Clinical implementation of artificial intelligence-driven cone-beam computed tomography-guided online adaptive radiotherapy in the pelvic region

A Pilot Study of Simulation-Free Hippocampal-Avoidance Whole Brain Radiation Therapy Using Diagnostic MRI-Based and Online Adaptive Planning

PURPOSE

We aimed to demonstrate the clinical feasibility and safety of simulation-free hippocampal avoidance whole brain radiation therapy (HA-WBRT) in a pilot study (National Clinical Trial 05096286).

METHODS AND MATERIALS

Ten HA-WBRT candidates were enrolled for treatment on a commercially available computed tomography (CT)-guided linear accelerator with online adaptive capabilities. Planning structures were contoured on patient-specific diagnostic magnetic resonance imaging (MRI), which were registered to a CT of similar head shape, obtained from an atlas-based database (AB-CT). These patient-specific diagnostic MRI and AB-CT data sets were used for preplan calculation, using NRG-CC001 constraints. At first fraction, AB-CTs were used as primary data sets and deformed to patient-specific cone beam CTs (CBCT) to give patient-matched density information. Brain, ventricle, and brain stem contours were matched through rigid translation and rotation to the corresponding anatomy on CBCT. Lens, optic nerve, and brain contours were manually edited based on CBCT visualization. Preplans were then reoptimized through online adaptation to create final, simulation-free plans, which were used if they met all objectives. Workflow tasks were timed. In addition, patients underwent CT-simulation to create immobilization devices and for prospective dosimetric comparison of simulation-free and simulation-based plans.

RESULTS

Median time from MRI importation to completion of "preplan" was 1 weekday (range, 1-4). Median on-table workflow duration was 41 minutes (range, 34-70). NRG-CC001 constraints were achieved by 90% of the simulation-free plans. One patient's simulation-free plan failed a planning target volume coverage objective (89% instead of 90% coverage); this was deemed acceptable for first-fraction delivery, with an offline replan used for subsequent fractions. Both simulation-free and simulation CT-based plans otherwise met constraints, without clinically meaningful differences.

CONCLUSIONS

Simulation-free HA-WBRT using online adaptive radiation therapy is feasible, safe, and results in dosimetrically comparable treatment plans to simulation CT-based workflows while providing convenience and time savings for patients.

Clinical Experience With an Offline Adaptive Radiation Therapy Head and Neck Program: Dosimetric Benefits and Opportunities for Patient Selection

PURPOSE

The objective of this study was to develop a linear accelerator (LINAC)-based adaptive radiation therapy (ART) workflow for the head and neck that is informed by automated image tracking to identify major anatomic changes warranting adaptation. In this study, we report our initial clinical experience with the program and an investigation into potential trigger signals for ART.

METHODS AND MATERIALS

Offline ART was systematically performed on patients receiving radiation therapy for head and neck cancer on C-arm LINACs. Adaptations were performed at a single time point during treatment with resimulation approximately 3 weeks into treatment. Throughout treatment, all patients were tracked using an automated image tracking system called the Automated Watchdog for Adaptive Radiotherapy Environment (AWARE). AWARE measures volumetric changes in gross tumor volumes (GTVs) and selected normal tissues via cone beam computed tomography scans and deformable registration. The benefit of ART was determined by comparing adaptive plan dosimetry and normal tissue complication probabilities against the initial plans recalculated on resimulation computed tomography scans. Dosimetric differences were then correlated with AWARE-measured volume changes to identify patient-specific triggers for ART. Candidate trigger variables were evaluated using receiver operator characteristic analysis.

RESULTS

In total, 46 patients received ART in this study. Among these patients, we observed a significant decrease in dose to the submandibular glands (mean ± standard deviation: -219.2 ± 291.2 cGy, P < 10-5), parotids (-68.2 ± 197.7 cGy, P = .001), and oral cavity (-238.7 ± 206.7 cGy, P < 10-5) with the adaptive plan. Normal tissue complication probabilities for xerostomia computed from mean parotid doses also decreased significantly with the adaptive plans (P = .008). We also observed systematic intratreatment volume reductions (ΔV) for GTVs and normal tissues. Candidate triggers were identified that predicted significant improvement with ART, including parotid ΔV = 7%, neck ΔV = 2%, and nodal GTV ΔV = 29%.

CONCLUSIONS

Systematic offline head and neck ART was successfully deployed on conventional LINACs and reduced doses to critical salivary structures and the oral cavity. Automated cone beam computed tomography tracking provided information regarding anatomic changes that may aid patient-specific triggering for ART.

Multi-institutional questionnaire-based survey on online adaptive radiotherapy performed using commercial systems in Japan in 2023

In this study, we aimed to conduct a survey on the current clinical practice of, staffing for, commissioning of, and staff training for online adaptive radiotherapy (oART) in the institutions that installed commercial oART systems in Japan, and to share the information with institutions that will implement oART systems in future. A web-based questionnaire, containing 107 questions, was distributed to nine institutions in Japan. Data were collected from November to December 2023. Three institutions each with the MRIdian (ViewRay, Oakwood Village, OH, USA), Unity (Elekta AB, Stockholm, Sweden), and Ethos (Varian Medical Systems, Palo Alto, CA, USA) systems completed the questionnaire. One institution (MRIdian) had not performed oART by the response deadline. Each institution had installed only one oART system. Hypofractionation, and moderate hypofractionation or conventional fractionation were employed in the MRIdian/Unity and Ethos systems, respectively. The elapsed time for the oART process was faster with the Ethos than with the other systems. All institutions added additional staff for oART. Commissioning periods differed among the oART systems owing to provision of beam data from the vendors. Chambers used during commissioning measurements differed among the institutions. Institutional training was provided by all nine institutions. To the best of our knowledge, this was the first survey about oART performed using commercial systems in Japan. We believe that this study will provide useful information to institutions that installed, are installing, or are planning to install oART systems.

Safety and Efficiency Analysis of Operational Decision-Making During Cone Beam Computed Tomography-Based Online Adaptive Radiation Therapy

PURPOSE

Cone beam computed tomography (CBCT)-based online adaptive radiation therapy (ART) is especially beneficial for patients with large interfractional anatomic changes. However, treatment planning and review decisions need to be made at the treatment console in real-time and may be delegated to clinical staff whose conventional scope of practice does not include making such decisions. Therefore, implementation can create new safety risks and inefficiencies. The objective of this work is to systematically analyze the safety and efficiency implications of human decision-making during the treatment session for CBCT-based online ART.

METHODS AND MATERIALS

The analysis was performed by applying the Systems-Theoretical Process Analysis technique and its extension for human decision-making. Four centers of different CBCT-based online ART practice models comprised the analysis team.

RESULTS

The general radiation therapy control structure was refined to model the interactions between routine treatment delivery staff and in-person or remote support staff. The treatment delivery staff perform 6 key control actions. Eighteen undesirable states of those control actions were identified as affecting safety and/or efficiency. In turn, 97 hazardous clinical scenarios were identified, with the control action "prepare and position patient" having the least number of scenarios and "delineate/edit influencer and target structures" having the most. Five of these are specific to either in-person or remote support during the treatment session, and 12 arise from staff support in general.

CONCLUSIONS

An optimally safe and efficient online ART program should require little to no support staff at the treatment console to reduce staff coordination. Uptraining of the staff already at the treatment console is needed to achieve this goal. Beyond the essential knowledge and skills such as contour editing and the selection of an optimal plan, uptraining should also target the specific cognitive biases identified in this work and the cognitive strategies to overcome these biases. Additionally, technological and organizational changes are necessary.

Bringing online adaptive radiotherapy to a standard C-arm linac

Current online adaptive radiotherapy (oART) workflows require dedicated equipment. Our aim was to develop and implement an oART workflow for a C-arm linac which can be performed using standard clinically available tools. A workflow was successfully developed and implemented. Three patients receiving palliative radiotherapy for bladder cancer were treated, with 33 of 35 total fractions being delivered with the cone-beam computed tomography (CBCT)-guided oART workflow. Average oART fraction duration was 24 min from start of CBCT acquisition to end of beam on. This work shows how oART could be performed without dedicated equipment, broadening oART availability for application at existing treatment machines.

Daily Online Adaptive Radiation Therapy of Postoperative Endometrial and Cervical Cancer With PTV Margin Reduction to 5 mm: Dosimetric Outcomes, Acute Toxicity, and First Clinical Experience

Purpose

This study evaluated the first clinical implementation of daily iterative cone beam computed tomography (iCBCT)-guided online adaptive radiation therapy (oART) in the postoperative treatment of endometrial and cervical cancer.

Methods and Materials

Seventeen consecutive patients treated with daily iCBCT-guided oART were enrolled in this prospective study, with a reduced uniform 3-dimensional PTV margin of 5 mm. Treatment plans were designed to deliver 45 or 50.4 Gy in 1.8 Gy daily fractions to PTV. Pre- and posttreatment ultrasound and iCBCT scans were performed to record intrafractional bladder and rectal volume changes. The accuracy of contouring, oART procedure time, dosimetric outcomes, and acute toxicity were evaluated.

Results

The average time from first iCBCT acquisition to completion of treatment was 22 minutes and 26 seconds. During this period, bladder volume increased by 44 cm3 using iCBCT contouring, whereas rectal volume remained stable (62.9 cm3 pretreatment vs 61.9 cm3 posttreatment). A total of 91.6% of influencers and 88.1% of CTVs required no or minor edits. The adapted plan was selected in all (434) fractions and significantly improved the dosimetry coverage for CTV and PTV, especially the vaginal PTV coverage by nearly 7% (P < .05). The adapted bladder Dmean was 104.61 cGy, and the rectum Dmean was 123.67 cGy, significantly lower than the scheduled plan of 108.24 and 128.19 cGy, respectively. The bone marrow and femur head left and right dosimetry were also improved with adaptation. Grade 2 acute gastrointestinal and genitourinary toxicities were 24% and 0, respectively. There was a grade 3 acute toxicity of decreased white blood cell count in 1 patient.

Conclusions

Daily oART was associated with favorable dosimetry improvement and low acute toxicity, supporting its safety and efficacy for postoperative treatment of endometrial and cervical cancer. These results need to be validated in a larger prospective randomized controlled cohort.

Evaluation of Ethos intelligent optimization engine for left locally advanced breast cancer

Purpose

To evaluate the feasibility to use a standard Ethos planning template to treat left-sided breast cancer with regional lymph nodes.

Material/Methods

The tuning cohort of 5 patients was used to create a planning template. The validation cohort included 15 patients treated for a locally advanced left breast cancer randomly enrolled. The Ethos planning template was tuned using standard 3 partial arc VMAT and two collimator rotation configurations: 45/285/345° and 30/60/330°. Re-planning was performed automatically using the template without editing. The study was conducted with a schedule of 42.3 Gy in 18 fractions to the breast/chestwall, internal mammary chain (IMC) and regional lymph nodes ("Nodes"). The PTV was defined as a 3D extension of the CTV with a margin of 7 mm, excluding the 5mm below the skin. The manual treatment plans were performed using Eclipse treatment planning system with AAA and PO algorithms (v15.6) and a manual arc VMAT configuration and imported in Ethos TPS (v1.1) for a dose calculation with Ethos Acuros algorithm. The automated plans were compared with the manual plans using PTV and CTV coverage, homogeneity and conformity indices (HI and CN) and doses to organs at risk (OAR) via DVH metrics. For each plan, the patient quality assurance (QA) were performed using Mobius3D and gamma index. Finally, two breast radiation oncologists performed a blinded assessment of the clinical acceptability of each of the three plans (manual and automated) for each patient.

Results

The manual and automated plans provided suitable treatment planning as regards dose constraints. The dosimetric comparison showed the CTV_breast D99% were significantly improved with both automated plans (p< 0,002) while PTV coverage was comparable. The doses to the organs at risk were equivalent for the three plans. Concerning treatment delivery, the Ethos-45° and Ethos-30° plans led to an increase in MUs compared to the manual plans, without affecting the beam on time. The average gamma index pass rates remained consistently above 98% regardless of the type of plan utilized. In the blinded evaluation, clinicians 1 and 2 assessed 13 out of 15 plans for Ethos 45° and 11 out of 15 plans for Ethos 30° as clinically acceptable.

Conclusion

Using a standard planning template for locally advanced breast cancer, the Ethos TPS provided automated plans that were clinically acceptable and comparable in quality to manually generated plans. Automated plans also dramatically reduce workflow and operator variability.

Time is NTCP: Should we maximize patient throughput or perform online adaptation on proton therapy systems?

BACKGROUND

Compared to conventional radiotherapy (XT), proton therapy (PT) may improve normal tissue complication probabilities (NTCP). However, PT typically requires higher adaptation rates due to an increased sensitivity to anatomical changes. Systematic online adaptation may address this issue, but it requires additional replanning time, decreasing patient throughput. Therefore, less patients would benefit in such case from PT for a given machine capacity, with results in worse NTCP.

AIM

To investigate the trade-off between PT patient throughput and NTCP gain as a function of the time needed for adaptation.

METHODS

A retrospective database of 14 lung patients with two repeated 4DCTs was used to compare NTCP values between XT and PT for NTCP2ym (2-year mortality), NTCPdysphagia and NTCPpneumonitis. Four scenarios were considered for PT: no adaptation using clinical robustness parameters (4D robust optimization, 3 % range error and PTV-equivalent setup errors); systematic online adaptation with clinical robustness parameters; setup errors reduced to 4 mm and to 2 mm. Dose was accumulated on the planning CT. The number of patients treated with PT depended on the extra time needed for adaptation, assuming an 8-hours capacity (assuming 14 patients a day; thus minimum 34.2 min per treatment session if there is no or instantaneous adaptation).

RESULTS

Baseline NTCP gains (PT against XT without adaptation) equaled 6.9 %, 6.1 %, and 7.7 % for NTCP2ym, NTCPdysphagia and NTCPpneumonitis, respectively. Using instantaneous online adaptation and setup errors of 2 mm, the overall gains were then 10.7 %, 13.6 % and 12.4 %. Taking into account loss of capacity, 13.7 min was the maximum extra-time allowed to complete adaptation and maintain an advantage on all three metrics for the 2-mm setup error scenario.

CONCLUSION

This study highlights the critical importance of keeping short online adaptation times when using systems with limited capacity like PT.

Adaptive automated sinogram normalization for ring artifacts suppression in CT

Ring artifacts pose a major barrier to obtaining precise reconstruction in computed tomography (CT). The presence of ring artifacts complicates the use of automatic means of processing CT reconstruction results, such as segmentation, correction of geometric shapes, alignment of reconstructed volumes. Although there are numerous efficient methods for suppressing ring artifacts, many of them appear to be manual. Along with this, a large proportion of the automatic methods cope unsatisfactorily with the target task while requiring computational capacity. The current work introduces a projection data preprocessing method for suppressing ring artifacts that constitutes a compromise among the outlined aspects - automaticity, high efficiency and computational speed. Derived as the automation of the classical sinogram normalization method, the proposed method specific advantages consist in adaptability in relation to the filtered sinograms and the edge-preservation property proven within the experiments on both synthetic and real CT data. Concerning the challenging open-access data, the method has performed superior quality comparable to that of the advanced methods: it has demonstrated 70.4% ring artifacts suppression percentage (RASP) quality metric. In application to our real laboratory CT data, the proposed method allowed us to gain significant refinement of the reconstruction quality which has not been surpassed by a range of compared manual ring artifacts suppression methods.

NRG Oncology Assessment of Artificial Intelligence Deep Learning-Based Auto-segmentation for Radiation Therapy: Current Developments, Clinical Considerations, and Future Directions

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.

Markerless liver online adaptive stereotactic radiotherapy: feasibility analysisCervantes

Objective. Radio-opaque markers are recommended for image-guided radiotherapy in liver stereotactic ablative radiotherapy (SABR), but their implantation is invasive. We evaluate in thisin-silicostudy the feasibility of cone-beam computed tomography-guided stereotactic online-adaptive radiotherapy (CBCT-STAR) to propagate the target volumes without implanting radio-opaque markers and assess its consequence on the margin that should be used in that context.Approach. An emulator of a CBCT-STAR-dedicated treatment planning system was used to generate plans for 32 liver SABR patients. Three target volume propagation strategies were compared, analysing the volume difference between the GTVPropagatedand the GTVConventional, the vector lengths between their centres of mass (lCoM), and the 95th percentile of the Hausdorff distance between these two volumes (HD95). These propagation strategies were: (1) structure-guided deformable registration with deformable GTV propagation; (2) rigid registration with rigid GTV propagation; and (3) image-guided deformable registration with rigid GTV propagation. Adaptive margin calculation integrated propagation errors, while interfraction position errors were removed. Scheduled plans (PlanNon-adaptive) and daily-adapted plans (PlanAdaptive) were compared for each treatment fraction.Main results.The image-guided deformable registration with rigid GTV propagation was the best propagation strategy regarding tolCoM(mean: 4.3 +/- 2.1 mm), HD95 (mean 4.8 +/- 3.2 mm) and volume preservation between GTVPropagatedand GTVConventional. This resulted in a planning target volume (PTV) margin increase (+69.1% in volume on average). Online adaptation (PlanAdaptive) reduced the violation rate of the most important dose constraints ('priority 1 constraints', 4.2 versus 0.9%, respectively;p< 0.001) and even improved target volume coverage compared to non-adaptive plans (PlanNon-adaptive).Significance. Markerless CBCT-STAR for liver tumours is feasible using Image-guided deformable registration with rigid GTV propagation. Despite the cost in terms of PTV volumes, daily adaptation reduces constraints violation and restores target volumes coverage.

ROAR-A: re-optimization based Online Adaptive Radiotherapy of anal cancer, a prospective phase II trial protocol

BACKGROUND

Chemo-radiotherapy with curative intent for anal cancer has high complete remission rates, but acute treatment-related gastrointestinal (GI) toxicity is significant. Toxicity occurs due to irradiation of surrounding normal tissue. Current radiotherapy requires the addition of large planning margins to the radiation field to ensure target coverage regardless of the considerable organ motion in the pelvic region. This increases the irradiated volume and radiation dose to the surrounding normal tissue and thereby toxicity. Online adaptive radiotherapy uses artificial intelligence to adjust the treatment to the anatomy of the day. This allows for the reduction of planning margins, minimizing the irradiated volume and thereby radiation to the surrounding normal tissue.This study examines if cone beam computed tomography (CBCT)-guided oART with daily automated treatment re-planning can reduce acute gastrointestinal toxicity in patients with anal cancer.

METHODS/DESIGN

The study is a prospective, single-arm, phase II trial conducted at Copenhagen University Hospital, Herlev and Gentofte, Denmark. 205 patients with local only or locally advanced anal cancer, referred for radiotherapy with or without chemotherapy with curative intent, are planned for inclusion. Toxicity and quality of life are reported with Common Terminology Criteria of Adverse Events and patient-reported outcome questionnaires, before, during, and after treatment. The primary endpoint is a reduction in the incidence of acute treatment-related grade ≥ 2 diarrhea from 36 to 25% after daily online adaptive radiotherapy compared to standard radiotherapy. Secondary endpoints include all acute and late toxicity, overall survival, and reduction in treatment interruptions.

RESULTS

Accrual began in January 2022 and is expected to finish in January 2026. Primary endpoint results are expected to be available in April 2026.

DISCUSSION

This is the first study utilizing online adaptive radiotherapy to treat anal cancer. We hope to determine whether there is a clinical benefit for the patients, with significant reductions in acute GI toxicity without compromising treatment efficacy.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT05438836. Danish Ethical Committee: H-21028093.

Clinical Workflow of Cone Beam Computer Tomography-Based Daily Online Adaptive Radiotherapy with Offline Magnetic Resonance Guidance: The Modular Adaptive Radiotherapy System (MARS)

PURPOSE

The Ethos (Varian Medical Systems) radiotherapy device combines semi-automated anatomy detection and plan generation for cone beam computer tomography (CBCT)-based daily online adaptive radiotherapy (oART). However, CBCT offers less soft tissue contrast than magnetic resonance imaging (MRI). This work aims to present the clinical workflow of CBCT-based oART with shuttle-based offline MR guidance.

METHODS

From February to November 2023, 31 patients underwent radiotherapy on the Ethos (Varian, Palo Alto, CA, USA) system with machine learning (ML)-supported daily oART. Moreover, patients received weekly MRI in treatment position, which was utilized for daily plan adaptation, via a shuttle-based system. Initial and adapted treatment plans were generated using the Ethos treatment planning system. Patient clinical data, fractional session times (MRI + shuttle transport + positioning, adaptation, QA, RT delivery) and plan selection were assessed for all fractions in all patients.

RESULTS

In total, 737 oART fractions were applied and 118 MRIs for offline MR guidance were acquired. Primary sites of tumors were prostate (n = 16), lung (n = 7), cervix (n = 5), bladder (n = 1) and endometrium (n = 2). The treatment was completed in all patients. The median MRI acquisition time including shuttle transport and positioning to initiation of the Ethos adaptive session was 53.6 min (IQR 46.5-63.4). The median total treatment time without MRI was 30.7 min (IQR 24.7-39.2). Separately, median adaptation, plan QA and RT times were 24.3 min (IQR 18.6-32.2), 0.4 min (IQR 0.3-1,0) and 5.3 min (IQR 4.5-6.7), respectively. The adapted plan was chosen over the scheduled plan in 97.7% of cases.

CONCLUSION

This study describes the first workflow to date of a CBCT-based oART combined with a shuttle-based offline approach for MR guidance. The oART duration times reported resemble the range shown by previous publications for first clinical experiences with the Ethos system.

Daily AI-Based Treatment Adaptation under Weekly Offline MR Guidance in Chemoradiotherapy for Cervical Cancer 1: The AIM-C1 Trial

(1) Background: External beam radiotherapy (EBRT) and concurrent chemotherapy, followed by brachytherapy (BT), offer a standard of care for patients with locally advanced cervical carcinoma. Conventionally, large safety margins are required to compensate for organ movement, potentially increasing toxicity. Lately, daily high-quality cone beam CT (CBCT)-guided adaptive radiotherapy, aided by artificial intelligence (AI), became clinically available. Thus, online treatment plans can be adapted to the current position of the tumor and the adjacent organs at risk (OAR), while the patient is lying on the treatment couch. We sought to evaluate the potential of this new technology, including a weekly shuttle-based 3T-MRI scan in various treatment positions for tumor evaluation and for decreasing treatment-related side effects. (2) Methods: This is a prospective one-armed phase-II trial consisting of 40 patients with cervical carcinoma (FIGO IB-IIIC1) with an age ≥ 18 years and a Karnofsky performance score ≥ 70%. EBRT (45-50.4 Gy in 25-28 fractions with 55.0-58.8 Gy simultaneous integrated boosts to lymph node metastases) will be accompanied by weekly shuttle-based MRIs. Concurrent platinum-based chemotherapy will be given, followed by 28 Gy of BT (four fractions). The primary endpoint will be the occurrence of overall early bowel and bladder toxicity CTCAE grade 2 or higher (CTCAE v5.0). Secondary outcomes include clinical feasibility, quality of life, and imaging-based response assessment.

Simulation and pre-planning omitted radiotherapy (SPORT): a feasibility study for prostate cancer

This study explored the feasibility of on-couch intensity modulated radiotherapy (IMRT) planning for prostate cancer (PCa) on a cone-beam CT (CBCT)-based online adaptive RT platform without an individualized pre-treatment plan and contours. Ten patients with PCa previously treated with image-guided IMRT (60 Gy/20 fractions) were selected. In contrast to the routine online adaptive RT workflow, a novel approach was employed in which the same preplan that was optimized on one reference patient was adapted to generate individual on-couch/initial plans for the other nine test patients using Ethos emulator. Simulation CTs of the test patients were used as simulated online CBCT (sCBCT) for emulation. Quality assessments were conducted on synthetic CTs (sCT). Dosimetric comparisons were performed between on-couch plans, on-couch plans recomputed on the sCBCT and individually optimized plans for test patients. The median value of mean absolute difference between sCT and sCBCT was 74.7 HU (range 69.5-91.5 HU). The average CTV/PTV coverage by prescription dose was 100.0%/94.7%, and normal tissue constraints were met for the nine test patients in on-couch plans on sCT. Recalculating on-couch plans on the sCBCT showed about 0.7% reduction of PTV coverage and a 0.6% increasing of hotspot, and the dose difference of the OARs was negligible (<0.5 Gy). Hence, initial IMRT plans for new patients can be generated by adapting a reference patient's preplan with online contours, which had similar qualities to the conventional approach of individually optimized plan on the simulation CT. Further study is needed to identify selection criteria for patient anatomy most amenable to this workflow.

Advancing equitable and personalized cancer care: Novel applications and priorities of artificial intelligence for fairness and inclusivity in the patient care workflow

Patient care workflows are highly multimodal and intertwined: the intersection of data outputs provided from different disciplines and in different formats remains one of the main challenges of modern oncology. Artificial Intelligence (AI) has the potential to revolutionize the current clinical practice of oncology owing to advancements in digitalization, database expansion, computational technologies, and algorithmic innovations that facilitate discernment of complex relationships in multimodal data. Within oncology, radiation therapy (RT) represents an increasingly complex working procedure, involving many labor-intensive and operator-dependent tasks. In this context, AI has gained momentum as a powerful tool to standardize treatment performance and reduce inter-observer variability in a time-efficient manner. This review explores the hurdles associated with the development, implementation, and maintenance of AI platforms and highlights current measures in place to address them. In examining AI's role in oncology workflows, we underscore that a thorough and critical consideration of these challenges is the only way to ensure equitable and unbiased care delivery, ultimately serving patients' survival and quality of life.

The accuracy of artificial intelligence deformed nodal structures in cervical online cone-beam-based adaptive radiotherapy

Background and Purpose

Online cone-beam-based adaptive radiotherapy (ART) adjusts for anatomical changes during external beam radiotherapy. However, limited cone-beam image quality complicates nodal contouring. Despite this challenge, artificial-intelligence guided deformation (AID) can auto-generate nodal contours. Our study investigated the optimal use of such contours in cervical online cone-beam-based ART.

Materials and Methods

From 136 adaptive fractions across 21 cervical cancer patients with nodal disease, we extracted 649 clinically-delivered and AID clinical target volume (CTV) lymph node boost structures. We assessed geometric alignment between AID and clinical CTVs via dice similarity coefficient, and 95% Hausdorff distance, and geometric coverage of clinical CTVs by AID planning target volumes by false positive dice. Coverage of clinical CTVs by AID contour-based plans was evaluated using D100, D95, V100%, and V95%.

Results

Between AID and clinical CTVs, the median dice similarity coefficient was 0.66 and the median 95 % Hausdorff distance was 4.0 mm. The median false positive dice of clinical CTV coverage by AID planning target volumes was 0. The median D100 was 1.00, the median D95 was 1.01, the median V100% was 1.00, and the median V95% was 1.00. Increased nodal volume, fraction number, and daily adaptation were associated with reduced clinical CTV coverage by AID-based plans.

Conclusion

In one of the first reports on pelvic nodal ART, AID-based plans could adequately cover nodal targets. However, physician review is required due to performance variation. Greater attention is needed for larger, daily-adapted nodes further into treatment.

Artificial intelligence applied to image-guided radiation therapy (IGRT): a systematic review by the Young Group of the Italian Association of Radiotherapy and Clinical Oncology (yAIRO)

INTRODUCTION

The advent of image-guided radiation therapy (IGRT) has recently changed the workflow of radiation treatments by ensuring highly collimated treatments. Artificial intelligence (AI) and radiomics are tools that have shown promising results for diagnosis, treatment optimization and outcome prediction. This review aims to assess the impact of AI and radiomics on modern IGRT modalities in RT.

METHODS

A PubMed/MEDLINE and Embase systematic review was conducted to investigate the impact of radiomics and AI to modern IGRT modalities. The search strategy was "Radiomics" AND "Cone Beam Computed Tomography"; "Radiomics" AND "Magnetic Resonance guided Radiotherapy"; "Radiomics" AND "on board Magnetic Resonance Radiotherapy"; "Artificial Intelligence" AND "Cone Beam Computed Tomography"; "Artificial Intelligence" AND "Magnetic Resonance guided Radiotherapy"; "Artificial Intelligence" AND "on board Magnetic Resonance Radiotherapy" and only original articles up to 01.11.2022 were considered.

RESULTS

A total of 402 studies were obtained using the previously mentioned search strategy on PubMed and Embase. The analysis was performed on a total of 84 papers obtained following the complete selection process. Radiomics application to IGRT was analyzed in 23 papers, while a total 61 papers were focused on the impact of AI on IGRT techniques.

DISCUSSION

AI and radiomics seem to significantly impact IGRT in all the phases of RT workflow, even if the evidence in the literature is based on retrospective data. Further studies are needed to confirm these tools' potential and provide a stronger correlation with clinical outcomes and gold-standard treatment strategies.

Predicting cervical cancer target motion using a multivariate regression model to enable patient selection for adaptive external beam radiotherapy

Background and purpose

Interfraction motion during cervical cancer radiotherapy is substantial in some patients, minimal in others. Non-adaptive plans may miss the target and/or unnecessarily irradiate normal tissue. Adaptive radiotherapy leads to superior dose-volume metrics but is resource-intensive. The aim of this study was to predict target motion, enabling patient selection and efficient resource allocation.

Materials and methods

Forty cervical cancer patients had CT with full-bladder (CT-FB) and empty-bladder (CT-EB) at planning, and daily cone-beam CTs (CBCTs). The low-risk clinical target volume (CTVLR) was contoured. Mean coverage of the daily CTVLR by the CT-FB CTVLR was calculated for each patient. Eighty-three investigated variables included measures of organ geometry, patient, tumour and treatment characteristics. Models were trained on 29 patients (171 fractions). The Two-CT multivariate model could use all available data. The Single-CT multivariate model excluded data from the CT-EB. A univariate model was trained using the distance moved by the uterine fundus tip between CTs, the only method of patient selection found in published cervix plan-of-the-day studies. Models were tested on 11 patients (68 fractions). Accuracy in predicting mean coverage was reported as mean absolute error (MAE), mean squared error (MSE) and R2.

Results

The Two-CT model was based upon rectal volume, dice similarity coefficient between CT-FB and CT-EB CTVLR, and uterine thickness. The Single-CT model was based upon rectal volume, uterine thickness and tumour size. Both performed better than the univariate model in predicting mean coverage (MAE 7 %, 7 % and 8 %; MSE 82 %2, 65 %2, 110 %2; R2 0.2, 0.4, -0.1).

Conclusion

Uterocervix motion is complex and multifactorial. We present two multivariate models which predicted motion with reasonable accuracy using pre-treatment information, and outperformed the only published method.

Nine years of plan of the day for cervical cancer: Plan library remains effective compared to fully online-adaptive techniques

BACKGROUND AND PURPOSE

Since 2011, our center has been using a library-based Plan-of-the-Day (PotD) strategy for external beam radiotherapy of cervical cancer patients to reduce normal tissue dose while maintaining adequate target coverage. With the advent of fully online-adaptive techniques such as daily online-adaptive replanning, further dose reduction may be possible. However, it is unknown how this reduction relates to plan library approaches, and how the most recent PotD strategies relate to no adaptation. In this study we compare the performance of our current PotD strategy with non-adaptive and fully online-adaptive techniques in terms of target volume size and normal tissue sparing.

MATERIALS AND METHODS

Treatment data of 376 patients treated with the PotD protocol between June 2011 and April 2020 were included. The size of the Planning Target Volumes (PTVs) was reconstructed for different strategies: full online adaptation, no adaptation, and the latest clinical version of the PotD protocol. Normal tissue sparing was estimated by the difference in margin volume to construct the PTV and the volume overlap of the PTV with bladder and rectum.

RESULTS

The current version of our PotD approach reduced the PTV margin volume by a median of 250 cm3 compared to no adaptation. Bladder-PTV overlap decreased from a median of 142 to 71 cm3, and from 39 to 16 cm3 for rectum-PTV. Fully online-adaptive approaches could further decrease the PTV volume by 144 cm3 using a 5 mm margin for residual errors. In this scenario, bladder-PTV overlap was reduced to 35 cm3 and rectum-PTV overlap to 11 cm3.

CONCLUSION

The current version of the PotD protocol is an effective technique to improve normal tissue sparing compared to no adaptation. Further sparing can be achieved using fully online-adaptive techniques, but at the cost of a more complex workflow and with a potentially limited impact. PotD-type protocols can therefore be considered as a suitable alternative to fully online-adaptive approaches.

Empirical analysis of a plan-of-the-day strategy to approximate daily online reoptimization for prostate CBCT-guided adaptive radiotherapy

PURPOSE

Adaptive radiotherapy (ART) can improve the dose delivered to the patient in the presence of anatomic variations. However, the required time, effort, and clinical resources are intensive. This work analyzed a plan-of-the-day (POD) approach on clinical patients treated with online ART to explore implementations that balance dosimetric benefit and clinical resource cost.

METHODS

Eight patients treated to the prostate and proximal seminal vesicles with 26 fractions of CBCT-guided, daily online ART were retrospectively analyzed. With a plan library composed of daily adaptive plans from the initial week of treatment and the original plan, the effect of a POD approach starting the following week was investigated by simulating use of these previously generated plans under 3- and 6-degree-of-freedom patient alignment. The plan selected for each treatment was that from the library that maximized the Dice similarity coefficient of the clinical target volume with that of the current treatment fraction. The resulting distribution of several target coverage and organ-at-risk dose metrics are described relative to those achieved with the daily online reoptimized adaptive technique.

RESULTS

The values of target coverage and organ-at-risk dose metrics varied across patients and metrics. The POD schemas closely approximated the reference values from a fully reoptimized adaptive plan yet required less than 20% of the reoptimization effort. The POD schemas also had a much greater effect on target coverage metrics than 6-degree-of-freedom registration did. Organ-at-risk dose metrics also varied considerably across patients but did not exhibit a consistent dependence on the particular schema.

CONCLUSIONS

POD schemas were able to achieve the vast majority of the dosimetric benefit of daily online ART with a small fraction of the online reoptimization effort. Strategies like this might allow for more practical and strategic implementation of ART so as to benefit a greater number of patients.

Comparison of Ethos template-based planning and AI-based dose prediction: General performance, patient optimality, and limitations

PURPOSE

Ethos proposes a template-based automatic dose planning (Etb) for online adaptive radiotherapy. This study evaluates the general performance of Etb for prostate cancer, as well as the ability to generate patient-optimal plans, by comparing it with another state-of-the-art automatic planning method, i.e., deep learning dose prediction followed by dose mimicking (DP + DM).

MATERIALS

General performances and capability to produce patient-optimal plan were investigated through two studies: Study-S1 generated plans for 45 patients using our initial Ethos clinical goals template (EG_init), and compared them to manually generated plans (MG). For study-S2, 10 patients which showed poor performances at study-S1 were selected. S2 compared the quality of plans generated with four different methods: 1) Ethos initial template (EG_init_selected), 2) Ethos updated template-based on S1 results (EG_upd_selected), 3) DP + DM, and 4) MG plans.

RESULTS

EG_init plans showed satisfactory performance for dose level above 50 Gy: reported mean metrics differences (EG_init minus MG) never exceeded 0.6 %. However, lower dose levels showed loosely optimized metrics, mean differences for V30Gy to rectum and V20Gy to anal canal were of 6.6 % and 13.0 %. EG_init_selected showed amplified differences in V30Gy to rectum and V20Gy to anal canal: 8.5 % and 16.9 %, respectively. These dropped to 5.7 % and 11.5 % for EG_upd_selected plans but strongly increased V60Gy to rectum for 2 patients. DP + DM plans achieved differences of 3.4 % and 4.6 % without compromising any V60Gy.

CONCLUSION

General performances of Etb were satisfactory. However, optimizing with template of goals might be limiting for some complex cases. Over our test patients, DP + DM outperformed the Etb approach.

A feasibility study of adaptive radiation therapy for postprostatectomy prostate cancer

Postoperative prostate radiotherapy requires large planning target volume (PTV) margins to account for motion and deformation of the prostate bed. Adaptive radiation therapy (ART) can incorporate image-guidance data to personalize PTVs that maintain coverage while reducing toxicity. We present feasibility and dosimetry results of a prospective study of postprostatectomy ART. Twenty-one patients were treated with single-adaptation ART. Conventional treatments were delivered for fractions 1 to 6 and adapted plans for the remaining 27 fractions. Clinical target volumes (CTVs) and small bowel delineated on fraction 1 to 4 CBCT were used to generate adapted PTVs and planning organ-at-risk (OAR) volumes for adapted plans. PTV volume and OAR dose were compared between ART and conventional using Wilcoxon signed-rank tests. Weekly CBCT were used to assess the fraction of CTV covered by PTV, CTV D99, and small bowel D1cc. Clinical metrics were compared using a Student's t-test (p < 0.05 significant). Offline adaptive planning required 1.9 ± 0.4 days (mean ± SD). ART decreased mean adapted PTV volume 61 ± 37 cc and bladder wall D50 compared with conventional treatment (p < 0.01). The CTV was fully covered for 96% (97%) of fractions with ART (conventional). Reconstructing dose on weekly CBCT, a nonsignificant reduction in CTV D99 was observed with ART (94%) compared to conventional (96%). Reduced CTV D99 with ART was significantly correlated with large anterior-posterior rectal diameter on simulation CT. ART reduced the number of fractions exceeding our institution's small bowel D1c limit from 14% to 7%. This study has demonstrated the feasibility of offline ART for post-prostatectomy cancer. ART facilitates PTV volume reduction while maintaining reasonable CTV coverage and can reduce the dose to adjacent normal tissues.

Effects of machine learning-based clinical decision support systems on decision-making, care delivery, and patient outcomes: a scoping review

OBJECTIVE

This study aims to summarize the research literature evaluating machine learning (ML)-based clinical decision support (CDS) systems in healthcare settings.

MATERIALS AND METHODS

We conducted a review in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta Analyses extension for Scoping Review). Four databases, including PubMed, Medline, Embase, and Scopus were searched for studies published from January 2016 to April 2021 evaluating the use of ML-based CDS in clinical settings. We extracted the study design, care setting, clinical task, CDS task, and ML method. The level of CDS autonomy was examined using a previously published 3-level classification based on the division of clinical tasks between the clinician and CDS; effects on decision-making, care delivery, and patient outcomes were summarized.

RESULTS

Thirty-two studies evaluating the use of ML-based CDS in clinical settings were identified. All were undertaken in developed countries and largely in secondary and tertiary care settings. The most common clinical tasks supported by ML-based CDS were image recognition and interpretation (n = 12) and risk assessment (n = 9). The majority of studies examined assistive CDS (n = 23) which required clinicians to confirm or approve CDS recommendations for risk assessment in sepsis and for interpreting cancerous lesions in colonoscopy. Effects on decision-making, care delivery, and patient outcomes were mixed.

CONCLUSION

ML-based CDS are being evaluated in many clinical areas. There remain many opportunities to apply and evaluate effects of ML-based CDS on decision-making, care delivery, and patient outcomes, particularly in resource-constrained settings.

Benchmarking Automated Machine Learning-Enhanced Planning With Ethos Against Manual and Knowledge-Based Planning for Locally Advanced Lung Cancer

Purpose

Currently, there is insufficient guidance for standard fractionation lung planning using the Varian Ethos adaptive treatment planning system and its unique intelligent optimization engine. Here, we address this gap in knowledge by developing a methodology to automatically generate high-quality Ethos treatment plans for locally advanced lung cancer.

Methods and Materials

Fifty patients previously treated with manually generated Eclipse plans for inoperable stage IIIA-IIIC non-small cell lung cancer were included in this institutional review board-approved retrospective study. Fifteen patient plans were used to iteratively optimize a planning template for the Daily Adaptive vs Non-Adaptive External Beam Radiation Therapy With Concurrent Chemotherapy for Locally Advanced Non-Small Cell Lung Cancer: A Prospective Randomized Trial of an Individualized Approach for Toxicity Reduction (ARTIA-Lung); the remaining 35 patients were automatically replanned without intervention. Ethos plan quality was benchmarked against clinical plans and reoptimized knowledge-based RapidPlan (RP) plans, then judged using standard dose-volume histogram metrics, adherence to clinical trial objectives, and qualitative review.

Results

Given equal prescription target coverage, Ethos-generated plans showed improved primary and nodal planning target volume V95% coverage (P < .001) and reduced lung gross tumor volume V5 Gy and esophagus D0.03 cc metrics (P ≤ .003) but increased mean esophagus and brachial plexus D0.03 cc metrics (P < .001) compared with RP plans. Eighty percent, 49%, and 51% of Ethos, clinical, and RP plans, respectively, were "per protocol" or met "variation acceptable" ARTIA-Lung planning metrics. Three radiation oncologists qualitatively scored Ethos plans, and 78% of plans were clinically acceptable to all reviewing physicians, with no plans receiving scores requiring major changes.

Conclusions

A standard Ethos template produced lung radiation therapy plans with similar quality to RP plans, elucidating a viable approach for automated plan generation in the Ethos adaptive workspace.

"Scan-(pre)Plan-Treat" Workflow for Bone Metastases Using the Ethos Therapy System: A Single-Center, In Silico Experience

Purpose

To report on the accuracy of automated delineation, treatment plan quality, and duration of an in-silico "scan-(pre)plan-treat" (SPT) workflow for vertebral bone metastases using a 1 × 8 Gy regimen.

Method and Materials

The cloud-based emulator system of the Ethos therapy system was used to adapt an organ-at-risk-sparing preplan created on the diagnostic CT to the anatomy-of-the-day using the cone beam CT made before treatment.

Results

SPT using the Ethos emulator system resulted in relatively good coverage of the PTV and acceptable dose to the OAR. Delivery time and plan homogeneity was the best for 7-field IMRT plan template.

Conclusions

A SPT workflow formula results in a highly conformal treatment delivery while maintaining an acceptable timeframe for the patient on the treatment couch.

Online adaptive radiotherapy for bladder cancer using a simultaneous integrated boost and fiducial markers

PURPOSE

The aim was to assess the feasibility of online adaptive radiotherapy (oART) for bladder cancer using a focal boost by focusing on the quality of the online treatment plan and automatic target delineation, duration of the workflow and performance in the presence of fiducial markers for tumor bed localization.

METHODS

Fifteen patients with muscle invasive bladder cancer received daily oART with Cone Beam CT (CBCT), artificial intelligence (AI)-assisted automatic delineation of the daily anatomy and online plan reoptimization. The bladder and pelvic lymph nodes received a total dose of 40 Gy in 20 fractions, the tumor received an additional simultaneously integrated boost (SIB) of 15 Gy. The dose distribution of the reference plan was calculated for the daily anatomy, i.e. the scheduled plan. Simultaneously, a reoptimization of the plan was performed i.e. the adaptive plan. The target coverage and V95% outside the target were evaluated for both plans. The need for manual adjustments of the GTV delineation, the duration of the workflow and the influence of fiducial markers were assessed.

RESULTS

All 300 adaptive plans met the requirement of the CTV-coverage V95%≥98% for both the boost (55 Gy) and elective volume (40 Gy). For the scheduled plans the CTV-coverage was 53.5% and 98.5%, respectively. Significantly less tissue outside the targets received 55 Gy in case of the adaptive plans as compared to the scheduled plans. Manual corrections of the GTV were performed in 67% of the sessions. In 96% of these corrections the GTV was enlarged and resulted in a median improvement of 1% for the target coverage. The median on-couch time was 22 min. A third of the session time consisted of reoptimization of the treatment plan. Fiducial markers were visible on the CBCTs and aided the tumor localization.

CONCLUSIONS

AI-driven CBCT-guided oART aided by fiducial markers is feasible for bladder cancer radiotherapy treatment including a SIB. The quality of the adaptive plans met the clinical requirements and fiducial markers were visible enabling consistent daily tumor localization. Improved automatic delineation to lower the need for manual corrections and faster reoptimization would result in shorter session time.

Evaluation of the clinical feasibility of cone-beam computed tomography guided online adaption for simulation-free palliative radiotherapy

Background and purpose

Simulation-free radiotherapy, where diagnostic imaging is used for treatment planning, improves accessibility of radiotherapy for eligible palliative patients. Combining this pathway with online adaptive radiotherapy (oART) may improve accuracy of treatment, expanding the number of eligible patients. This study evaluated the adaptive process duration, plan dose volume histogram (DVH) metrics and geometric accuracy of a commercial cone-beam computed tomography (CBCT)-guided oART system for simulation-free, palliative radiotherapy.

Materials and methods

Ten previously treated palliative cases were used to compare system-generated contours against clinician contours in a test environment with Dice Similarity Coefficient (DSC). Twenty simulation-free palliative patients were treated clinically using CBCT-guided oART. Analysis of oART clinical treatment data included; evaluation of the geometric accuracy of system-generated synthetic CT relative to session CBCT anatomy using a Likert scale, comparison of adaptive plan dose distributions to unadapted, using DVH metrics and recording the duration of key steps in the oART workflow.

Results

Auto-generated contours achieved a DSC of higher than 0.85, excluding the stomach which was attributed to CBCT image quality issues. Synthetic CT was locally aligned to CBCT anatomy for approximately 80% of fractions, with the remaining suboptimal yet clinically acceptable. Adaptive plans achieved a median CTV V95% of 99.5%, compared to 95.6% for unadapted. The median overall oART process duration was found to be 13.2 mins, with contour editing being the most time-intensive adaptive step.

Conclusions

The CBCT-guided oART system utilising a simulation-free planning approach was found to be sufficiently accurate for clinical implementation, this may further streamline and improve care for palliative patients.

In silico trial of simulation-free hippocampal-avoidance whole brain adaptive radiotherapy

Background and Purpose

Hippocampal-avoidance whole brain radiotherapy (HA-WBRT) can be a time-consuming process compared to conventional whole brain techniques, thus potentially limiting widespread utilization. Therefore, we evaluated the in silico clinical feasibility, via dose-volume metrics and timing, by leveraging a computed tomography (CT)-based commercial adaptive radiotherapy (ART) platform and workflow in order to create and deliver patient-specific, simulation-free HA-WBRT.

Materials and methods

Ten patients previously treated for central nervous system cancers with cone-beam computed tomography (CBCT) imaging were included in this study. The CBCT was the adaptive image-of-the-day to simulate first fraction on-board imaging. Initial contours defined on the MRI were rigidly matched to the CBCT. Online ART was used to create treatment plans at first fraction. Dose-volume metrics of these simulation-free plans were compared to standard-workflow HA-WBRT plans on each patient CT simulation dataset. Timing data for the adaptive planning sessions were recorded.

Results

For all ten patients, simulation-free HA-WBRT plans were successfully created utilizing the online ART workflow and met all constraints. The median hippocampi D100% was 7.8 Gy (6.6-8.8 Gy) in the adaptive plan vs 8.1 Gy (7.7-8.4 Gy) in the standard workflow plan. All plans required adaptation at first fraction due to both a failing hippocampal constraint (6/10 adaptive fractions) and sub-optimal target coverage (6/10 adaptive fractions). Median time for the adaptive session was 45.2 min (34.0-53.8 min).

Conclusions

Simulation-free HA-WBRT, with commercially available systems, was clinically feasible via plan-quality metrics and timing, in silico.

Reduction of PTV margins for elective pelvic lymph nodes in online adaptive radiotherapy of prostate cancer patients

BACKGROUND

Cone beam CT (CBCT) based online adaptive radiotherapy (oART) is a new development in radiotherapy. With oART, the requirements for planning target volume (PTV) margins differ from standard therapy because motion occurs during a session. In this study, we aim to evaluate a margin reduction for locally advanced prostate patients treated with oART.

MATERIAL AND METHODS

Intrafraction motion of the elective pelvic lymph nodes was evaluated by two radiation therapists (RTTs) for 150 fractions from 10 prostate patients treated with oART. PTV margins of 3, 4 and 5 mm where added to these lymph nodes for all patients. The seven first patients were treated with 5 mm PTV margin, while the last three patients were treated with 4 mm margin. After treatment, the RTTs reviewed the verification CBCTs and evaluated whether the various PTV margins would have covered the adapted clinical target volume, scoring each fraction as approved, inconclusive or rejected. Couch shifts corresponding to the rigid prostate match between the CBCTs were analyzed with respect to the RTT evaluation.

RESULTS

The RTTs approved a 4 mm margin in 95% of the fractions, while 2% of the fractions were rejected. For a 3 mm margin, 57% of the fractions were approved, while 5% were rejected. The scoring from the two RTTs was consistent; e.g., for 3 mm, one RTT approved 58% of the fractions, while the other approved 55%. If the couch was moved less than 2 mm in any direction, 70% of the fractions were approved for a 3 mm margin, compared to 32% for shifts greater than 2 mm.

CONCLUSION

It is safe to reduce the PTV margin from 5 to 4 mm for the elective pelvic lymph nodes for prostate patients treated with oART. Further margin reductions can be motivated for patients presenting little intrafraction motion.

Clinical experiences with online adaptive radiotherapy of vulvar carcinoma

BACKGROUND AND PURPOSE

Radiotherapy for vulvar carcinoma is challenging due to relatively high risk of locoregional disease recurrence, a technically challenging target, and postoperative lymphocele, and a high risk radiation sequelae. We aim to explore, if it is possible to reduce dose to normal tissue, while maintaining CTV coverage for this patient group with online adaptive radiotherapy.

MATERIALS AND METHODS

20 patients with vulvar carcinoma (527 fractions) were treated with online adaptation on a Varian Ethos accelerator. Setup CBCTs were acquired daily for adaptive planning. Verification CBCTs were acquired immediately prior to dose delivery. CTV dose coverage and dose to bladder and rectum were extracted from the scheduled and adapted plans as well as from adapted plans recalculated based on verification CBCTs. In addition, analysis of the decision of the adaptive procedure was performed for 17 patients (465 fractions).

RESULTS

Mean CTV D95% and standard deviation was 98% ± 5% for the scheduled plan compared to 100.0 ± 0.3% and 100.0 ± 0.8% for the adapted plan on the setup and verification CBCT respectively. Dose to OARs varied substantially and did not show any benefit from adaption itself, however a margin reduction was implemented after the first patients treated. The adapted plan was chosen for 63.5% of the fractions and dominant reasons for not adapting were 'no significant dosimetric gain' (75 fractions, 14%) and 'Medical doctor (MD) not available for treatment' (50 fractions, 9.5%). The median adaption time was 15 min and the 25th and 75th percentile was 12 and 17 min, respectively.

CONCLUSION

CTVs and PTVs dose coverage were significantly improved with adaptation compared to image-guided RT. This gain was robust during the treatment time.

Triggering daily online adaptive radiotherapy in the pelvis: Dosimetric effects and procedural implications of trigger parameter-value selection

BACKGROUND

Online adaptive radiotherapy (ART) can address dosimetric consequences of variations in anatomy by creating a new plan during treatment. However, ART is time- and labor-intensive and should be implemented in a resource-conscious way. Adaptive triggers composed of parameter-value pairs may direct the judicious use of online ART.

PURPOSE

This work analyzed our clinical experience using CBCT-based daily online ART to demonstrate how a conceptual framework based on adaptive triggers affects the dosimetric and procedural impact of ART.

METHODS

Sixteen patients across several pelvic sites were treated with CBCT-based daily online ART. Differences in standardized dose metrics were compared between the original plan, the original plan recalculated on the daily anatomy, and an adaptive plan. For each metric, trigger values were analyzed in terms of the proportion of treatments adapted and the distribution of metric values.

RESULTS

Target coverage metrics were compromised due to anatomic variation with the average change per treatment ranging from -0.90 to -0.05 Gy, -0.47 to -0.02 Gy, -0.31 to -0.01 Gy, and -12.45% to -2.65% for PTV D99%, PTV D95%, CTV D99%, and CTV V100%, respectively. These were improved using the adaptive plan (-0.03 to 0.01 Gy, -0.02 to 0.00 Gy, -0.03 to 0.00 Gy, and -4.70% to 0.00%, respectively). Increasingly strict triggers resulted in a non-linear increase in the proportion of treatments adapted and improved the distribution of metric values with diminishing returns. Some organ-at-risk (OAR) metrics were compromised by anatomic variation and improved using the adaptive plan, but changes in most OAR metrics were randomly distributed.

CONCLUSIONS

Daily online ART improved target coverage across multiple pelvic treatment sites and techniques. These effects were larger than those for OAR metrics, suggesting that maintaining target coverage was our primary benefit of CBCT-based daily online ART. Analyses like these can determine online ART triggers from a cost-benefit perspective.

Review of cone beam computed tomography based online adaptive radiotherapy: current trend and future direction

Adaptive radiotherapy (ART) was introduced in the late 1990s to improve the accuracy and efficiency of therapy and minimize radiation-induced toxicities. ART combines multiple tools for imaging, assessing the need for adaptation, treatment planning, quality assurance, and has been utilized to monitor inter- or intra-fraction anatomical variations of the target and organs-at-risk (OARs). Ethos™ (Varian Medical Systems, Palo Alto, CA), a cone beam computed tomography (CBCT) based radiotherapy treatment system that uses artificial intelligence (AI) and machine learning to perform ART, was introduced in 2020. Since then, numerous studies have been done to examine the potential benefits of Ethos™ CBCT-guided ART compared to non-adaptive radiotherapy. This review will explore the current trends of Ethos™, including improved CBCT image quality, a feasible clinical workflow, daily automated contouring and treatment planning, and motion management. Nevertheless, evidence of clinical improvements with the use of Ethos™ are limited and is currently under investigation via clinical trials.

Intentional deep overfit learning for patient-specific dose predictions in adaptive radiotherapy

BACKGROUND

The framework of adaptive radiation therapy (ART) was crafted to address the underlying sources of intra-patient variation that were observed throughout numerous patients' radiation sessions. ART seeks to minimize the consequential dosimetric uncertainty resulting from this daily variation, commonly through treatment planning re-optimization. Re-optimization typically consists of manual evaluation and modification of previously utilized optimization criteria. Ideally, frequent treatment plan adaptation through re-optimization on each day's computed tomography (CT) scan may improve dosimetric accuracy and minimize dose delivered to organs at risk (OARs) as the planning target volume (PTV) changes throughout the course of treatment.

PURPOSE

Re-optimization in its current form is time-consuming and inefficient. In response to this ART bottleneck, we propose a deep learning based adaptive dose prediction model that utilizes a head and neck (H&N) patient's initial planning data to fine-tune a previously trained population model towards a patient-specific model. Our fine-tuned, patient-specific (FT-PS) model, which is trained using the intentional deep overfit learning (IDOL) method, may enable clinicians and treatment planners to rapidly evaluate relevant dosimetric changes daily and re-optimize accordingly.

METHODS

An adaptive population (AP) model was trained using adaptive data from 33 patients. Separately, 10 patients were selected for training FT-PS models. The previously trained AP model was utilized as the base model weights prior to re-initializing model training for each FT-PS model. Ten FT-PS models were separately trained by fine-tuning the previous model weights based on each respective patient's initial treatment plan. From these 10 patients, 26 ART treatment plans were withheld from training as the test dataset for retrospective evaluation of dose prediction performance between the AP and FT-PS models. Each AP and FT-PS dose prediction was compared against the ground truth dose distribution as originally generated during the patient's course of treatment. Mean absolute percent error (MAPE) evaluated the dose differences between a model's prediction and the ground truth.

RESULTS

MAPE was calculated within the 10% isodose volume region of interest for each of the AP and FT-PS models dose predictions and averaged across all test adaptive sessions, yielding 5.759% and 3.747% respectively. MAPE differences were compared between AP and FT-PS models across each test session in a test of statistical significance. The differences were statistically significant in a paired t-test with two-tailed p-value equal to3.851 × 10 - 9 $3.851 \times {10}^{ - 9}$ and 95% confidence interval (CI) equal to [-2.483, -1.542]. Furthermore, MAPE was calculated using each individually segmented structure as an ROI. Nineteen of 24 structures demonstrated statistically significant differences between the AP and FT-PS models.

CONCLUSION

We utilized the IDOL method to fine-tune a population-based dose prediction model into an adaptive, patient-specific model. The averaged MAPE across the test dataset was 5.759% for the population-based model versus 3.747% for the fine-tuned, patient-specific model, and the difference in MAPE between models was found to be statistically significant. Our work demonstrates the feasibility of patient-specific models in adaptive radiotherapy, and offers unique clinical benefit by utilizing initial planning data that contains the physician's treatment intent.

Initial clinical experience building a dual CT- and MR-guided adaptive radiotherapy program

Introduction

Our institution was the first in the world to clinically implement MR-guided adaptive radiotherapy (MRgART) in 2014. In 2021, we installed a CT-guided adaptive radiotherapy (CTgART) unit, becoming one of the first clinics in the world to build a dual-modality ART clinic. Herein we review factors that lead to the development of a high-volume dual-modality ART program and treatment census over an initial, one-year period.

Materials and Methods

The clinical adaptive service at our institution is enabled with both MRgART (MRIdian, ViewRay, Inc, Mountain View, CA) and CTgART (ETHOS, Varian Medical Systems, Palo Alto, CA) platforms. We analyzed patient and treatment information including disease sites treated, radiation dose and fractionation, and treatment times for patients on these two platforms. Additionally, we reviewed our institutional workflow for creating, verifying, and implementing a new adaptive workflow on either platform.

Results

From October 2021 to September 2022, 256 patients were treated with adaptive intent at our institution, 186 with MRgART and 70 with CTgART. The majority (106/186) of patients treated with MRgART had pancreatic cancer, and the most common sites treated with CTgART were pelvis (23/70) and abdomen (20/70). 93.0% of treatments on the MRgART platform were stereotactic body radiotherapy (SBRT), whereas only 72.9% of treatments on the CTgART platform were SBRT. Abdominal gated cases were allotted a longer time on the CTgART platform compared to the MRgART platform, whereas pelvic cases were allotted a shorter time on the CTgART platform when compared to the MRgART platform. Our adaptive implementation technique has led to six open clinical trials using MRgART and seven using CTgART.

Conclusions

We demonstrate the successful development of a dual platform ART program in our clinic. Ongoing efforts are needed to continue the development and integration of ART across platforms and disease sites to maximize access and evidence for this technique worldwide.

Per-fraction planning to enhance optimization degrees of freedom compared to the conventional single-plan approach

Objective. In conventional radiotherapy, a single treatment plan is generated pre-treatment, and delivered in daily fractions. In this study, we propose to generate different treatment plans for all fractions ('Per-fraction' planning) to reduce cumulative organs at risk (OAR) doses. Per-fraction planning was compared to the 'Conventional' single-plan approach for non-coplanar 4 × 9.5 Gy prostate stereotactic body radiation therapy (SBRT).Approach. An in-house application for fully automated, non-coplanar multi-criterial treatment planning with integrated beam angle and fluence optimization was used for plan generations. For the Conventional approach, a single 12-beam non-coplanar IMRT plan with individualized beam angles was generated for each of the 20 included patients. In Per-fraction planning, four fraction plans were generated for each patient. For each fraction, a different set of patient-specific 12-beam configurations could be automatically selected. Per-fraction plans were sequentially generated by adding dose to already generated fraction plan(s). For each fraction, the cumulative- and fraction dose were simultaneously optimized, allowing some minor constraint violations in fraction doses, but not in cumulative.Main results. In the Per-fraction approach, on average 32.9 ± 3.1 [29;39] unique beams per patient were used. PTV doses in the separate Per-fraction plans were acceptable and highly similar to those in Conventional plans, while also fulfilling all OAR hard constraints. When comparing total cumulative doses, Per-fraction planning showed improved bladder sparing for all patients with reductions in Dmean of 22.6% (p= 0.0001) and in D1cc of 2.0% (p= 0.0001), reductions in patient volumes receiving 30% and 50% of the prescribed dose of 54.7% and 6.3%, respectively, and a 3.1% lower rectum Dmean (p= 0.007). Rectum D1cc was 4.1% higher (p= 0.0001) and Urethra dose was similar.Significance. In this proof-of-concept paper, Per-fraction planning resulted in several dose improvements in healthy tissues compared to the Conventional single-plan approach, for similar PTV dose. By keeping the number of beams per fraction the same as in Conventional planning, reported dosimetric improvements could be obtained without increase in fraction durations. Further research is needed to explore the full potential of the Per-fraction planning approach.

DAART: a deep learning platform for deeply accelerated adaptive radiation therapy for lung cancer

Purpose

The study aimed to implement a novel, deeply accelerated adaptive radiation therapy (DAART) approach for lung cancer radiotherapy (RT). Lung cancer is the most common cause of cancer-related death, and RT is the preferred medically inoperable treatment for early stage non-small cell lung cancer (NSCLC). In the current lengthy workflow, it takes a median of four weeks from diagnosis to RT treatment, which can result in complete restaging and loss of local control with delay. We implemented the DAART approach, featuring a novel deepPERFECT system, to address unwanted delays between diagnosis and treatment initiation.

Materials and methods

We developed a deepPERFECT to adapt the initial diagnostic imaging to the treatment setup to allow initial RT planning and verification. We used data from 15 patients with NSCLC treated with RT to train the model and test its performance. We conducted a virtual clinical trial to evaluate the treatment quality of the proposed DAART for lung cancer radiotherapy.

Results

We found that deepPERFECT predicts planning CT with a mean high-intensity fidelity of 83 and 14 HU for the body and lungs, respectively. The shape of the body and lungs on the synthesized CT was highly conformal, with a dice similarity coefficient (DSC) of 0.91, 0.97, and Hausdorff distance (HD) of 7.9 mm, and 4.9 mm, respectively, compared with the planning CT scan. The tumor showed less conformality, which warrants acquisition of treatment Day1 CT and online adaptive RT. An initial plan was designed on synthesized CT and then adapted to treatment Day1 CT using the adapt to position (ATP) and adapt to shape (ATS) method. Non-inferior plan quality was achieved by the ATP scenario, while all ATS-adapted plans showed good plan quality.

Conclusion

DAART reduces the common online ART (ART) treatment course by at least two weeks, resulting in a 50% shorter time to treatment to lower the chance of restaging and loss of local control.

Implementing cone-beam computed tomography-guided online adaptive radiotherapy in cervical cancer

Background and purpose

Adaptive radiotherapy (ART) in locally advanced cervical cancer (LACC) has shown promising outcomes. This study investigated the feasibility of cone-beam computed tomography (CBCT)-guided online ART (oART) for the treatment of LACC.

Material and methods

The quality of the automated radiotherapy treatment plans and artificial intelligence (AI)-driven contour delineation for LACC on a novel CBCT-guided oART system were assessed. Dosimetric analysis of 200 simulated oART sessions were compared with standard treatment. Feasibility of oART was assessed from the delivery of 132 oART fractions for the first five clinical LACC patients. The simulated and live oART sessions compared a fixed planning target volume (PTV) margin of 1.5 cm around the uterus-cervix clinical target volume (CTV) with an internal target volume-based approach. Workflow timing measurements were recorded.

Results

The automatically-generated 12-field intensity-modulated radiotherapy plans were comparable to manually generated plans. The AI-driven organ-at-risk (OAR) contouring was acceptable requiring, on average, 12.3 min to edit, with the bowel performing least well and rated as unacceptable in 16 % of cases. The treated patients demonstrated a mean PTV D98% (+/-SD) of 96.7 (+/- 0.2)% for the adapted plans and 94.9 (+/- 3.7)% for the non-adapted scheduled plans (p<10^-5). The D2cc (+/-SD) for the bowel, bladder and rectum were reduced by 0.07 (+/- 0.03)Gy, 0.04 (+/-0.05)Gy and 0.04 (+/-0.03)Gy per fraction respectively with the adapted plan (p <10^-5). In the live.setting, the mean oART session (+/-SD) from CBCT acquisition to beam-on was 29 +/- 5 (range 21-44) minutes.

Conclusion

CBCT-guided oART was shown to be feasible with dosimetric benefits for patients with LACC. Further work to analyse potential reductions in PTV margins is ongoing.

Segmentation by test-time optimization for CBCT-based adaptive radiation therapy

PURPOSE

Online adaptive radiotherapy (ART) requires accurate and efficient auto-segmentation of target volumes and organs-at-risk (OARs) in mostly cone-beam computed tomography (CBCT) images, which often have severe artifacts and lack soft-tissue contrast, making direct segmentation very challenging. Propagating expert-drawn contours from the pretreatment planning CT through traditional or deep learning (DL)-based deformable image registration (DIR) can achieve improved results in many situations. Typical DL-based DIR models are population based, that is, trained with a dataset for a population of patients, and so they may be affected by the generalizability problem.

METHODS

In this paper, we propose a method called test-time optimization (TTO) to refine a pretrained DL-based DIR population model, first for each individual test patient, and then progressively for each fraction of online ART treatment. Our proposed method is less susceptible to the generalizability problem and thus can improve overall performance of different DL-based DIR models by improving model accuracy, especially for outliers. Our experiments used data from 239 patients with head-and-neck squamous cell carcinoma to test the proposed method. First, we trained a population model with 200 patients and then applied TTO to the remaining 39 test patients by refining the trained population model to obtain 39 individualized models. We compared each of the individualized models with the population model in terms of segmentation accuracy.

RESULTS

The average improvement of the Dice similarity coefficient (DSC) and 95% Hausdorff distance (HD95) of segmentation can be up to 0.04 (5%) and 0.98 mm (25%), respectively, with the individualized models compared to the population model over 17 selected OARs and a target of 39 patients. Although the average improvement may seem mild, we found that the improvement for outlier patients with structures of large anatomical changes is significant. The number of patients with at least 0.05 DSC improvement or 2 mm HD95 improvement by TTO averaged over the 17 selected structures for the state-of-the-art architecture VoxelMorph is 10 out of 39 test patients. By deriving the individualized model using TTO from the pretrained population model, TTO models can be ready in about 1 min. We also generated the adapted fractional models for each of the 39 test patients by progressively refining the individualized models using TTO to CBCT images acquired at later fractions of online ART treatment. When adapting the individualized model to a later fraction of the same patient, the model can be ready in less than a minute with slightly improved accuracy.

CONCLUSIONS

The proposed TTO method is well suited for online ART and can boost segmentation accuracy for DL-based DIR models, especially for outlier patients where the pretrained models fail.

A survey of practice patterns for adaptive particle therapy for interfractional changes

Background and purpose

Anatomical changes may compromise the planned target coverage and organs-at-risk dose in particle therapy. This study reports on the practice patterns for adaptive particle therapy (APT) to evaluate current clinical practice and wishes and barriers to further implementation.

Materials and methods

An institutional questionnaire was distributed to PT centres worldwide (7/2020-6/2021) asking which type of APT was used, details of the workflow, and what the wishes and barriers to implementation were. Seventy centres from 17 countries participated. A three-round Delphi consensus analysis (10/2022) among the authors followed to define recommendations on required actions and future vision.

Results

Out of the 68 clinically operational centres, 84% were users of APT for at least one treatment site with head and neck being most common. APT was mostly performed offline with only two online APT users (plan-library). No centre used online daily re-planning. Daily 3D imaging was used for APT by 19% of users. Sixty-eight percent of users had plans to increase their use or change their technique for APT. The main barrier was "lack of integrated and efficient workflows". Automation and speed, reliable dose deformation for dose accumulation and higher quality of in-room volumetric imaging were identified as the most urgent task for clinical implementation of online daily APT.

Conclusion

Offline APT was implemented by the majority of PT centres. Joint efforts between industry research and clinics are needed to translate innovations into efficient and clinically feasible workflows for broad-scale implementation of online APT.

A roadmap for implementation of kV-CBCT online adaptive radiation therapy and initial first year experiences

PURPOSE

Online Adaptive Radiation Therapy (oART) follows a different treatment paradigm than conventional radiotherapy, and because of this, the resources, implementation, and workflows needed are unique. The purpose of this report is to outline our institution's experience establishing, organizing, and implementing an oART program using the Ethos therapy system.

METHODS

We include resources used, operational models utilized, program creation timelines, and our institutional experiences with the implementation and operation of an oART program. Additionally, we provide a detailed summary of our first year's clinical experience where we delivered over 1000 daily adaptive fractions. For all treatments, the different stages of online adaption, primary patient set-up, initial kV-CBCT acquisition, contouring review and edit of influencer structures, target review and edits, plan evaluation and selection, Mobius3D 2nd check and adaptive QA, 2nd kV-CBCT for positional verification, treatment delivery, and patient leaving the room, were analyzed.

RESULTS

We retrospectively analyzed data from 97 patients treated from August 2021-August 2022. One thousand six hundred seventy seven individual fractions were treated and analyzed, 632(38%) were non-adaptive and 1045(62%) were adaptive. Seventy four of the 97 patients (76%) were treated with standard fractionation and 23 (24%) received stereotactic treatments. For the adaptive treatments, the generated adaptive plan was selected in 92% of treatments. On average(±std), adaptive sessions took 34.52 ± 11.42 min from start to finish. The entire adaptive process (from start of contour generation to verification CBCT), performed by the physicist (and physician on select days), was 19.84 ± 8.21 min.

CONCLUSION

We present our institution's experience commissioning an oART program using the Ethos therapy system. It took us 12 months from project inception to the treatment of our first patient and 12 months to treat 1000 adaptive fractions. Retrospective analysis of delivered fractions showed that the average overall treatment time was approximately 35 min and the average time for the adaptive component of treatment was approximately 20 min.

Impact of Adapted Radiotherapy Schedules on Bowel Sparing in Node-Positive Cervical Cancer

PURPOSE

Definitive radiation therapy (RT) for locally advanced node-positive cervical cancer confers significant toxicity to pelvic organs including the small bowel. Gross nodal disease exhibits significant shrinkage during RT, and yet conventional RT does not account for this change. We evaluated the reduction in absorbed bowel dose using various adaptive RT schedules.

METHODS AND MATERIALS

We obtained 130 evaluable scans (computed tomography simulation and 25 cone beam computed tomography scans per patient) of 5 patients who had received definitive external beam RT for lymph node positive cervical cancer daily over 5 weeks. Using a single universal volumetric modulated arc therapy plan with predefined optimization priorities, we created adapted RT plans in 4 schedules: Daily, Weekly, Twice, and NoAdapt (mimicking conventional nonadapted RT). The in silico (computer modeled) patients were treated to 45 Gy to primary cervical disease with a simultaneous integrated boost to 55 Gy to involved lymph nodes. We evaluated dose metrics including D2cc, D15cc, and V45 to determine the impact of adapted RT schedules on bowel sparing. Statistical tests included the Student t test, analysis of variance, and the Spearman rank correlation.

RESULTS

The quantity of reduced bowel dose was significantly associated with the chosen planning schedule in all evaluated metrics and was proportional to the frequency of adaptive RT with significant moderate-to-strong monotonicity. Both D2cc and D15cc were reduced an average of 2.7 Gy using daily replanning compared with a nonadapted approach. A minimally adapted strategy of only 2 replans also confers a significant dosimetric benefit over a nonadapted approach. Reduced standard deviations of D2cc and V45 bowel doses over the treatment courses were significantly associated with the choice of planning schedule with strong monotonicity.

CONCLUSIONS

All adaptive RT schedules evaluated confer significant dosimetric advantages in bowel sparing over a conventional nonadapted technique, with greater sparing seen with more frequent replanning schedules. These findings warrant future trials of adaptive RT for pelvic malignancies.

Adaptive radiotherapy for breast cancer

Research in the field of local and locoregional breast cancer radiotherapy aims to maintain excellent oncological outcomes while reducing treatment-related toxicity. Adaptive radiotherapy (ART) considers variations in target and organs at risk (OARs) anatomy occurring during the treatment course and integrates these in re-optimized treatment plans. Exploiting ART routinely in clinic may result in smaller target volumes and better OAR sparing, which may lead to reduction of acute as well as late toxicities. In this review MR-guided and CT-guided ART for breast cancer patients according to different clinical scenarios (neoadjuvant and adjuvant partial breast irradiation, whole breast, chest wall and regional nodal irradiation) are reviewed and their advantages as well as challenging aspects discussed.

Carpe Diem: Making the Most of Plan-of-the-Day for Cervical Cancer Radiation Therapy

PURPOSE

Radiation therapy is the key treatment for locally advanced cervical cancer. Organ motion presents a challenge to accurate targeting of external beam radiation therapy. The plan-of-the-day (PotD) adaptive approach is therefore an attractive option. We present our experience and the procedural steps required to implement PotD for cervix cancer.

METHODS AND MATERIALS

We reviewed relevant studies on organ motion and adaptive radiation therapy identified through a literature search and cross referencing. These included 10 dosimetric and 3 quality of life studies directly assessing the PotD approach to radiation therapy in cervix cancer.

RESULTS

Studies show improvements in target coverage and reduction of dose received by normal tissues and suggest improved toxicity. Clinical implementation of PotD has been slow because of a number of difficulties and uncertainties, which we discuss with the aim of helping teams to implement PotD at their center.

CONCLUSIONS

The PotD approach improves dosimetry and may improve toxicity. We describe a framework to assist with practical implementation.

Visual analysis of image-guided radiation therapy based on bibliometrics: A review

Radiation therapy plays an important role in tumor treatment. The development of image-guided radiation therapy (IGRT) technology provides a strong guarantee for precise radiation therapy of tumors. However, bibliometric studies on IGRT research have rarely been reported. This study uses literature collected from the Web of Science during 1987 to 2021 as a sample and uses the bibliometric method to reveal the current research status, hotspots, and development trends in IGRT. Based on 6407 papers published from the Web of Science during 1987 to 2021, we utilized Microsoft Excel 2007 and cite space software to perform statistical analysis and visualization of IGRT. A total of 6407 articles were included, this area of IGRT has gone through 4 stages: budding period, growth period, outbreak period, and stationary period. The research category is mainly distributed in Radiology Nuclear Medicine Medical Imaging, which intersects with the research categories of Materials, Physics, and Mathematics. Yin FF, Tanderup K, and Sonke JJ are highly productive scholars who are active in IGRT research, while Jaffray DA, van Herk M and Guckenberger M are authors with high impact in this field. The team of scholars has close cooperation within the team and weak cooperation among teams. The League of European Research Universities, University of Texas System, University of Toronto, and Princess Margaret Cancer are the main research institutions in this field. The United States has the most research literature, followed by China and Germany. Six thousand four hundred seven articles are distributed in 712 journals, and the top 3 journals are Med Phys, Int J Radiat Oncol, and Radiather Oncol. Precise registration, intelligence, magnetic resonance guidance, and deep learning are current research hotspots. These results demonstrate that the research in this field is relatively mature and fruitful in the past 35 years, providing a solid theoretical basis and practical experience for precision radiotherapy.