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Purpose: Online adaptive radiotherapy relies on a high degree of automation to enable rapid planning procedures. The Varian Ethos intelligent optimization engine (IOE) was originally designed for conventional treatments so it is crucial to provide clear guidance for lung SAbR plans. This study investigates using the Ethos IOE together with adaptive‐specific optimization tuning structures we designed and templated within Ethos to mitigate inter‐planner variability in meeting RTOG metrics for both online‐adaptive and offline SAbR plans. Methods: We developed a planning strategy to automate the generation of tuning structures and optimization. This was validated by retrospective analysis of 35 lung SAbR cases (total 105 fractions) treated on Ethos. The effectiveness of our planning strategy was evaluated by comparing plan quality with‐and‐without auto‐generated tuning structures. Internal target volume (ITV) contour was compared between that drawn from CT simulation and from cone‐beam CT (CBCT) at time of treatment to verify CBCT image quality and treatment effectiveness. Planning strategy robustness for lung SAbR was quantified by frequency of plans meeting reference plan RTOG constraints. Results: Our planning strategy creates a gradient within the ITV with maximum dose in the core and improves intermediate dose conformality on average by 2%. ITV size showed no significant difference between those contoured from CT simulation and first fraction, and also trended towards decreasing over course of treatment. Compared to non‐adaptive plans, adaptive plans better meet reference plan goals (37% vs. 100% PTV coverage compliance, for scheduled and adapted plans) while improving plan quality (improved GI (gradient index) by 3.8%, CI (conformity index) by 1.7%). Conclusion: We developed a robust and readily shareable planning strategy for the treatment of adaptive lung SAbR on the Ethos system. We validated that automatic online plan re‐optimization along with the formulated adaptive tuning structures can ensure consistent plan quality. With the proposed planning strategy, highly ablative treatments are feasible on Ethos. [ABSTRACT FROM AUTHOR]

Simple Summary: Radiotherapy, a crucial cancer treatment, has evolved significantly over the years. Traditionally, treatment plans were based on initial scans used throughout the treatment course, accounting for changes in the patient's anatomy by additional margins to targets. However, the field has moved towards decreasing margins with the advancement of delivery and targeting accuracy in order to decrease toxicity, and the increasing use of image guidance has illuminated patient anatomical changes such as organ deformation, weight loss, tumor shrinkage, and even biological changes that are unaccounted for by the conventional approach. Adaptive radiotherapy (ART) addresses this by adjusting treatment plans according to these changes. ART can be conducted in two ways: online (adjustments made during treatment sessions) and offline (adjustments made between treatment sessions). Advances in technology, especially in medical imaging (CT, MRI, and PET scans) and artificial intelligence, have made ART more feasible and efficient. ART offers more precise cancer treatment by adapting to changes in the patient's body, leading to better outcomes with fewer side effects. Radiotherapy, a crucial technique in cancer therapy, has traditionally relied on the premise of largely unchanging patient anatomy during the treatment course and encompassing uncertainties by target margins. This review introduces adaptive radiotherapy (ART), a notable innovation that addresses anatomy changes and optimizes the therapeutic ratio. ART utilizes advanced imaging techniques such as CT, MRI, and PET to modify the treatment plan based on observed anatomical changes and even biological changes during the course of treatment. The narrative review provides a comprehensive guide on ART for healthcare professionals and trainees in radiation oncology and anyone else interested in the topic. The incorporation of artificial intelligence in ART has played a crucial role in improving effectiveness, particularly in contour segmentation, treatment planning, and quality assurance. This has expedited the process to render online ART feasible, lowered the burden for radiation oncology practitioners, and enhanced the precision of dynamically personalized treatment. Current technical and clinical progress on ART is discussed in this review, highlighting the ongoing development of imaging technologies and AI and emphasizing their contribution to enhancing the applicability and effectiveness of ART. [ABSTRACT FROM AUTHOR]

Clinical implementation of online adaptive radiation therapy requires initial and ongoing performance assessment of the underlying auto‐segmentation and adaptive planning algorithms, although a straightforward and efficient process for this in phantom is lacking. The purpose of this work was to investigate robustness and repeatability of the artificial intelligence‐assisted online segmentation and adaptive planning process on the Varian Ethos adaptive platform, and to develop an end‐to‐end test strategy for online adaptive radiation therapy. Five synthetic deformations were generated and applied to a computed tomography image of an anthropomorphic pelvis phantom, and reference treatment plans were generated from each of the resulting deformed images. The undeformed phantom was repeatedly imaged, and the online adaptive process was performed including auto‐segmentation, review and manual correction of contours, and adaptive plan creation. One adaptive fractions in five different deformation scenarios were performed. The manually corrected contours had a high degree of consistency (> 93% Dice similarity coefficient and < 1.0 mm mean surface distance) across repeated fractions, with no significant variation across the synthetic deformation instance except for bowel (p = 0.026, one‐way ANOVA). Adaptive treatment plans also resulted in highly consistent dose–volume values for targets and organs at risk. A straightforward and efficient process was developed and used to quantify a set of organ specific contouring and dosimetric action levels to help establish uncertainty bounds for an end‐to‐end test on the Varian Ethos system. [ABSTRACT FROM AUTHOR]

REBUTTAL Daniel Hyer, PhD Dr. Cai has argued against the future of MRI-guided adaptive radiotherapy based on three main points: high capital cost, complex workflows, and low patient throughput. The non-contrast on-board (CB) CTs have and will continue to play an important role in radiotherapy, but the future of online adaptive radiotherapy is the MR toolset. Future mainstream platform for online adaptive radiotherapy will be using on-board MR rather than on-board (CB) CT images The final point that Dr. Cai makes is that the throughput of MR adaptive radiotherapy is lower than that of CT adaptive radiotherapy. [Extracted from the article]

Purpose: We describe the acceptance testing, commissioning, periodic quality assurance, and workflow procedures developed for the first clinically implemented magnetic resonance imaging‐guided radiation therapy (MR‐IGRT) system for real‐time tracking and beam control. Methods: The system utilizes real‐time cine imaging capabilities at 4 frames per second for real‐time tracking and beam control. Testing of the system was performed using an in‐house developed motion platform and a commercially available motion phantom. Anatomical tracking is performed by first identifying a target (a region of interest that is either tissue to be treated or a critical structure) and generating a contour around it. A boundary contour is also created to identify tracking margins. The tracking algorithm deforms the anatomical contour (target or a normal organ) on every subsequent cine frame and compares it to the static boundary contour. If the anatomy of interest moves outside the boundary, the radiation delivery is halted until the tracked anatomy returns to treatment portal. The following were performed to validate and clinically implement the system: (a) spatial integrity evaluation; (b) tracking accuracy; (c) latency; (d) relative point dose and spatial dosimetry; (e) development of clinical workflow for gating; and (f) independent verification by an outside credentialing service. Results: The spatial integrity of the MR system was found to be within 2 mm over a 45‐cm diameter field‐of‐view. The tracking accuracy for geometric targets was within 1.2 mm. The average system latency was measured to be within 394 ms. The dosimetric accuracy using ionization chambers was within 1.3% ± 1.7%, and the dosimetric spatial accuracy was within 2 mm. The phantom irradiation for the outside credentialing service had satisfactory results, as well. Conclusions: The first clinical MR‐IGRT system was validated for real‐time tracking and gating capabilities and shown to be reliable and accurate. Patient workflow methods were developed for efficient treatment. Periodic quality assurance tests can be efficiently performed with commercially available equipment to ensure accurate system performance. [ABSTRACT FROM AUTHOR]

Purpose The purpose of this study was to investigate and characterize the performance of a Multi Leaf Collimator ( MLC) designed for Cobalt-60 based MR-guided radiation therapy system in a 0.35 T magnetic field. Methods The MLC design and unique assembly features in the ViewRay MRIdian system were first reviewed. The RF cage shielding of MLC motor and cables were evaluated using ACR phantoms with real-time imaging and quantified by signal-to-noise ratio. The dosimetric characterizations, including the leaf transmission, leaf penumbra, tongue-and-groove effect, were investigated using radiosensitive films. The output factor of MLC-defined fields was measured with ionization chambers for both symmetric fields from 2.1 × 2.1 cm2 to 27.3 × 27.3 cm2 and asymmetric fields from 10.5 × 10.5 cm2 to 10.5 × 2.0 cm2. Multi leaf collimator ( MLC) positional accuracy was assessed by delivering either a picket fence ( PF) style pattern on radiochromic films with wire-jig phantom or double and triple-rectangular patterns on ArcCheck- MR (Sun Nuclear, Melbourne, FL, USA) with gamma analysis as the pass/fail indicator. Leaf speed tests were performed to assess the capability of full range leaf travel within manufacture's specifications. Multi leaf collimator plan delivery reproducibility was tested by repeatedly delivering both open fields and fields with irregular shaped segments over 1-month period. Results Comparable SNRs within 4% were observed for MLC moving and stationary plans on vendor-reconstructed images, and the direct k-space reconstructed images showed that the three SNRs are within 1%. The maximum leaf transmission for all three MLCs was less than 0.35% and the average leakage was 0.153 ± 0.006%, 0.151 ± 0.008%, and 0.159 ± 0.015% for head 1, 2, and 3, respectively. Both the leaf edge and leaf end penumbra showed comparable values within 0.05 cm, and the measured values are within 0.1 cm with TPS values. The leaf edge TG effect indicated 10% underdose and the leaf end TG showed a shifted dose distribution with 0.3 cm offset. The leaf positioning test showed a 0.2 cm accuracy in the PF style test, and a gamma passing rate above 96% was observed with a 3%/2 mm criteria when comparing the measured double/triple-rectangular pattern fluence with TPS calculated fluence. The average leaf speed when executing the test plan fell in a range from 1.86 to 1.95 cm/s. The measured and TPS calculated output factors were within 2% for squared fields and within 3% for rectangular fields. The reproducibility test showed the deviation of output factors were well within 2% for square fields and the gamma passing rate within 1.5% for fields with irregular segments. The Monte Carlo predicted output factors were within 2% compared to TPS values. 15 out of the 16 IMRT plans have gamma passing rate more than 98% compared to the TPS fluence with an average passing rate of 99.1 ± 0.6%. Conclusion The MRIdian MLC has a good RF noise shielding design, low radiation leakage, good positioning accuracy, comparable TG effect, and can be modeled by an independent Monte Carlo calculation platform. [ABSTRACT FROM AUTHOR]

Deep learning neural networks (DLNN) in Artificial intelligence (AI) have been extensively explored for automatic segmentation in radiotherapy (RT). In contrast to traditional model-based methods, data-driven AI-based models for auto-segmentation have shown high accuracy in early studies in research settings and controlled environment (single institution). Vendor-provided commercial AI models are made available as part of the integrated treatment planning system (TPS) or as a stand-alone tool that provides streamlined workflow interacting with the main TPS. These commercial tools have drawn clinics' attention thanks to their significant benefit in reducing the workload from manual contouring and shortening the duration of treatment planning. However, challenges occur when applying these commercial AI-based segmentation models to diverse clinical scenarios, particularly in uncontrolled environments. Contouring nomenclature and guideline standardization has been the main task undertaken by the NRG Oncology. AI auto-segmentation holds the potential clinical trial participants to reduce interobserver variations, nomenclature non-compliance, and contouring guideline deviations. Meanwhile, trial reviewers could use AI tools to verify contour accuracy and compliance of those submitted datasets. In recognizing the growing clinical utilization and potential of these commercial AI auto-segmentation tools, NRG Oncology has formed a working group to evaluate the clinical utilization and potential of commercial AI auto-segmentation tools. The group will assess in-house and commercially available AI models, evaluation metrics, clinical challenges, and limitations, as well as future developments in addressing these challenges. General recommendations are made in terms of the implementation of these commercial AI models, as well as precautions in recognizing the challenges and limitations. [ABSTRACT FROM AUTHOR]

Positron emission tomography (PET)-guided radiation therapy is a novel tracked dose delivery modality that uses real-time PET to guide radiation therapy beamlets. The BIOGUIDE-X study was performed with sequential cohorts of participants to (1) identify the fluorodeoxyglucose (FDG) dose for PET-guided therapy and (2) confirm that the emulated dose distribution was consistent with a physician-approved radiation therapy plan. This prospective study included participants with at least 1 FDG-avid targetable primary or metastatic tumor (2-5 cm) in the lung or bone. For cohort I, a modified 3 + 3 design was used to determine the FDG dose that would result in adequate signal for PET-guided therapy. For cohort II, PET imaging data were collected on the X1 system before the first and last fractions among patients undergoing conventional stereotactic body radiation therapy. PET-guided therapy dose distributions were modeled on the patient's computed tomography anatomy using the collected PET data at each fraction as input to an "emulated delivery" and compared with the physician-approved plan. Cohort I demonstrated adequate FDG activity in 6 of 6 evaluable participants (100.0%) with the first injected dose level of 15 mCi FDG. In cohort II, 4 patients with lung tumors and 5 with bone tumors were enrolled, and evaluable emulated delivery data points were collected for 17 treatment fractions. Sixteen of the 17 emulated deliveries resulted in dose distributions that were accurate with respect to the approved PET-guided therapy plan. The 17th data point was just below the 95% threshold for accuracy (dose-volume histogram score = 94.6%). All emulated fluences were physically deliverable. No toxicities were attributed to multiple FDG administrations. PET-guided therapy is a novel radiation therapy modality in which a radiolabeled tumor can act as its own fiducial for radiation therapy targeting. Emulated therapy dose distributions calculated from continuously acquired real-time PET data were accurate and machine-deliverable in tumors that were 2 to 5 cm in size with adequate FDG signal characteristics. [ABSTRACT FROM AUTHOR]

Background: To simplify the adaptive treatment planning workflow while achieving the optimal tumor-dose coverage in pancreatic cancer patients undergoing daily adaptive magnetic resonance image guided radiation therapy (MR-IGRT).Methods: In daily adaptive MR-IGRT, the plan objective function constructed during simulation is used for plan re-optimization throughout the course of treatment. In this study, we have constructed the initial objective functions using two methods for 16 pancreatic cancer patients treated with the ViewRay™ MR-IGRT system: 1) the conventional method that handles the stomach, duodenum, small bowel, and large bowel as separate organs at risk (OARs) and 2) the OAR grouping method. Using OAR grouping, a combined OAR structure that encompasses the portions of these four primary OARs within 3 cm of the planning target volume (PTV) is created. OAR grouping simulation plans were optimized such that the target coverage was comparable to the clinical simulation plan constructed in the conventional manner. In both cases, the initial objective function was then applied to each successive treatment fraction and the plan was re-optimized based on the patient's daily anatomy. OAR grouping plans were compared to conventional plans at each fraction in terms of coverage of the PTV and the optimized PTV (PTV OPT), which is the result of the subtraction of overlapping OAR volumes with an additional margin from the PTV.Results: Plan performance was enhanced across a majority of fractions using OAR grouping. The percentage of the volume of the PTV covered by 95% of the prescribed dose (D95) was improved by an average of 3.87 ± 4.29% while D95 coverage of the PTV OPT increased by 3.98 ± 4.97%. Finally, D100 coverage of the PTV demonstrated an average increase of 6.47 ± 7.16% and a maximum improvement of 20.19%.Conclusions: In this study, our proposed OAR grouping plans generally outperformed conventional plans, especially when the conventional simulation plan favored or disregarded an OAR through the assignment of distinct weighting parameters relative to the other critical structures. OAR grouping simplifies the MR-IGRT adaptive treatment planning workflow at simulation while demonstrating improved coverage compared to delivered pancreatic cancer treatment plans in daily adaptive radiation therapy. [ABSTRACT FROM AUTHOR]

Purpose: To validate the dosimetric accuracy of a commercially available magnetic resonance guided intensity modulated radiation therapy (MRgIMRT) system using a hybrid approach: 3-dimensional (3D) measurements and Monte Carlo calculations.Methods and Materials: We used PRESAGE radiochromic plastic dosimeters with remote optical computed tomography readout to perform 3D high-resolution measurements, following a novel remote dosimetry protocol. We followed the intensity modulated radiation therapy commissioning recommendations of American Association of Physicists in Medicine Task Group 119, adapted to incorporate 3D data. Preliminary tests ("AP" and "3D-Bands") were delivered to 9.5-cm usable diameter cylindrical PRESAGE dosimeters to validate the treatment planning system (TPS) for nonmodulated deliveries; assess the sensitivity, uniformity, and rotational symmetry of the PRESAGE dosimeters; and test the robustness of the remote dosimetry protocol. Following this, 4 clinical MRgIMRT plans ("MultiTarget," "Prostate," "Head/Neck," and "C-Shape") were measured using 13-cm usable diameter PRESAGE dosimeters. For all plans, 3D-γ (3% or 3 mm global, 10% threshold) passing rates were calculated and 3D-γ maps were examined. Point doses were measured with an IBA-CC01 ionization chamber for validation of absolute dose. Finally, by use of an in-house-developed, GPU-accelerated Monte Carlo algorithm (gPENELOPE), we independently calculated dose for all 6 Task Group 119 plans and compared against the TPS.Results: For PRESAGE measurements, 3D-γ analysis yielded passing rates of 98.7%, 99.2%, 98.5%, 98.0%, 99.2%, and 90.7% for AP, 3D-Bands, MultiTarget, Prostate, Head/Neck, and C-Shape, respectively. Ion chamber measurements were within an average of 0.5% (±1.1%) from the TPS dose. Monte Carlo calculations demonstrated good agreement with the TPS, with a mean 3D-γ passing rate of 98.5% ± 1.9% using a stricter 2%/2-mm criterion.Conclusions: We have validated the dosimetric accuracy of a commercial MRgIMRT system using high-resolution 3D techniques. We have demonstrated for the first time that hybrid 3D remote dosimetry is a comprehensive and feasible approach to commissioning MRgIMRT. This may provide better sensitivity in error detection compared with standard 2-dimensional measurements and could be used when implementing complex new magnetic resonance guided radiation therapy technologies. [ABSTRACT FROM AUTHOR]

• A (quasi) real-time automatic treatment planning (RTTP) method for radiotherapy was proposed. • Fluence map prediction took only one step after the anatomy contours were prepared. • Model training was based on 314 patients with 2198 projection data samples. • Plan quality and physician blinded review results are close to the manual planning method. • A promising planning method in which real-time planning efficiency and reasonable dose requirements are needed. To establish and evaluate a (quasi) real-time automated treatment planning (RTTP) strategy utilizing a one-step full 3D fluence map prediction model based on a nonorthogonal convolution operation for rectal cancer radiotherapy. The RTTP approach directly extracts 3D projections from volumetric CT and anatomical data according to the beam incident direction. A 3D deep learning model with a nonorthogonal convolution operation was established that takes projections in cone beam space as input, extracts the features along and around the ray-trace path, and outputs a predicted fluence map (PFM) for each beam. The PFM is then converted to the MLC sequence with deliverable MUs to generate the final treatment plan. A total of 314 rectal adenocarcinoma patients with 2198 projection data samples were used in model training and validation. An extra 20 patients were used to test the feasibility of the RTTP method by comparing the plan quality, efficiency, deliverability performance, and physician blinded review results with the manual plans. Overall, the RTTP plans met the clinical dose criteria for target coverage, conformity, homogeneity, and organ-at-risk dose sparing. Compared to manual plans, the RTTP plans showed increases in PTV D 1% by only 2.33% (p < 0.001) and a decrease in PTV D 99% by 0.45% (p < 0.05). The RTTP plans showed a dose increase in the bladder, with a V 50 of 14.01 ± 11.75% vs. 10.74 ± 8.51%, respectively, and no significant increases in the femoral head with the mean dose. The planning efficiency was improved in RTTP planning, with 39 s vs. 944 s in fluence map generation; the deliverability performance was saved by 1.91% (p < 0.001) in total MU. According to the blinded plan review by our physician, 55% of RTTP plans can be directly used in clinical radiotherapy treatment. The quasi RTTP method improves the planning efficiency and deliverability performance while maintaining a plan quality close to that of the optimized manual plans in rectal radiotherapy. [ABSTRACT FROM AUTHOR]

• First prospective imaging study of physician contours on a novel ring-gantry kV-CBCT imaging device. • Physician delineation of OARs on the Halcyon kV-CBCT device was non-inferior to simulation CT. • Improved on-board CT-based imaging may enable CT-guided online adaptive radiotherapy. A kV imager coupled to a novel, ring-gantry radiotherapy system offers improved on-board kV-cone-beam computed tomography (CBCT) acquisition time (17–40 seconds) and image quality, which may improve CT radiotherapy image-guidance and enable online adaptive radiotherapy. We evaluated whether inter-observer contour variability over various anatomic structures was non-inferior using a novel ring gantry kV-CBCT (RG-CBCT) imager as compared to diagnostic-quality simulation CT (simCT). Seven patients undergoing radiotherapy were imaged with the RG-CBCT system at breath hold (BH) and/or free breathing (FB) for various disease sites on a prospective imaging study. Anatomy was independently contoured by seven radiation oncologists on: 1. SimCT 2. Standard C-arm kV-CBCT (CA-CBCT), and 3. Novel RG-CBCT at FB and BH. Inter-observer contour variability was evaluated by computing simultaneous truth and performance level estimation (STAPLE) consensus contours, then computing average symmetric surface distance (ASSD) and Dice similarity coefficient (DSC) between individual raters and consensus contours for comparison across image types. Across 7 patients, 18 organs-at-risk (OARs) were evaluated on 27 image sets. Both BH and FB RG-CBCT were non-inferior to simCT for inter-observer delineation variability across all OARs and patients by ASSD analysis (p < 0.001), whereas CA-CBCT was not (p = 0.923). RG-CBCT (FB and BH) also remained non-inferior for abdomen and breast subsites compared to simCT on ASSD analysis (p < 0.025). On DSC comparison, neither RG-CBCT nor CA-CBCT were non-inferior to simCT for all sites (p > 0.025). Inter-observer ability to delineate OARs using novel RG-CBCT images was non-inferior to simCT by the ASSD criterion but not DSC criterion. [ABSTRACT FROM AUTHOR]