior to clinical experts. Further model development is recommended before potential clinical implementation.

Stereotactic ablative body radiotherapy (SABR) is routinely used for the management of oligometastatic disease. Increasingly, there is overlap of targets or organs at risk with previous radiotherapy fields. As substantial variation in delivery of clinical practice exists, the UK SABR Consortium worked with a collaborative national group to develop pelvic SABR re-irradiation consensus guidelines. The scope of the guidance includes patient selection criteria, pre-treatment considerations, delineation guidelines, dose prescription, calculations of cumulative dose constraints, and optimal planning technique. This guidance is part of an ongoing national prospective audit in collaboration with the Royal College of Radiologists and EORTC ReCare.

Treating multiple scalp metastasis in patients is challenging due to the large area that needs to be treated and the complex structure of the scalp. Dose coverage with coplanar fields is hard to optimize with the Halcyon machine's three degrees of freedom (3DoF) couch movement. A potential solution is to use a 3D-printed bolus, which can be designed to fit the scalp contour. This covers more area to improve dose delivery, ensuring that the skin receives the necessary radiation dose while protecting organs at risk (OaR's). A total dose of 39 Gy was delivered to a 71-year-old patient in 13 fractions as a total scalp irradiation (TSI) treatment. The Volumetric Modulated Arc Therapy (VMAT) technique employed four full arcs, which covered the planning target volume (PTV) and ensured optimal dose distribution across the treatment area. A 3D-printed bolus was created using a flexible resin for patient comfort and improved positioning as well as dose delivery. 95% of the PTV received 98.85% of the prescribed dose, with a maximum dose of 107.1% and a conformity index (CI) of 0.95. At the six-month follow-up, the patient showed no signs of scalp metastases, confirming the success of the treatment across the entire scalp. The use of the custom-made, 3D-printed bolus contributed significantly to the treatment success. This study marks the first clinical experience with 3D-printed boluses in our country. Our previous validation study demonstrates that a designed 3D-printed bolus, when integrated into the clinical setup, can provide solution for customizing treatment in cases involving superficial tumors that require good dose distribution.