On the acceptance, commissioning, and quality assurance of electron FLASH units

Background & Purpose

FLASH or ultra-high dose rate (UHDR) radiation therapy (RT) has gained attention in recent years for its ability to spare normal tissues relative to conventional dose rate (CDR) RT in various preclinical trials. However, clinical implementation of this promising treatment option has been limited because of the lack of availability of accelerators capable of delivering UHDR RT. Commercial options are finally reaching the market that produce electron beams with average dose rates of up to 1000 Gy/s. We established a framework for the acceptance, commissioning, and periodic quality assurance (QA) of electron FLASH units and present an example of commissioning.

Methods

A protocol for acceptance, commissioning, and QA of UHDR linear accelerators was established by combining and adapting standards and professional recommendations for standard linear accelerators based on the experience with UHDR at four clinical centers that use different UHDR devices. Non-standard dosimetric beam parameters considered included pulse width, pulse repetition frequency, dose per pulse, and instantaneous dose rate, together with recommendations on how to acquire these measurements.

Results

The 6- and 9-MeV beams of an UHDR electron device were commissioned by using this developed protocol. Measurements were acquired with a combination of ion chambers, beam current transformers (BCTs), and dose-rate-independent passive dosimeters. The unit was calibrated according to the concept of redundant dosimetry using a reference setup.

Conclusions

This study provides detailed recommendations for the acceptance testing, commissioning, and routine QA of low-energy electron UHDR linear accelerators. The proposed framework is not limited to any specific unit, making it applicable to all existing eFLASH units in the market. Through practical insights and theoretical discourse, this document establishes a benchmark for the commissioning of UHDR devices for clinical use.

Proof-of-concept for a thin conical X-ray target optimized for intensity and directionality for use in a carbon nanotube-based compact X-ray tube

BACKGROUND

Carbon nanotube-based cold cathode technology has revolutionized the miniaturization of X-ray tubes. However, current applications of these devices required optimization for large, uniform fields with low intensity.

PURPOSE

This work investigated the feasibility and radiological characteristics of a novel conical X-ray target optimized for high intensity and high directionality to be used in a compact X-ray tube.

METHODS

The proposed device uses an ultrathin, conical tungsten-diamond target that exhibits significant heat loading while maintaining a small focal spot size and promoting forward-directedness of the X-ray field through preferential attenuation of oblique-angled photons. The electrostatic and thermal properties of the theoretical tube were calculated and analyzed using COMSOL Multiphysics software. The production, transport, and calculation of radiological properties associated with the resultant X-ray field were performed using the Geant4 toolkit via its wrapper, TOPAS.

RESULTS

Heat transfer analysis of this X-ray tube demonstrated the feasibility of a 200-kV electron beam bombarding the proposed target at a maximum current of 100 mA using a 1-ms symmetric duty cycle. The cathode of the X-ray tube was designed to be segmented into nine switchable electrical segments for modulation of the focal spot size from 0.4- to 10.8-mm. After importing the COMSOL-derived electron beam into TOPAS for X-ray production simulations, radiological analysis of the resultant field demonstrated high levels of intrinsic beam collimation while maintaining high intensity. A maximum dose rate of 17,887 cGy/min was calculated for 1-mm depth in water at 7-cm distance.

CONCLUSIONS

The proposed X-ray tube design can create highly directional X-ray fields with superior fluence compared to that of current commercial X-ray tubes of comparable size.

Quantifying the Effect of 4-Dimensional Computed Tomography-Based Deformable Dose Accumulation on Representing Radiation Damage for Patients with Locally Advanced Non-Small Cell Lung Cancer Treated with Standard-Fractionated Intensity-Modulated Radiation Therapy

PURPOSE

The aim of this study was to investigate the dosimetric and clinical effects of 4-dimensional computed tomography (4DCT)-based longitudinal dose accumulation in patients with locally advanced non-small cell lung cancer treated with standard-fractionated intensity-modulated radiation therapy (IMRT).

METHODS AND MATERIALS

Sixty-seven patients were retrospectively selected from a randomized clinical trial. Their original IMRT plan, planning and verification 4DCTs, and ∼4-month posttreatment follow-up CTs were imported into a commercial treatment planning system. Two deformable image registration algorithms were implemented for dose accumulation, and their accuracies were assessed. The planned and accumulated doses computed using average-intensity images or phase images were compared. At the organ level, mean lung dose and normal-tissue complication probability (NTCP) for grade ≥2 radiation pneumonitis were compared. At the region level, mean dose in lung subsections and the volumetric overlap between isodose intervals were compared. At the voxel level, the accuracy in estimating the delivered dose was compared by evaluating the fit of a dose versus radiographic image density change (IDC) model. The dose-IDC model fit was also compared for subcohorts based on the magnitude of NTCP difference (|ΔNTCP|) between planned and accumulated doses.

RESULTS

Deformable image registration accuracy was quantified, and the uncertainty was considered for the voxel-level analysis. Compared with planned doses, accumulated doses on average resulted in <1-Gy lung dose increase and <2% NTCP increase (up to 8.2 Gy and 18.8% for a patient, respectively). Volumetric overlap of isodose intervals between the planned and accumulated dose distributions ranged from 0.01 to 0.93. Voxel-level dose-IDC models demonstrated a fit improvement from planned dose to accumulated dose (pseudo-R2 increased 0.0023) and a further improvement for patients with ≥2% |ΔNTCP| versus for patients with <2% |ΔNTCP|.

CONCLUSIONS

With a relatively large cohort, robust image registrations, multilevel metric comparisons, and radiographic image-based evidence, we demonstrated that dose accumulation more accurately represents the delivered dose and can be especially beneficial for patients with greater longitudinal response.

Operational Ontology for Oncology (O3): A Professional Society-Based, Multistakeholder, Consensus-Driven Informatics Standard Supporting Clinical and Research Use of Real-World Data From Patients Treated for Cancer

PURPOSE

The ongoing lack of data standardization severely undermines the potential for automated learning from the vast amount of information routinely archived in electronic health records (EHRs), radiation oncology information systems, treatment planning systems, and other cancer care and outcomes databases. We sought to create a standardized ontology for clinical data, social determinants of health, and other radiation oncology concepts and interrelationships.

METHODS AND MATERIALS

The American Association of Physicists in Medicine's Big Data Science Committee was initiated in July 2019 to explore common ground from the stakeholders' collective experience of issues that typically compromise the formation of large inter- and intra-institutional databases from EHRs. The Big Data Science Committee adopted an iterative, cyclical approach to engaging stakeholders beyond its membership to optimize the integration of diverse perspectives from the community.

RESULTS

We developed the Operational Ontology for Oncology (O3), which identified 42 key elements, 359 attributes, 144 value sets, and 155 relationships ranked in relative importance of clinical significance, likelihood of availability in EHRs, and the ability to modify routine clinical processes to permit aggregation. Recommendations are provided for best use and development of the O3 to 4 constituencies: device manufacturers, centers of clinical care, researchers, and professional societies.

CONCLUSIONS

O3 is designed to extend and interoperate with existing global infrastructure and data science standards. The implementation of these recommendations will lower the barriers for aggregation of information that could be used to create large, representative, findable, accessible, interoperable, and reusable data sets to support the scientific objectives of grant programs. The construction of comprehensive "real-world" data sets and application of advanced analytical techniques, including artificial intelligence, holds the potential to revolutionize patient management and improve outcomes by leveraging increased access to information derived from larger, more representative data sets.

Operational Ontology for Radiation Oncology (OORO): A Professional Society-Based, Multi-Stakeholder Consensus Driven Informatics Standard Supporting Clinical and Research Use of Real-World Data

PURPOSE/OBJECTIVE(S)

There is a critical need for large-scale, multi-institutional "real-world" data to evaluate patient, diagnosis and treatment factors affecting oncology patient outcomes. However, lack of data standardization undermines the potential for automated learning from the vast amount of information routinely archived in electronic health records (EHRs), Radiation Oncology Information Systems and other cancer care databases. As next step to promote data standardization beyond the American Association of Physicists in Medicine (AAPM)'s TG-263 guidance for radiotherapy (RT) nomenclature, the AAPM's Big Data Subcommittee (BDSC) has led an international RT professional society collaboration to develop the Operational Ontology for Radiation Oncology (OORO).

MATERIALS/METHODS

Initiated July 2019 to explore issues that typically compromise formation of large inter- and intra- institutional databases from EHRs, the AAPM's BDSC membership includes representatives from the AAPM, American Society of Radiation Oncology (ASTRO), Canadian Organization of Medical Physicists (COMP), Canadian Association of Radiation Oncology (CARO), European Society of Therapeutic Radiation Oncology (ESTRO) and clinical trials experts from NRG Oncology. Multiple external stakeholders were engaged, including government agencies, vendors and RT community members through the iterative and consensus-driven approach to OORO development.

RESULTS

The OORO includes 42 key elements, 359 attributes, 144 value sets, and 155 relationships, ranked for priority of implementation based on clinical significance, likelihood of availability in EHRs, or ability to modify routine clinical processes to permit aggregation. The initial version of OORO includes many disease-site independent concepts common for all cancer patients and a smaller set specific for prostate cancer. The OORO development methodology is currently being applied/adapted to include additional disease site-specific concepts beginning with head and neck cancers.

CONCLUSION

The first of its kind in radiation oncology, the OORO is a professional society-based, multi-stakeholder, consensus driven informatics standard. The iterative and collaborative approach to ontology development and refinement aims to ensure that OORO serves as a « living » guidance document, facilitating incremental expansion of data elements over time, as disease site-specific standards are set and RT concepts evolve. Supporting construction of comprehensive "real-world" datasets and application of advanced analytic techniques, including artificial intelligence (AI), OORO holds the potential to revolutionize patient management and improve outcomes.

Quantification of the role of lead foil in flattening filter free beam reference dosimetry

PURPOSE

To quantify the potential error in outputs for flattening filter free (FFF) beams associated with use of a lead foil in beam quality determination per the addendum protocol for TG-51, we examined differences in measurements of the beam quality conversion factor k_Q when using or not using lead foil.

METHODS

Two FFF beams, a 6 MV FFF and a 10 MV FFF, were calibrated on eight Varian TrueBeams and two Elekta Versa HD linear accelerators (linacs) according to the TG-51 addendum protocol by using Farmer ionization chambers [TN 30013 (PTW) and SNC600c (Sun Nuclear)] with traceable absorbed dose-to-water calibrations. In determining k_Q , the percentage depth-dose at 10 cm [PDD(10)] was measured with 10×10 cm² field size at 100 cm source-to-surface distance (SSD). PDD(10) values were measured either with a 1 mm lead foil positioned in the path of the beam [%dd(10)_Pb ] or with omission of a lead foil [%dd(10)]. The %dd(10)x values were then calculated and the k_Q factors determined by using the empirical fit equation in the TG-51 addendum for the PTW 30013 chambers. A similar equation was used to calculate k_Q for the SNC600c chamber, with the fitting parameters taken from a very recent Monte Carlo study. The differences in k_Q factors were compared for with lead foil vs. without lead foil.

RESULTS

Differences in %dd(10)x with lead foil and with omission of lead foil were 0.9 ± 0.2% for the 6 MV FFF beam and 0.6 ± 0.1% for the 10 MV FFF beam. Differences in k_Q values with lead foil and with omission of lead foil were -0.1 ± 0.02% for the 6 MV FFF and -0.1 ± 0.01% for the 10 MV FFF beams.

CONCLUSION

With evaluation of the lead foil role in determination of the k_Q factor for FFF beams. Our results suggest that the omission of lead foil introduces approximately 0.1% of error for reference dosimetry of FFF beams on both TrueBeam and Versa platforms.

Optimization of mesh generation for geometric accuracy, robustness, and efficiency of biomechanical-model-based deformable image registration

BACKGROUND

Successful generation of biomechanical-model-based deformable image registration (BM-DIR) relies on user-defined parameters that dictate surface mesh quality. The trial-and-error process to determine the optimal parameters can be labor-intensive and hinder DIR efficiency and clinical workflow.

PURPOSE

To identify optimal parameters in surface mesh generation as boundary conditions for a BM-DIR in longitudinal liver and lung CT images to facilitate streamlined image registration processes.

METHODS

Contrast-enhanced CT images of 29 colorectal liver cancer patients and end-exhale four-dimensional CT images of 26 locally advanced non-small cell lung cancer patients were collected. Different combinations of parameters that determine the triangle mesh quality (voxel side length and triangle edge length) were investigated. The quality of DIRs generated using these parameters was evaluated with metrics for geometric accuracy, robustness, and efficiency. Metrics for geometric accuracy included target registration error (TRE) of internal vessel bifurcations, dice similar coefficient (DSC), mean distance to agreement (MDA), Hausdorff distance (HD) for organ contours, and number of vertices in the triangle mesh. American Association of Physicists in Medicine Task Group 132 was used to ensure parameters met TRE, DSC, MDA recommendations before the comparison among the parameters. Robustness was evaluated as the success rate of DIR generation, and efficiency was evaluated as the total time to generate boundary conditions and compute finite element analysis.

RESULTS

Voxel side length of 0.2 cm and triangle edge length of 3 were found to be the optimal parameters for both liver and lung, with success rate of 1.00 and 0.98 and average DIR computation time of 100 and 143 s, respectively. For this combination, the average TRE, DSC, MDA, and HD were 0.38-0.40, 0.96-0.97, 0.09-0.12, and 0.87-1.17 mm, respectively.

CONCLUSION

The optimal parameters were found for the analyzed patients. The decision-making process described in this study serves as a recommendation for BM-DIR algorithms to be used for liver and lung. These parameters can facilitate consistence in the evaluation of published studies and more widespread utilization of BM-DIR in clinical practice.

Head and Neck Radiation Therapy Patterns of Practice Variability Identified as a Challenge to Real-World Big Data: Results From the Learning from Analysis of Multicentre Big Data Aggregation (LAMBDA) Consortium

Purpose

Outside of randomized clinical trials, it is difficult to develop clinically relevant evidence-based recommendations for radiation therapy (RT) practice guidelines owing to lack of comprehensive real-world data. To address this knowledge gap, we formed the Learning from Analysis of Multicenter Big Data Aggregation consortium to cooperatively implement RT data standardization, develop software solutions for data analysis, and recommend clinical practice change based on real-world data analyzed. The first phase of this "Big Data" study aimed at characterizing variability in clinical practice patterns of dosimetric data for organs at risk (OARs) that would undermine subsequent use of large-scale, electronically aggregated data to characterize associations with outcomes. Evidence from this study was used as the basis for practical recommendations to improve data quality.

Methods and Materials

Dosimetric details of patients with head and neck cancer treated with radiation therapy between 2014 and 2019 were analyzed. Institutional patterns of practice were characterized, including structure nomenclature, volumes, and frequency of contouring. Dose volume histogram (DVH) distributions were characterized and compared with institutional constraints and literature values.

Results

Plans for 4664 patients treated to a mean plan dose of 64.4 ± 13.2 Gy in 32 ± 4 fractions were aggregated. Before implementation of TG-263 guidelines in each institution, there was variability in OAR nomenclature across institutions and structures. With evidence from this study, we identified a targeted and practical set of recommendations aimed at improving the quality of real-world data.

Conclusions

Quantifying similarities and differences among institutions for OAR structures and DVH metrics is the launching point for next steps to investigate potential relationships between DVH parameters and patient outcomes.

Leveraging deep learning-based segmentation and contours-driven deformable registration for dose accumulation in abdominal structures

PURPOSE

Discrepancies between planned and delivered dose to GI structures during radiation therapy (RT) of liver cancer may hamper the prediction of treatment outcomes. The purpose of this study is to develop a streamlined workflow for dose accumulation in a treatment planning system (TPS) during liver image-guided RT and to assess its accuracy when using different deformable image registration (DIR) algorithms.

MATERIALS AND METHODS

Fifty-six patients with primary and metastatic liver cancer treated with external beam radiotherapy guided by daily CT-on-rails (CTOR) were retrospectively analyzed. The liver, stomach and duodenum contours were auto-segmented on all planning CTs and daily CTORs using deep-learning methods. Dose accumulation was performed for each patient using scripting functionalities of the TPS and considering three available DIR algorithms based on: (i) image intensities only; (ii) intensities + contours; (iii) a biomechanical model (contours only). Planned and accumulated doses were converted to equivalent dose in 2Gy (EQD2) and normal tissue complication probabilities (NTCP) were calculated for the stomach and duodenum. Dosimetric indexes for the normal liver, GTV, stomach and duodenum and the NTCP values were exported from the TPS for analysis of the discrepancies between planned and the different accumulated doses.

RESULTS

Deep learning segmentation of the stomach and duodenum enabled considerable acceleration of the dose accumulation process for the 56 patients. Differences between accumulated and planned doses were analyzed considering the 3 DIR methods. For the normal liver, stomach and duodenum, the distribution of the 56 differences in maximum doses (D2%) presented a significantly higher variance when a contour-driven DIR method was used instead of the intensity only-based method. Comparing the two contour-driven DIR methods, differences in accumulated minimum doses (D98%) in the GTV were >2Gy for 15 (27%) of the patients. Considering accumulated dose instead of planned dose in standard NTCP models of the duodenum demonstrated a high sensitivity of the duodenum toxicity risk to these dose discrepancies, whereas smaller variations were observed for the stomach.

CONCLUSION

This study demonstrated a successful implementation of an automatic workflow for dose accumulation during liver cancer RT in a commercial TPS. The use of contour-driven DIR methods led to larger discrepancies between planned and accumulated doses in comparison to using an intensity only based DIR method, suggesting a better capability of these approaches in estimating complex deformations of the GI organs.

Interinstitutional beam model portability study in a mixed vendor environment

A 6 MV flattened beam model for a Varian TrueBeamSTx c‐arm treatment delivery system in RayStation, developed and validated at one institution, was implemented and validated at another institution. The only parameter value adjustments were to accommodate machine output at the second institution. Validation followed MPPG 5.a. recommendations, with particular attention paid to IMRT and VMAT deliveries. With this minimal adjustment, the model passed validation across a broad spectrum of treatment plans, measurement devices, and staff who created the test plans and executed the measurements. This work demonstrates the possibility of using a single template model in the same treatment planning system with matched machines in a mixed vendor environment.

Geometric and dosimetric accuracy of deformable image registration between average-intensity images for 4DCT-based adaptive radiotherapy for non-small cell lung cancer

PURPOSE

Re-planning for four-dimensional computed tomography (4DCT)-based lung adaptive radiotherapy commonly requires deformable dose mapping between the planning average-intensity image (AVG) and the newly acquired AVG. However, such AVG-AVG deformable image registration (DIR) lacks accuracy assessment. The current work quantified and compared geometric accuracies of AVG-AVG DIR and corresponding phase-phase DIRs, and subsequently investigated the clinical impact of such AVG-AVG DIR on deformable dose mapping.

METHODS AND MATERIALS

Hybrid intensity-based AVG-AVG and phase-phase DIRs were performed between the planning and mid-treatment 4DCTs of 28 non-small cell lung cancer patients. An automated landmark identification algorithm detected vessel bifurcation pairs in both lungs. Target registration error (TRE) of these landmark pairs was calculated for both DIR types. The correlation between TRE and respiratory-induced landmark motion in the planning 4DCT was analyzed. Global and local dose metrics were used to assess the clinical implications of AVG-AVG deformable dose mapping with both DIR types.

RESULTS

TRE of AVG-AVG and phase-phase DIRs averaged 3.2 ± 1.0 and 2.6 ± 0.8 mm respectively (p < 0.001). Using AVG-AVG DIR, TREs for landmarks with <10 mm motion averaged 2.9 ± 2.0 mm, compared to 3.1 ± 1.9 mm for the remaining landmarks (p < 0.01). Comparatively, no significant difference was demonstrated for phase-phase DIRs. Dosimetrically, no significant difference in global dose metrics was observed between doses mapped with AVG-AVG DIR and the phase-phase DIR, but a positive linear relationship existed (p = 0.04) between the TRE of AVG-AVG DIR and local dose difference.

CONCLUSIONS

When the region of interest experiences <10 mm respiratory-induced motion, AVG-AVG DIR may provide sufficient geometric accuracy; conversely, extra attention is warranted, and phase-phase DIR is recommended. Dosimetrically, the differences in geometric accuracy between AVG-AVG and phase-phase DIRs did not impact global lung-based metrics. However, as more localized dose metrics are needed for toxicity assessment, phase-phase DIR may be required as its lower mean TRE improved voxel-based dosimetry.

Clinical implementation of automated treatment planning for whole-brain radiotherapy

The purpose of the study was to develop and clinically deploy an automated, deep learning‐based approach to treatment planning for whole‐brain radiotherapy (WBRT). We collected CT images and radiotherapy treatment plans to automate a beam aperture definition from 520 patients who received WBRT. These patients were split into training (n = 312), cross‐validation (n = 104), and test (n = 104) sets which were used to train and evaluate a deep learning model. The DeepLabV3+ architecture was trained to automatically define the beam apertures on lateral‐opposed fields using digitally reconstructed radiographs (DRRs).

For the beam aperture evaluation, 1st quantitative analysis was completed using a test set before clinical deployment and 2nd quantitative analysis was conducted 90 days after clinical deployment. The mean surface distance and the Hausdorff distances were compared in the anterior‐inferior edge between the clinically used and the predicted fields. Clinically used plans and deep‐learning generated plans were evaluated by various dose–volume histogram metrics of brain, cribriform plate, and lens.

The 1st quantitative analysis showed that the average mean surface distance and Hausdorff distance were 7.1 mm (±3.8 mm) and 11.2 mm (±5.2 mm), respectively, in the anterior–inferior edge of the field. The retrospective dosimetric comparison showed that brain dose coverage (D99%, D95%, D1%) of the automatically generated plans was 29.7, 30.3, and 32.5 Gy, respectively, and the average dose of both lenses was up to 19.0% lower when compared to the clinically used plans. Following the clinical deployment, the 2nd quantitative analysis showed that the average mean surface distance and Hausdorff distance between the predicted and clinically used fields were 2.6 mm (±3.2 mm) and 4.5 mm (±5.6 mm), respectively.

In conclusion, the automated patient‐specific treatment planning solution for WBRT was implemented in our clinic. The predicted fields appeared consistent with clinically used fields and the predicted plans were dosimetrically comparable.

Stereotactic ablative radiotherapy in operable stage I NSCLC patients: Long-term results of the expanded STARS clinical trial

8506

Background: We published a pooled analysis of 2 randomized trials (STARS/ROSEL) that compared lobectomy with mediastinal lymph node dissection (L-MLND) vs stereotactic ablative radiotherapy (SABR) in operable stage I NSCLC. There were no significant differences in disease progression but significantly higher 3-year overall survival (OS) in the SABR arm (95% vs 79%). Owing to concerns regarding the small sample size (n = 58), short follow-up (3 years), and non-uniform use of video-assisted thoracoscopic surgery (VATS), we expanded the STARS protocol to a single-arm SABR trial with a protocol-specified comparison to a published, longitudinally-followed institutional cohort of stage IA NSCLC status post VATS L-MLND (n = 229). Methods: Inclusion criteria were stage IA NSCLC (≤3 cm, N0M0 and staged by PET/CT with EBUS) with Zubrod performance status (PS) 0-2, baseline FEV1 > 40% and DLCO > 40% and deemed operable by a multidisciplinary team. SABR utilized 4-dimensional CT simulation and volumetric image guidance; 54 Gy in 3 fractions were delivered to planning target volumes (PTVs) located peripherally, or 50 Gy in 4 fractions to more central PTVs. All patients were followed by chest CT every three months for the first two years, every 6 months for another three years, and then annually. Non-inferiority of SABR could be claimed if the 3-year OS was not lower than the historical VATS L-MLND cohort by more than 12%. We conducted a risk-factor matched comparison study of the primary outcome between the SABR and the historical VATS L-MLND. Results: The median follow-up among the 80 SABR patients was 61 months (range, 34-79 months). The OS and progression-free survival (PFS) were 91% (95% CI: 85̃98%) and 80% (95% CI: 72̃89%) at 3 years, and 87% (95% CI: 79̃95%) and 77% (95% CI: 68̃87%) at 5 years, respectively. The 5-year cumulative incidence rate counting death as competing risk was 6.3% (95% CI: 2.3̃13.2%) local, 12.5% (95% CI: 6.4̃20.8%) regional, and 8.8% (95% CI: 3.8̃16.2%) distant (any recurrence 17.6% (95% CI: 10.1̃26.7%)). The 5 year cumulative incidence rate of second lung primary was 6.9% (95% CI: 2.5̃14.6%). There were 1.3% grade 3 and no grade 4-5 toxicities. The propensity score matched (age, gender, tumor size, histology, PS) comparison of SABR vs VATS L-MLND revealed no significant differences in PFS (p = 0.063), lung cancer-specific survival (p = 0.075), or cumulative incidence rates of local (p = 0.54), regional (p = 0.97), or distant failures (p = 0.33). The SABR arm was associated with significantly higher OS (91% vs 82% at 3 years and 87% vs 72% at 5 years; p = 0.012 from log-rank test). The hazard ratio was 0.411 (95% CI: 0.193̃0.875; p = 0.021). Conclusions: The long-term OS and PFS of SABR is not inferior to VATS L-MLND for operable stage IA NSCLC. SABR remains a promising approach for this population, but multidisciplinary management is strongly recommended. Clinical trial information: NCT02357992.

Report of Task Group 201 of the American Association of Physicists in Medicine: Quality management of external beam therapy data transfer

With the advancement of data-intensive technologies, such as image-guided radiation therapy (IGRT) and intensity-modulated radiation therapy (IMRT), the amount and complexity of data to be transferred between clinical subsystems have increased beyond the reach of manual checking. As a result, unintended treatment deviations (e.g., dose errors) may occur if the treatment system is not closely monitored by a comprehensive data transfer quality management program (QM). This report summarizes the findings and recommendations from the task group (TG) on quality assurance (QA) of external beam treatment data transfer (TG-201), with the aim to assist medical physicists in designing their own data transfer QM. As a background, a section of this report describes various models of data flow (distributed data repositories and single data base systems) and general data test characteristics (data integrity, interpretation, and consistency). Recommended tests are suggested based on the collective experience of TG-201 members. These tests are for the acceptance of, commissioning of, and upgrades to subsystems that store and/or modify clinical treatment data. As treatment complexity continues to evolve, we will need to do and know more about ensuring the quality of data transfers. The report concludes with the recommendation to move toward data transfer open standards compatibility and to develop tools that automate data transfer QA.

Initial Feasibility and Clinical Implementation of Daily MR-Guided Adaptive Head and Neck Cancer Radiation Therapy on a 1.5T MR-Linac System: Prospective R-IDEAL 2a/2b Systematic Clinical Evaluation of Technical Innovation

PURPOSE

This prospective study is, to our knowledge, the first report of daily adaptive radiation therapy (ART) for head and neck cancer (HNC) using a 1.5T magnetic resonance imaging-linear accelerator (MR-linac) with particular focus on safety and feasibility and dosimetric results of an online rigid registration-based adapt to position (ATP) workflow.

METHODS AND MATERIALS

Ten patients with HNC received daily ART on a 1.5T/7MV MR-linac, 6 using ATP only and 4 using ATP with 1 offline adapt-to-shape replan. Setup variability with custom immobilization masks was assessed by calculating the mean systematic error (M), standard deviation of the systematic error (Σ), and standard deviation of the random error (σ) of the isocenter shifts. Quality assurance was performed with a cylindrical diode array using 3%/3 mm γ criteria. Adaptive treatment plans were summed for each patient to compare the delivered dose with the planned dose from the reference plan. The impact of dosimetric variability between adaptive fractions on the summation plan doses was assessed by tracking the number of optimization constraint violations at each individual fraction.

RESULTS

The random errors (mm) for the x, y, and z isocenter shifts, respectively, were M = -0.3, 0.7, 0.1; Σ = 3.3, 2.6, 1.4; and σ = 1.7, 2.9, 1.0. The median (range) γ pass rate was 99.9% (90.9%-100%). The differences between the reference and summation plan doses were -0.61% to 1.78% for the clinical target volume and -11.74% to 8.11% for organs at risk (OARs), although an increase greater than 2% in OAR dose only occurred in 3 cases, each for a single OAR. All cases had at least 2 fractions with 1 or more constraint violations. However, in nearly all instances, constraints were still met in the summation plan despite multiple single-fraction violations.

CONCLUSIONS

Daily ART on a 1.5T MR-linac using an online ATP workflow is safe and clinically feasible for HNC and results in delivered doses consistent with planned doses.

Use of Multi-Site Radiation Therapy for Systemic Disease Control

Metastatic cancer is a heterogeneous entity, some of which could benefit from local consolidative radiation therapy (RT). Although randomized evidence is growing in support of using RT for oligometastatic disease, a highly active area of investigation relates to whether RT could benefit patients with polymetastatic disease. This article highlights the preclinical and clinical rationale for using RT for polymetastatic disease, proposes an exploratory framework for selecting patients best suited for these types of treatments, and briefly reviews potential challenges. The goal of this hypothesis-generating review is to address personalized multimodality systemic treatment for patients with metastatic cancer. The rationale for using high-dose RT is primarily for local control and immune activation in either oligometastatic or polymetastatic disease. However, the primary application of low-dose RT is to activate distinct antitumor immune pathways and modulate the tumor stroma in efforts to better facilitate T cell infiltration. We explore clinical cases involving high- and low-dose RT to demonstrate the potential efficacy of such treatment. We then group patients by extent of disease burden to implement high- and/or low-dose RT. Patients with low-volume disease may receive high-dose RT to all sites as part of an oligometastatic paradigm. Subjects with high-volume disease (for whom standard of care remains palliative RT only) could be treated with a combination of high-dose RT to a few sites for immune activation, while receiving low-dose RT to several remaining lesions to enhance systemic responses from high-dose RT and immunotherapy. We further discuss how emerging but speculative concepts such as immune function may be integrated into this approach and examine therapies currently under investigation that may help address immune deficiencies. The review concludes by addressing challenges in using RT for polymetastatic disease, such as concerns about treatment planning workflows, treatment times, dose constraints for multiple-isocenter treatments, and economic considerations.

Tissue-specific deformable image registration using a spatial-contextual filter

Intensity-based deformable registration with spatial-invariant regularization generally fails when distinct motion exists across different types of tissues. The purpose of this work was to develop and validate a new regularization approach for deformable image registration that is tissue-specific and able to handle motion discontinuities. Our approach was built upon a Demons registration framework, and used the image context supplementing the original spatial constraint to regularize displacement vector fields in iterative image registration process. The new regularization was implemented as a spatial-contextual filter, which favors the motion vectors within the same tissue type but penalizes the motion vectors from different tissues. This approach was validated using five public lung cancer patients, each with 300 landmark pairs identified by a thoracic radiation oncologist. The mean and standard deviation of the landmark registration errors were 1.3 ± 0.8 mm, compared with those of 2.3 ± 2.9 mm using the original Demons algorithm. Particularly, for the case with the largest initial landmark displacement of 15 ± 9 mm, the modified Demons algorithm had a registration error of 1.3 ± 1.1 mm, while the original Demons algorithm had a registration error of 3.6 ± 5.9 mm. We also qualitatively evaluated the modified Demons algorithm using two difficult cases in our routine clinic: one lung case with large sliding motion and one head and neck case with large anatomical changes in air cavity. Visual evaluation on the deformed image created by the deformable image registration showed that the modified Demons algorithm achieved reasonable registration accuracy, but the original Demons algorithm produced distinct registration errors.

Automatic Verification of Beam Apertures for Cervical Cancer Radiation Therapy

PURPOSE

Automated tools can help identify radiation treatment plans of unacceptable quality. To this end, we developed a quality verification technique to automatically verify the clinical acceptability of beam apertures for 4-field box treatments of patients with cervical cancer. By comparing the beam apertures to be used for treatment with a secondary set of beam apertures developed automatically, this quality verification technique can flag beam apertures that may need to be edited to be acceptable for treatment.

METHODS AND MATERIALS

The automated methodology for creating verification beam apertures uses a deep learning model trained on beam apertures and digitally reconstructed radiographs from 255 clinically acceptable planned treatments (as rated by physicians). These verification apertures were then compared with the treatment apertures using spatial comparison metrics to detect unacceptable treatment apertures. We tested the quality verification technique on beam apertures from 80 treatment plans. Each plan was rated by physicians, where 57 were rated clinically acceptable and 23 were rated clinically unacceptable.

RESULTS

Using various comparison metrics (the mean surface distance, Hausdorff distance, and Dice similarity coefficient) for the 2 sets of beam apertures, we found that treatment beam apertures rated acceptable had significantly better agreement with the verification beam apertures than those rated unacceptable (P < .01). Upon receiver operating characteristic analysis, we found the area under the curve for all metrics to be 0.89 to 0.95, which demonstrated the high sensitivity and specificity of our quality verification technique.

CONCLUSIONS

We found that our technique of automatically verifying the beam aperture is an effective tool for flagging potentially unacceptable beam apertures during the treatment plan review process. Accordingly, we will clinically deploy this quality verification technique as part of a fully automated treatment planning tool and automated plan quality assurance program.