Clinical adoption patterns of 0.35 Tesla MR-guided radiation therapy in Europe and Asia

BACKGROUND Magnetic resonance-guided radiotherapy (MRgRT) utilization is rapidly expanding, driven by advanced capabilities including better soft tissue imaging, continuous intrafraction target visualization, automatic triggered beam delivery, and the availability of on-table adaptive replanning. Our objective was to describe patterns of 0.35 Tesla (T)-MRgRT utilization in Europe and Asia among early adopters of this novel technology. METHODS Anonymized administrative data from all 0.35T-MRgRT treatment systems in Europe and Asia were extracted for patients who completed treatment from 2015 to 2020. Detailed treatment information was analyzed for all MR-linear accelerators (linac) and -cobalt systems. RESULTS From 2015 through the end of 2020, there were 5796 completed treatment courses delivered in 46,389 individual fractions. 23.5% of fractions were adapted. Ultra-hypofractionated (UHfx) dose schedules (1-5 fractions) were delivered for 63.5% of courses, with 57.8% of UHfx fractions adapted on-table. The most commonly treated tumor types were prostate (23.5%), liver (14.5%), lung (12.3%), pancreas (11.2%), and breast (8.0%), with increasing compound annual growth rates (CAGRs) in numbers of courses from 2015 through 2020 (pancreas: 157.1%; prostate: 120.9%; lung: 136.0%; liver: 134.2%). CONCLUSIONS This is the first comprehensive study reporting patterns of utilization among early adopters of a 0.35T-MRgRT system in Europe and Asia. Intrafraction MR image-guidance, advanced motion management, and increasing adoption of on-table adaptive RT have accelerated a transition to UHfx regimens. MRgRT has been predominantly used to treat tumors in the upper abdomen, pelvis and lungs, and increasingly with adaptive replanning, which is a radical departure from legacy radiotherapy practices

Effect of COVID-19 pandemic on practice in European radiation oncology centers

ESTRO surveyed European radiation oncology department heads to evaluate the impact of COVID-19. Telemedicine was used in 78% of the departments, and 60% reported a decline in patient volume. Use of protective measures was implemented on a large scale, but shortages of personal protective equipment were present in more than half of the departments. (C) 2020 The Author(s). Published by Elsevier B.V. Radiotherapy and Oncology 150 (2020) 40-42 This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)

Using Spatial Probability Maps to Highlight Potential Inaccuracies in Deep Learning-Based Contours: Facilitating Online Adaptive Radiation Therapy

Purpose: Contouring organs at risk remains a largely manual task, which is time consuming and prone to variation. Deep learning-based delineation (DLD) shows promise both in terms of quality and speed, but it does not yet perform perfectly. Because of that, manual checking of DLD is still recommended. There are currently no commercial tools to focus attention on the areas of greatest uncertainty within a DLD contour. Therefore, we explore the use of spatial probability maps (SPMs) to help efficiency and reproducibility of DLD checking and correction, using the salivary glands as the paradigm. Methods and Materials: A 3-dimensional fully convolutional network was trained with 315/264 parotid/submandibular glands. Subsequently, SPMs were created using Monte Carlo dropout (MCD). The method was boosted by placing a Gaussian distribution (GD) over the model's parameters during sampling (MCD + GD). MCD and MCD + GD were quantitatively compared and the SPMs were visually inspected. Results: The addition of the GD appears to increase the method's ability to detect uncertainty. In general, this technique demonstrated uncertainty in areas that (1) have lower contrast, (2) are less consistently contoured by clinicians, and (3) deviate from the anatomic norm. Conclusions: We believe the integration of uncertainty information into contours made using DLD is an important step in highlighting where a contour may be less reliable. We have shown how SPMs are one way to achieve this and how they may be integrated into the online adaptive radiation therapy workflow

Strategies to improve deep learning-based salivary gland segmentation

Background: Deep learning-based delineation of organs-at-risk for radiotherapy purposes has been investigated to reduce the time-intensiveness and inter-/intra-observer variability associated with manual delineation. We systematically evaluated ways to improve the performance and reliability of deep learning for organ-at-risk segmentation, with the salivary glands as the paradigm. Improving deep learning performance is clinically relevant with applications ranging from the initial contouring process, to on-line adaptive radiotherapy. Methods: Various experiments were designed: increasing the amount of training data (1) with original images, (2) with traditional data augmentation and (3) with domain-specific data augmentation; (4) the influence of data quality was tested by comparing training/testing on clinical versus curated contours, (5) the effect of using several custom cost functions was explored, and (6) patient-specific Hounsfield unit windowing was applied during inference; lastly, (7) the effect of model ensembles was analyzed. Model performance was measured with geometric parameters and model reliability with those parameters’ variance. Results: A positive effect was observed from increasing the (1) training set size, (2/3) data augmentation, (6) patient-specific Hounsfield unit windowing and (7) model ensembles. The effects of the strategies on performance diminished when the base model performance was already ‘high’. The effect of combining all beneficial strategies was an increase in average Sørensen–Dice coefficient of about 4% and 3% and a decrease in standard deviation of about 1% and 1% for the submandibular and parotid gland, respectively. Conclusions: A subset of the strategies that were investigated provided a positive effect on model performance and reliability. The clinical impact of such strategies would be an expected reduction in post-segmentation editing, which facilitates the adoption of deep learning for autonomous automated salivary gland segmentation

Bringing FLASH to the Clinic: Treatment Planning Considerations for Ultrahigh Dose-Rate Proton Beams

Purpose: Preclinical research into ultrahigh dose rate (eg, ≥40 Gy/s) “FLASH”-radiation therapy suggests a decrease in side effects compared with conventional irradiation while maintaining tumor control. When FLASH is delivered using a scanning proton beam, tissue becomes subject to a spatially dependent range of dose rates. This study systematically investigates dose rate distributions and delivery times for proton FLASH plans using stereotactic lung irradiation as the paradigm. Methods and Materials: Stereotactic lung radiation therapy FLASH-plans, using 244 MeV scanning proton transmission beams, with the Bragg peak behind the body, were made for 7 patients. Evaluated parameters were dose rate distribution within a beam, overall irradiation time, number of times tissue is irradiated, and quality of the FLASH-plans compared with the clinical volumetric-modulated arc therapy (VMAT) plans. Results: Sparing of lungs, thoracic wall, and heart in the FLASH-plans was equal to or better than that in the VMAT-plans. For a spot peak dose rate (SPDR, the dose rate in the middle of the spot) of 100 Gy/s, ∼40% of dose is delivered at FLASH dose rates, and for SPDR = 360 Gy/s this increased to ∼75%. One-hundred percent FLASH dose rate cannot be achieved owing to small contributions from distant spots with lower dose rates. The total irradiation time varied between 300 to 730 ms, and around 85% of the dose-receiving body volume was irradiated by either 1 or 2 beams. Conclusions: Clinical implementation of FLASH using scanning proton beams requires multiple treatment planning considerations: dosimetric, temporal, and spatial parameters all seem important. The FLASH efficiency of a scanning proton beam increases with SPDR. The methodology proposed in this proof-of-principle study provides a framework for evaluating the FLASH characteristics of scanning proton beam plans and can be adapted as FLASH parameters are better defined. It currently seems logical to optimize plans for the shortest delivery time, maximum amount of high dose rate coverage, and maximum amount of single beam and continuous irradiation

Same-day consultation, simulation and lung Stereotactic Ablative Radiotherapy delivery on a Magnetic Resonance-linac

Background and Purpose: Magnetic resonance-guided radiotherapy (MRgRT) with real-time intra-fraction tumor motion monitoring allows for high precision Stereotactic Ablative Radiotherapy (SABR). This study aimed to investigate the clinical feasibility, patient satisfaction and delivery accuracy of single-fraction MR-guided SABR in a single day (one-stop-shop, OSS). Methods and Materials: Ten patients with small lung tumors eligible for single fraction treatments were included. The OSS procedure consisted of consultation, treatment simulation, treatment planning and delivery. Following SABR delivery, patients completed a reported experience measure (PREM) questionnaire. Prescribed doses ranged 28–34 Gy. Median GTV was 2.2 cm3 (range 1.3–22.9 cm3). A gating boundary of 3 mm, and PTV margin of 5 mm around the GTV, were used with auto-beam delivery control. Accuracy of SABR delivery was studied by analyzing delivered MR-cines reconstructed from machine log files. Results: All 10 patients completed the OSS procedure in a single day, and all reported satisfaction with the process. Median time for the treatment planning step and the whole procedure were 2.8 h and 6.6 h, respectively. With optimization of the procedure, treatment could be completed in half a day. During beam-on, the 3 mm tracking boundary encompassed between 78.0 and 100 % of the GTV across all patients, with corresponding PTV values being 94.4–100 % (5th-95th percentiles). On average, system-latency for triggering a beam-off event comprised 5.3 % of the delivery time. Latency reduced GTV coverage by an average of −0.3 %. Duty-cycles during treatment delivery ranged from 26.1 to 64.7 %. Conclusions: An OSS procedure with MR-guided SABR for lung cancer led to good patient satisfaction. Gated treatment delivery was highly accurate with little impact of system-latency

Same-day consultation, simulation and lung Stereotactic Ablative Radiotherapy delivery on a Magnetic Resonance-linac

Background and Purpose: Magnetic resonance-guided radiotherapy (MRgRT) with real-time intra-fraction tumor motion monitoring allows for high precision Stereotactic Ablative Radiotherapy (SABR). This study aimed to investigate the clinical feasibility, patient satisfaction and delivery accuracy of single-fraction MR-guided SABR in a single day (one-stop-shop, OSS). Methods and Materials: Ten patients with small lung tumors eligible for single fraction treatments were included. The OSS procedure consisted of consultation, treatment simulation, treatment planning and delivery. Following SABR delivery, patients completed a reported experience measure (PREM) questionnaire. Prescribed doses ranged 28–34 Gy. Median GTV was 2.2 cm3 (range 1.3–22.9 cm3). A gating boundary of 3 mm, and PTV margin of 5 mm around the GTV, were used with auto-beam delivery control. Accuracy of SABR delivery was studied by analyzing delivered MR-cines reconstructed from machine log files. Results: All 10 patients completed the OSS procedure in a single day, and all reported satisfaction with the process. Median time for the treatment planning step and the whole procedure were 2.8 h and 6.6 h, respectively. With optimization of the procedure, treatment could be completed in half a day. During beam-on, the 3 mm tracking boundary encompassed between 78.0 and 100 % of the GTV across all patients, with corresponding PTV values being 94.4–100 % (5th-95th percentiles). On average, system-latency for triggering a beam-off event comprised 5.3 % of the delivery time. Latency reduced GTV coverage by an average of −0.3 %. Duty-cycles during treatment delivery ranged from 26.1 to 64.7 %. Conclusions: An OSS procedure with MR-guided SABR for lung cancer led to good patient satisfaction. Gated treatment delivery was highly accurate with little impact of system-latency