Numerical optimization of longitudinal collimator geometry for novel x-ray field.

Objective. A novel x-ray field produced by an ultrathin conical target is described in the literature. However, the optimal design for an associated collimator remains ambiguous. Current optimization methods using Monte Carlo calculations restrict the efficiency and robustness of the design process. A more generic optimization method that reduces parameter constraints while minimizing computational load is necessary. A numerical method for optimizing the longitudinal collimator hole geometry for a cylindrically-symmetrical x-ray tube is demonstrated and compared to Monte Carlo calculations.Approach. The x-ray phase space was modelled as a four-dimensional histogram differential in photon initial position, final position, and photon energy. The collimator was modeled as a stack of thin washers with varying inner radii. Simulated annealing was employed to optimize this set of inner radii according to various objective functions calculated on the photon flux at a specified plane.Main results. The analytical transport model used for optimization was validated against Monte Carlo calculations using Geant4 via its wrapper, TOPAS. Optimized collimators and the resulting photon flux profiles are presented for three focal spot sizes and five positions of the source. Optimizations were performed with multiple objective functions based on various weightings of precision, intensity, and field flatness metrics. Finally, a select set of these optimized collimators, plus a parallel-hole collimator for comparison, were modeled in TOPAS. The evolution of the radiation field profiles are presented for various positions of the source for each collimator.Significance. This novel optimization strategy proved consistent and robust across the range of x-ray tube settings regardless of the optimization starting point. Common collimator geometries were re-derived using this algorithm while simultaneously optimizing geometry-specific parameters. The advantages of this strategy over iterative Monte Carlo-based techniques, including computational efficiency, radiation source-specificity, and solution flexibility, make it a desirable optimization method for complex irradiation geometries.

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.

Deep learning-based automatic segmentation of cardiac substructures for lung cancers.

PURPOSE: Accurate and comprehensive segmentation of cardiac substructures is crucial for minimizing the risk of radiation-induced heart disease in lung cancer radiotherapy. We sought to develop and validate deep learning-based auto-segmentation models for cardiac substructures. MATERIALS AND METHODS: Nineteen cardiac substructures (whole heart, 4 heart chambers, 6 great vessels, 4 valves, and 4 coronary arteries) in 100 patients treated for non-small cell lung cancer were manually delineated by two radiation oncologists. The valves and coronary arteries were delineated as planning risk volumes. An nnU-Net auto-segmentation model was trained, validated, and tested on this dataset with a split ratio of 75:5:20. The auto-segmented contours were evaluated by comparing them with manually drawn contours in terms of Dice similarity coefficient (DSC) and dose metrics extracted from clinical plans. An independent dataset of 42 patients was used for subjective evaluation of the auto-segmentation model by 4 physicians. RESULTS: The average DSCs were 0.95 (+/- 0.01) for the whole heart, 0.91 (+/- 0.02) for 4 chambers, 0.86 (+/- 0.09) for 6 great vessels, 0.81 (+/- 0.09) for 4 valves, and 0.60 (+/- 0.14) for 4 coronary arteries. The average absolute errors in mean/max doses to all substructures were 1.04 (+/- 1.99) Gy and 2.20 (+/- 4.37) Gy. The subjective evaluation revealed that 94% of the auto-segmented contours were clinically acceptable. CONCLUSION: We demonstrated the effectiveness of our nnU-Net model for delineating cardiac substructures, including coronary arteries. Our results indicate that this model has promise for studies regarding radiation dose to cardiac substructures.

Stereotactic ablative radiotherapy with or without immunotherapy for early-stage or isolated lung parenchymal recurrent node-negative non-small-cell lung cancer: an open-label, randomised, phase 2 trial.

BACKGROUND: Stereotactic ablative radiotherapy (SABR) is the standard treatment for medically inoperable early-stage non-small-cell lung cancer (NSCLC), but regional or distant relapses, or both, are common. Immunotherapy reduces recurrence and improves survival in people with stage III NSCLC after chemoradiotherapy, but its utility in stage I and II cases is unclear. We therefore conducted a randomised phase 2 trial of SABR alone compared with SABR with immunotherapy (I-SABR) for people with early-stage NSCLC. METHODS: We did an open-label, randomised, phase 2 trial comparing SABR to I-SABR, conducted at three different hospitals in TX, USA. People aged 18 years or older with histologically proven treatment-naive stage IA-IB (tumour size ≤4 cm, N0M0), stage IIA (tumour size ≤5 cm, N0M0), or stage IIB (tumour size >5 cm and ≤7 cm, N0M0) as per the American Joint Committee on Cancer version 8 staging system or isolated parenchymal recurrences (tumour size ≤7 cm) NSCLC (TanyNanyM0 before definitive surgery or chemoradiotherapy) were included in this trial. Participants were randomly assigned (1:1; using the Pocock & Simon method) to receive SABR with or without four cycles of nivolumab (480 mg, once every 4 weeks, with the first dose on the same day as, or within 36 h after, the first SABR fraction). This trial was unmasked. The primary endpoint was 4-year event-free survival (local, regional, or distant recurrence; second primary lung cancer; or death). Analyses were both intention to treat (ITT) and per protocol. This trial is registered with ClinicalTrials.gov (NCT03110978) and is closed to enrolment. FINDINGS: From June 30, 2017, to March 22, 2022, 156 participants were randomly assigned, and 141 participants received assigned therapy. At a median 33 months' follow-up, I-SABR significantly improved 4-year event-free survival from 53% (95% CI 42-67%) with SABR to 77% (66-91%; per-protocol population, hazard ratio [HR] 0·38; 95% CI 0·19-0·75; p=0·0056; ITT population, HR 0·42; 95% CI 0·22-0·80; p=0·0080). There were no grade 3 or higher adverse events associated with SABR. In the I-SABR group, ten participants (15%) had grade 3 immunologial adverse events related to nivolumab; none had grade 3 pneumonitis or grade 4 or higher toxicity. INTERPRETATION: Compared with SABR alone, I-SABR significantly improved event-free survival at 4 years in people with early-stage treatment-naive or lung parenchymal recurrent node-negative NSCLC, with tolerable toxicity. I-SABR could be a treatment option in these participants, but further confirmation from a number of currently accruing phase 3 trials is required. FUNDING: Bristol-Myers Squibb and MD Anderson Cancer Center Alliance, National Cancer Institute at the National Institutes of Health through Cancer Center Core Support Grant and Clinical and Translational Science Award to The University of Texas MD Anderson Cancer Center.

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.

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 kQ 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 kQ , 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 kQ 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 kQ for the SNC600c chamber, with the fitting parameters taken from a very recent Monte Carlo study. The differences in kQ 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 kQ 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 kQ 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.

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.

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.

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.

The potential of biology-guided radiation therapy in thoracic cancer: A preliminary treatment planning study.

Purpose: We investigated the feasibility of biology-guided radiotherapy (BgRT), a technique that utilizes real-time positron emission imaging to minimize tumor motion uncertainties, to spare nearby organs at risk. Methods: Volumetric modulated arc therapy (VMAT), intensity-modulated proton (IMPT) therapy, and BgRT plans were created for a paratracheal node recurrence (case 1; 60 Gy in 10 fractions) and a primary peripheral left upper lobe adenocarcinoma (case 2; 50 Gy in four fractions). Results: For case 1, BgRT produced lower bronchus V40 values compared to VMAT and IMPT. For case 2, total lung V20 was lower in the BgRT case compared to VMAT and IMPT. Conclusions: BgRT has the potential to reduce the radiation dose to proximal critical structures but requires further detailed investigation.

An Automated Treatment Planning Framework for Spinal Radiation Therapy and Vertebral-Level Second Check.

PURPOSE: Complicating factors such as time pressures, anatomic variants in the spine, and similarities in adjacent vertebrae are associated with incorrect level treatments of the spine. The purpose of this work was to mitigate such challenges by fully automating the treatment planning process for diagnostic and simulation computed tomography (CT) scans. METHODS AND MATERIALS: Vertebral bodies are labeled on CT scans of any length using 2 intendent deep-learning models-mirroring 2 different experts labeling the spine. Then, a U-Net++ architecture was trained, validated, and tested to contour each vertebra (n = 220 CT scans). Features from the CT and auto-contours were input into a random forest classifier to predict whether vertebrae were correctly labeled. This classifier was trained using auto-contours from cone beam computed tomography, positron emission tomography/CT, simulation CT, and diagnostic CT images (n = 56 CT scans, 751 contours). Auto-plans were generated via scripting. Each model was combined into a framework to make a fully automated clinical tool. A retrospective planning study was conducted in which 3 radiation oncologists scored auto-plan quality on an unseen patient cohort (n = 60) on a 5-point scale. CT scans varied in scan length, presence of surgical implants, imaging protocol, and metastatic burden. RESULTS: The results showed that the uniquely designed convolutional neural networks accurately labeled and segmented vertebral bodies C1-L5 regardless of imaging protocol or metastatic burden. Mean dice-similarity coefficient was 85.0% (cervical), 90.3% (thoracic), and 93.7% (lumbar). The random forest classifier predicted mislabeling across various CT scan types with an area under the curve of 0.82. All contouring and labeling errors within treatment regions (11 of 11), including errors from patient plans with atypical anatomy (eg, T13, L6) were detected. Radiation oncologists scored 98% of simulation CT-based plans and 92% of diagnostic CT-based plans as clinically acceptable or needing minor edits for patients with typical anatomy. On average, end-to-end treatment planning time of the clinical tool was less than 8 minutes. CONCLUSIONS: This novel method to automatically verify, contour, and plan palliative spine treatments is efficient and effective across various CT scan types. Furthermore, it is the first to create a clinical tool that can automatically verify vertebral level in CT images.

Adding customized electron energy beams to TrueBeam linear accelerators.

PURPOSE: To better meet clinical needs and facilitate optimal treatment planning, we added two new electron energy beams (7 and 11 MeV) to two Varian TrueBeam linacs. METHODS: We worked with the vendor to create two additional customized electron energies without hardware modifications. For each beam, we set the bending magnet current and then optimized other beam-specific parameters to achieve depths of 50% ionization (I50 ) of 2.9 cm for 7 MeV and 4.2 cm for the 11 MeV beam with the 15 × 15 cm2 cone at 100 cm source-to-surface distance (SSD) by using an ionization chamber profiler (ICP) with a double-wedge (DW) phantom. Beams were steered and balanced to optimize symmetry with the ICP. After all parameters were set, full commissioning was done including measuring beam profiles, percent depth doses (PDDs), output factors (OFs) at standard, and extended SSDs. Measured data were compared between the two linacs and against the values calculated by our RayStation treatment planning system (TPS) following Medical Physics Practice Guideline 5.a (MPPG 5.a) guidelines. RESULTS: The I50 values initially determined with the ICP/DW agreed with those from a PDD-scanned in-water phantom within 0.2 mm for the 7 and 11 MeV on both linacs. Comparison of the beam characteristics from the two linacs indicated that flatness and symmetry agreed within 0.4%, and point-by-point differences in PDD were within 0.01% ± 0.3% for the 7 MeV and 0.01% ± 0.3% for the 11 MeV. The OF ratios between the two linacs were 1.000 ± 0.007 for the 7 MeV and 1.004 ± 0.007 for the 11 MeV. Agreement between TPS-calculated outputs and measurements were -0.1% ± 1.0% for the 7 MeV and 0.2% ± 0.8% for the 11 MeV. All other parameters met the MPPG 5.a's 3%/3-mm criteria. CONCLUSION: We were able to add two new beam energies with no hardware modifications. Tuning of the new beams was facilitated by the ICP/DW system allowing us to have the procedures done in a few hours and achieve highly consistent results across two linacs. PACS numbers: 87.55.Qr, 87.56.Fc.

Using Failure Mode and Effects Analysis to Evaluate Risk in the Clinical Adoption of Automated Contouring and Treatment Planning Tools.

PURPOSE: In this study, we applied the failure mode and effects analysis (FMEA) approach to an automated radiation therapy contouring and treatment planning tool to assess, and subsequently limit, the risk of deploying automated tools. METHODS AND MATERIALS: Using an FMEA, we quantified the risks associated with the Radiation Planning Assistant (RPA), an automated contouring and treatment planning tool currently under development. A multidisciplinary team identified and scored each failure mode, using a combination of RPA plan data and experience for guidance. A 1-to-10 scale for severity, occurrence, and detectability of potential errors was used, following American Association of Physicists in Medicine Task Group 100 recommendations. High-risk failure modes were further explored to determine how the workflow could be improved to reduce the associated risk. RESULTS: Of 290 possible failure modes, we identified 126 errors that were unique to the RPA workflow, with a mean risk priority number (RPN) of 56.3 and a maximum RPN of 486. The top 10 failure modes were caused by automation bias, operator error, and software error. Twenty-one failure modes were above the action threshold of RPN = 125, leading to corrective actions. The workflow was modified to simplify the user interface and better training resources were developed, which highlight the importance of thorough review of the output of automated systems. After the changes, we rescored the high-risk errors, resulting in a final mean and maximum RPN of 33.7 and 288, respectively. CONCLUSIONS: We identified 126 errors specific to the automated workflow, most of which were caused by automation bias or operator error, which emphasized the need to simplify the user interface and ensure adequate user training. As a result of changes made to the software and the enhancement of training resources, the RPNs subsequently decreased, showing that FMEA is an effective way to assess and reduce risk associated with the deployment of automated planning tools.

Body region-specific 3D age-scaling functions for scaling whole-body computed tomography anatomy for pediatric late effects studies.

Purpose.Radiation epidemiology studies of childhood cancer survivors treated in the pre-computed tomography (CT) era reconstruct the patients' treatment fields on computational phantoms. For such studies, the phantoms are commonly scaled to age at the time of radiotherapy treatment because age is the generally available anthropometric parameter. Several reference size phantoms are used in such studies, but reference size phantoms are only available at discrete ages (e.g.: newborn, 1, 5, 10, 15, and Adult). When such phantoms are used for RT dose reconstructions, the nearest discrete-aged phantom is selected to represent a survivor of a specific age. In this work, we (1) conducted a feasibility study to scale reference size phantoms at discrete ages to various other ages, and (2) evaluated the dosimetric impact of using exact age-scaled phantoms as opposed to nearest age-matched phantoms at discrete ages.Methods.We have adopted the University of Florida/National Cancer Institute (UF/NCI) computational phantom library for our studies. For the feasibility study, eight male and female reference size UF/NCI phantoms (5, 10, 15, and 35 years) were downscaled to fourteen different ages which included next nearest available lower discrete ages (1, 5, 10 and 15 years) and the median ages at the time of RT for Wilms' tumor (3.9 years), craniospinal (8.0 years), and all survivors (9.1 years old) in the Childhood Cancer Survivor Study (CCSS) expansion cohort treated with RT. The downscaling was performed using our in-house age scaling functions (ASFs). To geometrically validate the scaling, Dice similarity coefficient (DSC), mean distance to agreement (MDA), and Euclidean distance (ED) were calculated between the scaled and ground-truth discrete-aged phantom (unscaled UF/NCI) for whole-body, brain, heart, liver, pancreas, and kidneys. Additionally, heights of the scaled phantoms were compared with ground-truth phantoms' height, and the Centers for Disease Control and Prevention (CDC) reported 50th percentile height. Scaled organ masses were compared with ground-truth organ masses. For the dosimetric assessment, one reference size phantom and seventeen body-size dependent 5-year-old phantoms (9 male and 8 female) of varying body mass indices (BMI) were downscaled to 3.9-year-old dimensions for two different radiation dose studies. For the first study, we simulated a 6 MV photon right-sided flank field RT plan on a reference size 5-year-old and 3.9-year-old (both of healthy BMI), keeping the field size the same in both cases. Percent of volume receiving dose ≥15 Gy (V15) and the mean dose were calculated for the pancreas, liver, and stomach. For the second study, the same treatment plan, but with patient anatomy-dependent field sizes, was simulated on seventeen body-size dependent 5- and 3.9-year-old phantoms with varying BMIs. V15, mean dose, and minimum dose received by 1% of the volume (D1), and by 95% of the volume (D95) were calculated for pancreas, liver, stomach, left kidney (contralateral), right kidney, right and left colons, gallbladder, thoracic vertebrae, and lumbar vertebrae. A non-parametric Wilcoxon rank-sum test was performed to determine if the dose to organs of exact age-scaled and nearest age-matched phantoms were significantly different (p < 0.05).Results.In the feasibility study, the best DSCs were obtained for the brain (median: 0.86) and whole-body (median: 0.91) while kidneys (median: 0.58) and pancreas (median: 0.32) showed poorer agreement. In the case of MDA and ED, whole-body, brain, and kidneys showed tighter distribution and lower median values as compared to other organs. For height comparison, the overall agreement was within 2.8% (3.9 cm) and 3.0% (3.2 cm) of ground-truth UF/NCI and CDC reported 50th percentile heights, respectively. For mass comparison, the maximum percent and absolute differences between the scaled and ground-truth organ masses were within 31.3% (29.8 g) and 211.8 g (16.4%), respectively (across all ages). In the first dosimetric study, absolute difference up to 6% and 1.3 Gy was found for V15and mean dose, respectively. In the second dosimetric study, V15and mean dose were significantly different (p < 0.05) for all studied organs except the fully in-beam organs. D1and D95were not significantly different for most organs (p > 0.05).Conclusion.We have successfully evaluated our ASFs by scaling UF/NCI computational phantoms from one age to another age, which demonstrates the feasibility of scaling any CT-based anatomy. We have found that dose to organs of exact age-scaled and nearest aged-matched phantoms are significantly different (p < 0.05) which indicates that using the exact age-scaled phantoms for retrospective dosimetric studies is a better approach.

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.

Stereotactic ablative radiotherapy for operable stage I non-small-cell lung cancer (revised STARS): long-term results of a single-arm, prospective trial with prespecified comparison to surgery.

BACKGROUND: A previous pooled analysis of the STARS and ROSEL trials showed higher survival after stereotactic ablative radiotherapy (SABR) than with surgery for operable early-stage non-small-cell lung cancer (NSCLC), but that analysis had notable limitations. This study reports long-term results of the revised STARS trial, in which the SABR group was re-accrued with a larger sample size, along with a protocol-specified propensity-matched comparison with a prospectively registered, contemporary institutional cohort of patients who underwent video-assisted thoracoscopic surgical lobectomy with mediastinal lymph node dissection (VATS L-MLND). METHODS: This single-arm prospective trial was done at the University of Texas MD Anderson Cancer Center (Houston, TX, USA) and enrolled patients aged 18 years or older with a Zubrod performance status of 0-2, newly diagnosed and histologically confirmed NSCLC with N0M0 disease (squamous cell, adenocarcinoma, large cell, or NSCLC not otherwise specified), and a tumour diameter of 3 cm or less. This trial did not include patients from the previous pooled analysis. SABR dosing was 54 Gy in three fractions (for peripheral lesions) or 50 Gy in four fractions (for central tumours; simultaneous integrated boost to gross tumour totalling 60 Gy). The primary endpoint was the 3-year overall survival. For the propensity-matching analysis, we used a surgical cohort from the MD Anderson Department of Thoracic and Cardiovascular Surgery's prospectively registered, institutional review board-approved database of all patients with clinical stage I NSCLC who underwent VATS L-MLND during the period of enrolment in this trial. Non-inferiority could be claimed if the 3-year overall survival rate after SABR was lower than that after VATS L-MLND by 12% or less and the upper bound of the 95% CI of the hazard ratio (HR) was less than 1·965. Propensity matching consisted of determining a propensity score using a multivariable logistic regression model including several covariates (age, tumour size, histology, performance status, and the interaction of age and sex); based on the propensity scores, one patient in the SABR group was randomly matched with one patient in the VATS L-MLND group using a 5:1 digit greedy match algorithm. This study is registered with ClinicalTrials.gov, NCT02357992. FINDINGS: Between Sept 1, 2015, and Jan 31, 2017, 80 patients were enrolled and included in efficacy and safety analyses. Median follow-up time was 5·1 years (IQR 3·9-5·8). Overall survival was 91% (95% CI 85-98) at 3 years and 87% (79-95) at 5 years. SABR was tolerated well, with no grade 4-5 toxicity and one (1%) case each of grade 3 dyspnoea, grade 2 pneumonitis, and grade 2 lung fibrosis. No serious adverse events were recorded. Overall survival in the propensity-matched VATS L-MLND cohort was 91% (95% CI 85-98) at 3 years and 84% (76-93) at 5 years. Non-inferiority was claimed since the 3-year overall survival after SABR was not lower than that observed in the VATS L-MLND group. There was no significant difference in overall survival between the two patient cohorts (hazard ratio 0·86 [95% CI 0·45-1·65], p=0·65) from a multivariable analysis. INTERPRETATION: Long-term survival after SABR is non-inferior to VATS L-MLND for operable stage IA NSCLC. SABR remains promising for such cases but multidisciplinary management is strongly recommended. FUNDING: Varian Medical Systems and US National Cancer Institute (National Institutes of Health).

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.

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.

Beam energy metrics for the acceptance and quality assurance of Halcyon linear accelerator.

PURPOSE: Establish and compare two metrics for monitoring beam energy changes in the Halcyon platform and evaluate the accuracy of these metrics across multiple Halcyon linacs. METHOD: The first energy metric is derived from the diagonal normalized flatness (FDN ), which is defined as the ratio of the average measurements at a fixed off-axis equal distance along the open profiles in two diagonals to the measurement at the central axis with an ionization chamber array (ICA). The second energy metric comes from the area ratio (AR) of the quad wedge (QW) profiles measured with the QW on the top of the ICA. Beam energy is changed by adjusting the magnetron current in a non-clinical Halcyon. With D10cm measured in water at each beam energy, the relationships between FDN or AR energy metrics to D10cm in water is established with linear regression across six energy settings. The coefficients from these regressions allow D10cm (FDN ) calculation from FDN using open profiles and D10cm (QW) calculation from AR using QW profiles. RESULTS: Five Halcyon linacs from five institutions were used to evaluate the accuracy of the D10cm (FDN ) and the D10cm (QW) energy metrics by comparing to the D10cm values computed from the treatment planning system (TPS) and D10cm measured in water. For the five linacs, the D10cm (FDN ) reported by the ICA based on FDN from open profiles agreed with that calculated by TPS within -0.29 ± 0.23% and 0.61% maximum discrepancy; the D10cm (QW) reported by the QW profiles agreed with that calculated by TPS within -0.82 ± 1.27% and -2.43% maximum discrepancy. CONCLUSION: The FDN -based energy metric D10cm (FDN ) can be used for acceptance testing of beam energy, and also for the verification of energy in periodic quality assurance (QA) processes.