Purpose: The phase 3 Veterans Affairs Lung Cancer Surgery Or Stereotactic Radiotherapy study implemented centralized quality assurance (QA) to mitigate risks of protocol deviations. This report summarizes the quality and compliance of the fi rst 100 participants treated with stereotactic body radiation therapy (SBRT) in this study. Methods and Materials: A centralized QA program was developed to credential and monitor study sites to ensure standard-of-care lung SBRT treatments are delivered to participants. Requirements were adapted from protocols established by the National Cancer Institute's Image and Radiation Oncology Core, which provides oversight for clinical trials sponsored by the National Cancer Institute's National Clinical Trials Network. Results: The fi rst 100 lung SBRT treatment plans were reviewed from April 2017 to October 2022. Tumor contours were appropriate in all submissions. Planning target volume (PTV) expansions were less than the minimum 5 mm requirement in 2% of cases. Critical organ-at-risk structures were contoured accurately for the proximal bronchial tree, trachea, esophagus, spinal cord, and brachial plexus in 75%, 92%, 100%, 100%, and 95% of cases, respectively. Prescriptions were appropriate in 98% of cases; 2 central tumors were treated using a peripheral tumor dose prescription while meeting organ-at-risk constraints. PTV V100% (the percentage of target volume that receives 100% or more of the prescription) values were above the protocol-defined minimum of 94% in all but 1 submission. The median dose maximum (Dmax) within the PTV was 125.4% (105.8%-149.0%; SD 8.7%), where values reference the percentage of the prescription dose. High-dose conformality (ratio of the volume of the prescription isodose to the volume of the PTV) and intermediate-dose compactness [R50% (ratio of the volume of the half prescription isodose to the volume of the PTV) and D2cm (the maximum dose beyond a 2 cm expansion of the PTV expressed as a percentage of the prescription dose)] were acceptable or deviation acceptable in 100% and 94% of cases, respectively. Conclusions: The fi rst 100 participants randomized to SBRT in this study were appropriately treated without safety concerns. A response to the incorrect prescriptions led to preventative measures without further recurrences. The program was developed in a health care system without prior experience with a centralized radiation therapy QA program and may serve as a reference for other institutions. Published by Elsevier Inc. on behalf of American Society for Radiation Oncology. This is an open access article under the CC BY-NC- ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

PURPOSE:Despite the frequency with which patients with cancer receive radiotherapy, integrating radiation oncology data with other aspects of the clinical record remains challenging because of siloed and variable software systems, high data complexity, and inconsistent data encoding. Recognizing these challenges, the Veterans Affairs (VA) National Radiation Oncology Program (NROP) is developing Granular Radiotherapy Information Database (GRID), a platform and pipeline to combine radiotherapy data across the VA with the goal of both better understanding treatment patterns and outcomes and enhancing research and data analysis capabilities. METHODS:This study represents a proof-of-principle retrospective cohort analysis and review of select radiation treatment data from the VA Radiation Oncology Quality Surveillance Program (VAROQS) initiative. Key radiation oncology data elements were extracted from Digital Imaging and Communications in Medicine Radiotherapy extension (DICOM-RT) files and combined into a single database using custom scripts. These data were transferred to the VA's Corporate Data Warehouse (CDW) for integration and comparison with the VA Cancer Registry System and tumor sequencing data. RESULTS:The final cohort includes 1,568 patients, 766 of whom have corresponding DICOM-RT data. All cases were successfully linked to the CDW; 18.8% of VAROQS cases were not reported in the existing VA cancer registry. The VAROQS data contributed accurate radiation treatment details that were often erroneous or missing from the cancer registry record. Tumor sequencing data were available for approximately 5% of VAROQS cases. Finally, we describe a clinical dosimetric analysis leveraging GRID. CONCLUSION:NROP's GRID initiative aims to integrate VA radiotherapy data with other clinical data sets. It is anticipated to generate the single largest collection of radiation oncology-centric data merged with detailed clinical and genomic data, primed for large-scale quality assurance, research reuse, and discovery science.

Purpose/Objective(s) To define the extent and pattern of interobserver variations of clinical and planning target volumes (CTV and PTV) delineation among international experts in predefined primary and nodal integral gross tumor volumes (iGTV) and organs at risk (OAR) volumes in lung cancer Materials/Methods Experts were invited by email globally from ASTRO, ESTRO, IAEA, FARO, and TROD for this study, and DICOM images of two anonymized cases (T1N0M0 adenocancer: Stereotactic ablative body radiotherapy, SABR & T2aN2M0 adenocancer: LA-NSCLC) were delivered online via cloud large file share to each colleague. The average data set included delineated iGTV based on 4 D-CT scan including 10 respiratory phases of primary tumor for both cases & nodal disease for LA-NSCLC, and OAR to decrease the discrepancy and interobserver variability for this step. Experts were asked to delineate the planning tumor volumes (PTV) for SABR and CTV & PTV for LA-NSCLC based on their institutional guidelines. We used a systematic process to create and analyze a consensus atlas among multiple observers facilitated by ProKnow DS Version 1.35.2; forming 50% ground truth area common in all participants contours. The comparison metrics encompassed matching volume, missing volume, extra volume, Dice coefficient for similarity, and mean differences in displacement along the X, Y, and Z axes. Results 25 experts participated the study. SABR: The PTV analysis revealed a notable level of concordance (mean DSC = 0.94 ± 0.07) between the delineations by experts and the consensus, accompanied by minimal volume disparity. LA_NSCLC: A high level of similarity (mean DSC = 0.84 ± 0.11) existed between the CTV delineations of experts and the consensus, while the experts' contours exhibited an additional volume averaging 30.07 ± 39.99 cc compared to the consensus, indicating heterogeneity within the group. Most of the experts trimmed their CTVs from chest wall and airways. A high degree of similarity (mean DSC = 0.87 ± 0.09) characterized the PTV delineations in comparison to the consensus. However, discrepancies in missing (median 8.69, mean 21.11) and additional volume (median 20.52, mean 43.64) values suggested heterogeneity within the group, with a notable standard deviation (31.28 for missing, 53.43 for extra volume). Conclusion Even in an environment of predefined primary iGTV based on 4D CT simulation for SABR, there is no 100% consensus in PTV and the discrepancy with interobserver variability for CTV was evident if more than one target present as nodal iGTV in addition to primary iGTV, accordingly which can potentially affect and change the PTV to lead into different OAR scenarios; appealing the need for standardized peer review processes and more structured guidelines for CTV.

Purpose/Objective(s) To define the extent and pattern of interobserver variations of clinical target volume (CTV) delineation among international experts in predefined primary and nodal gross tumor volumes (GTV) and organs at risk (OAR) volumes in head and neck cancer Materials/Methods Experts were invited by email globally from ASTRO, ESTRO, IAEA, FARO, and TROD for this study, and DICOM images of two anonymized cases (T1N2M0 HPV+ right tonsillar cancer: OPX & T1N2M0 EBV+ left nasopharyngeal cancer: NPX) were delivered online via cloud large file share to each colleague. The data set included delineated GTV of primary tumor & nodal disease and OAR to decrease the discrepancy and interobserver variability for this step. Experts were asked to delineate the clinical tumor volumes (CTV1, higher dose; CTV2, intermediate dose; CTV3, lower dose) and planning tumor volumes (PTV1, higher dose; PTV2, intermediate dose; PTV3, lower dose) based on their institutional guidelines. We used a systematic process to create and analyze a consensus atlas among multiple observers facilitated by ProKnow DS Version 1.35.2 (Precision radiation medicine company, Golden Rule Version 1.2.8531.2131); forming 50% ground truth area common in all participants contours. The comparison metrics encompassed matching volume, missing volume, extra volume, Dice coefficient for similarity, and mean differences in displacement along the X, Y, and Z axes. Results 32 experts participated the study. NPX: Moderate similarity was observed in both CTV1 (mean DSC = 0.73 ± 0.19) and CTV2 (mean DSC = 0.67 ± 0.17). Additionally, both CTVs' centers of mass exhibit slight displacement compared to the consensus CTVs' centers of mass. While moderate similarity characterized PTV1 (mean DSC = 0.77 ± 0.15) and PTV2 (mean DSC = 0.69 ± 0.17), PTV3 demonstrated poor similarity (mean DSC = 0.45 ± 0.21). PTV1 and PTV2 showed minor displacement compared to the consensus PTVs' centers of mass, whereas PTV3 exhibited significant displacement in both X and Y directions. OPX: Moderate similarity is observed in both CTV1 (mean DSC = 0.72 ± 0.19) and CTV2 (mean DSC = 0.66 ± 0.17), while both CTVs show minor displacement of their centers of mass compared to the consensus CTVs' centers of mass. Only PTV1 showed moderate agreement (mean DSC = 0.73 ± 0.16) between the experts and the consensus, with minor displacement in centers of mass, while PTV2 and PTV3 had poor similarity. Dice scoring alone did not correlate well with displacements from the centers of masses in X, Y, Z axis in these comparisons. While CTV1 in both cases showed moderate similarity among experts, agreement decreased on CTV2 with more variations, which accordingly diverged the PTVs. Conclusion Even in an environment of predefined primary and nodal GTV to decrease the discrepancy, interobserver variability is evident especially starting from CTV2, accordingly which can potentially affect and change the PTVs to lead into different OAR scenarios; appealing standardized peer review processes and more structured guidelines for CTV.

Purpose/Objective(s) To define the extent and pattern of global interobserver variations of prescription and plan evaluation in predefined gross tumor volume (GTV) and organs at risk (OAR) volumes among international experts in head and neck cancers Materials/Methods Experts were invited by email globally from ASTRO, ESTRO, IAEA, FARO, and TROD for this study, and DICOM images of two anonymized cases were delivered online via cloud large file share to each colleague. The data set included delineated gross tumor volumes (GTV) of primary tumor & nodal disease and organs at risk (OAR) to decrease the discrepancy and interobserver variability for this step. Experts were asked to delineate the clinical tumor volumes (CTV) and planning tumor volumes (PTV) based on their institutional guidelines; to generate a treatment plan in their planning software, as if they were to be treated in their center, based on their institutional / departmental acceptance criteria. We used a systematic process to create and analyze a consensus atlas among multiple observers facilitated by ProKnow DS Version 1.35.2. The plans were submitted to Precision radiation medicine company ProKnow (Elekta AB) in the standard DICOM format, encompassing DICOM images, RT structure sets, RT plans, and RT doses. Each plan's dose-volume histogram (DVH) was automatically recalculated to ensure consistency across different planning systems. A consensus plan, for consensus CTV and PTV atlas, generated using treatment planning software with VMAT technique was uploaded to ProKnow DS for statistical analysis to create scorecards adhering to clinical dose criteria, with subsequent comparison of target and organ-at-risk (OAR) doses against the consensus plan. Results 25 experts participated the study. NPX: The dose coverages within the target volumes are compatible. OAR dose reductions were significant in doses to the chiasm, brainstem, optic nerves, lenses, and eyes, but not for parotids and larynx via the plan generated based on consensus CTV and PTV. Though in limits, there was wide variances among expert plans for OAR doses of D50% of cochlea, brainstem, and optic chiasm. OPX: The dose coverages within the target volumes are compatible. OAR dose reductions were significant in doses to the optic nerves, lenses, and eyes, but not for chiasm, brainstem, parotids, and larynx via the plan generated based on consensus CTV and PTV. Though in limits, there was wide variances among expert plans for OAR doses of D50% of constrictors, larynx, and brachial plexus. Conclusion Though in normal range of dose limits, OAR doses were significantly heterogeneous. Even in an environment of predefined primary and nodal GTV to decrease the discrepancy, interobserver variability is evident especially starting from CTV2, accordingly which directly affected the PTVs, which generated different OAR scenarios; appealing the need for standardized peer review processes and more structured guidelines for CTV.

Following on from the 2015 Lancet Oncology Commission on expanding global access to radiotherapy, Radiotherapy and theranostics: a Lancet Oncology Commission was created to assess the access and availability of radiotherapy to date and to address the important issue of access to the promising field of theranostics at a global level. A marked disparity in the availability of radiotherapy machines between high-income countries and low-income and middle-income countries (LMICs) has been identified previously and remains a major problem. The availability of a suitably trained and credentialled workforce has also been highlighted as a major limiting factor to effective implementation of radiotherapy, particularly in LMICs. We investigated initiatives that could mitigate these issues in radiotherapy, such as extended treatment hours, hypofractionation protocols, and new technologies. The broad implementation of hypofractionation techniques compared with conventional radiotherapy in prostate cancer and breast cancer was projected to provide radiotherapy for an additional 2·2 million patients (0·8 million patients with prostate cancer and 1·4 million patients with breast cancer) with existing resources, highlighting the importance of implementing new technologies in LMICs. A global survey undertaken for this Commission revealed that use of radiopharmaceutical therapy-other than 131I-was highly variable in high-income countries and LMICs, with supply chains, workforces, and regulatory issues affecting access and availability. The capacity for radioisotope production was highlighted as a key issue, and training and credentialling of health professionals involved in theranostics is required to ensure equitable access and availability for patient treatment. New initiatives-such as the International Atomic Energy Agency's Rays of Hope programme-and interest by international development banks in investing in radiotherapy should be supported by health-care systems and governments, and extended to accelerate the momentum generated by recognising global disparities in access to radiotherapy. In this Commission, we propose actions and investments that could enhance access to radiotherapy and theranostics worldwide, particularly in LMICs, to realise health and economic benefits and reduce the burden of cancer by accessing these treatments.

PURPOSE:This study presents a novel and comprehensive framework for evaluating magnetic resonance guided radiotherapy (MRgRT) workflow by integrating the Failure Modes and Effects Analysis (FMEA) approach with Time-Driven Activity-Based Costing (TDABC). We assess the workflow for safety, quality, and economic implications, providing a holistic understanding of the MRgRT implementation. The aim is to offer valuable insights to healthcare practitioners and administrators, facilitating informed decision-making regarding the 0.35T MRIdian MR-Linac system's clinical workflow. METHODS:For FMEA, a multidisciplinary team followed the TG-100 methodology to assess the MRgRT workflow's potential failure modes. Following the mitigation of primary failure modes and workflow optimization, a treatment process was established for TDABC analysis. The TDABC was applied to both MRgRT and computed tomography guided RT (CTgRT) for typical five-fraction stereotactic body RT (SBRT) treatments, assessing total workflow and costs associated between the two treatment workflows. RESULTS:A total of 279 failure modes were identified, with 31 categorized as high-risk, 55 as medium-risk, and the rest as low-risk. The top 20% risk priority numbers (RPN) were determined for each radiation oncology care team member. Total MRgRT and CTgRT costs were assessed. Implementing technological advancements, such as real-time multi leaf collimator (MLC) tracking with volumetric modulated arc therapy (VMAT), auto-segmentation, and increasing the Linac dose rate, led to significant cost savings for MRgRT. CONCLUSION:In this study, we integrated FMEA with TDABC to comprehensively evaluate the workflow and the associated costs of MRgRT compared to conventional CTgRT for five-fraction SBRT treatments. FMEA analysis identified critical failure modes, offering insights to enhance patient safety. TDABC analysis revealed that while MRgRT provides unique advantages, it may involve higher costs. Our findings underscore the importance of exploring cost-effective strategies and key technological advancements to ensure the widespread adoption and financial sustainability of MRgRT in clinical practice.

Incident reporting and learning systems provide an opportunity to identify systemic vulnerabilities that contribute to incidents and potentially degrade quality. The narrative of an incident is intended to provide a clear, easy to understand description of an incident. Unclear, incomplete or poorly organized narratives compromise the ability to learn from them. This report provides guidance for drafting effective narratives, with particular attention to the use of narratives in incident reporting and learning systems (IRLS). Examples are given that compare effective and less than effective narratives. This report is mostly directed to organizations that maintain IRLS, but also may be helpful for individuals who desire to write a useful narrative for entry into such a system. Recommendations include the following: (1) Systems should allow a one- or two-sentence, free-text synopsis of an incident without guessing at causes; (2) Information included should form a sequence of events with chronology; and (3) Reporting and learning systems should consider using the headings suggested to guide the reporter through the narrative: (a) incident occurrences and actions by role; (b) prior circumstances and actions; (c) method by which the incident was identified; (d) equipment related details if relevant; (e) recovery actions by role; (f) relevant time span between responses; (g) and how individuals affected during or immediately after incident. When possible and appropriate, supplementary information including relevant data elements should be included using numerical scales or drop-down choices outside of the narrative. Information that should not be included in the narrative includes: (a) patient health information (PHI); (b) conjecture or blame; (c) jargon abbreviations or details without specifying their significance; (d) causal analysis.

Purpose Large-scale radiotherapy datasets drive predictive modeling, automated segmentation and planning, and personalized treatment, yet remain fragmented because treatment-planning (TPS) and record-and-verify (R&V) systems differ, DICOM implementations are inconsistent, and automated linkage tools are lacking. We developed a generalizable framework that automatically reconstructs complete planning and delivery datasets across diverse clinical environments with minimal manual effort. Methods We designed and implemented a software framework capable of automating the collection and integration of radiotherapy data from multiple institutions and TPS/R&V combinations. The system begins with Radiotherapy Treatment Records (RTRECORDS) and recursively traces unidirectional DICOM references to retrieve linked radiotherapy treatment plans (RTPLANs), doses (RTDOSEs), structure sets (RTSTRUCTs), planning images, image registrations (REG), and associated diagnostic images. Core components of the framework include automated DICOM queries, secure data transfer, integrity verification, linkage mapping, and detailed logging. To support diverse environments, we developed custom modules for non-DICOM-compliant systems, file format conversions, and robust error handling. Results The framework was deployed across four institutions using six different combinations of TPS and R&V systems. In a focused two-clinic implementation spanning 11 years of retrospective data, the system successfully processed and integrated data from 6,164 patients and 13,871 radiotherapy plans. The pipeline achieved a 99.76% success rate in identifying and linking complete treatment datasets, with an average processing time of 18 minutes per patient, demonstrating its efficiency and scalability in real-world conditions. Conclusion This automated framework provides a scalable and reliable solution for large-scale aggregation of radiotherapy data. It is compatible with heterogeneous clinical systems, including those lacking DICOM Query/Retrieve support, and overcomes key technical barriers to data integration. By enabling the creation of comprehensive, high-quality datasets, the framework supports advanced research and contributes to the improvement of clinical care in radiation oncology.

Purpose Radiation Oncology Learning Health System (RO-LHS) is a promising approach to improve the quality of care by integrating clinical, dosimetry, treatment delivery, research data in real-time. This paper describes a novel set of tools to support the development of a RO-LHS and the current challenges they can address. Methods We present a knowledge graph-based approach to map radiotherapy data from clinical databases to an ontology-based data repository using FAIR concepts. This strategy ensures that the data is easily discoverable, accessible, and can be used by other clinical decision support systems. It allows for visualization, presentation, and data analyses of valuable information to identify trends and patterns in patient outcomes. We designed a search engine that utilizes ontology-based keyword searching, synonym-based term matching that leverages the hierarchical nature of ontologies to retrieve patient records based on parent and children classes, connects to the Bioportal database for relevant clinical attributes retrieval. To identify similar patients, a method involving text corpus creation and vector embedding models (Word2Vec, Doc2Vec, GloVe, and FastText) are employed, using cosine similarity and distance metrics. Results The data pipeline and tool were tested with 1660 patient clinical and dosimetry records resulting in 504,180 RDF tuples and visualized data relationships using graph-based representations. Patient similarity analysis using embedding models showed that the Word2Vec model had the highest mean cosine similarity, while the GloVe model exhibited more compact embeddings with lower Euclidean and Manhattan distances. Conclusions The framework and tools described support the development of a RO-LHS. By integrating diverse data sources and facilitating data discovery and analysis, they contribute to continuous learning and improvement in patient care. The tools enhance the quality of care by enabling the identification of cohorts, clinical decision support, and the development of clinical studies and machine learning programs in radiation oncology.

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.

BACKGROUND:Clinical data collection related to prostate cancer (PCa) care is often unstructured or heterogeneous among providers, resulting in a high risk for ambiguity in its meaning when sharing or analyzing data. Ontologies, which are shareable formal (i.e., computable) representations of knowledge, can address these challenges by enabling machine-readable semantic interoperability. The purpose of this study was to identify PCa-specific key data elements (KDEs) for standardization in clinic and research. METHODS:A modified Delphi method using iterative online surveys was performed to report a consensus agreement on KDEs by a multidisciplinary panel of 39 PCa specialists. Data elements were divided into three themes in PCa and included (1) treatment-related toxicities (TRT), (2) patient-reported outcome measures (PROM), and (3) disease control metrics (DCM). RESULTS:The panel reached consensus on a thirty-item, two-tiered list of KDEs focusing mainly on urinary and rectal symptoms. The Expanded Prostate Cancer Index Composite (EPIC-26) questionnaire was considered most robust for PROM multi-domain monitoring, and granular KDEs were defined for DCM. CONCLUSIONS:This expert consensus on PCa-specific KDEs has served as a foundation for a professional society-endorsed, publicly available operational ontology developed by the American Association of Physicists in Medicine (AAPM) Big Data Sub Committee (BDSC).

In this study, we investigated 3D convolutional neural networks (CNNs) with input from radiographic and dosimetric datasets of primary lung tumors and surrounding lung volumes to predict the likelihood of radiation pneumonitis (RP). Pre-treatment, 3- and 6-month follow-up computed tomography (CT) and 3D dose datasets from one hundred and ninety-three NSCLC patients treated with stereotactic body radiotherapy (SBRT) were retrospectively collected and analyzed for this study. DenseNet-121 and ResNet-50 models were selected for this study as they are deep neural networks and have been proven to have high accuracy for complex image classification tasks. Both were modified with 3D convolution and max pooling layers to accept 3D datasets. We used a minority class oversampling approach and data augmentation to address the challenges of data imbalance and data scarcity. We built two sets of models for classification of three (No RP, Grade 1 RP, Grade 2 RP) and two (No RP, Yes RP) classes as outputs. The 3D DenseNet-121 models performed better (F1 score [0.81], AUC [0.91] [three class]; F1 score [0.77], AUC [0.84] [two class]) than the 3D ResNet-50 models (F1 score [0.54], AUC [0.72] [three-class]; F1 score [0.68], AUC [0.71] [two-class]) (p = 0.017 for three class predictions). We also attempted to identify salient regions within the input 3D image dataset via integrated gradient (IG) techniques to assess the relevance of the tumor surrounding volume for RP stratification. These techniques appeared to indicate the significance of the tumor and surrounding regions in the prediction of RP. Overall, 3D CNNs performed well to predict clinical RP in our cohort based on the provided image sets and radiotherapy dose information.

Physicians often label anatomical structure sets in Digital Imaging and Communications in Medicine (DICOM) images with nonstandard random names. Hence, the standardization of these names for the Organs at Risk (OARs), Planning Target Volumes (PTVs), and ‘Other’ organs is a vital problem. This paper presents novel deep learning methods on structure sets by integrating multimodal data compiled from the radiotherapy centers of the US Veterans Health Administration (VHA) and Virginia Commonwealth University (VCU). These de-identified data comprise 16,290 prostate structures. Our method integrates the multimodal textual and imaging data with Convolutional Neural Network (CNN)-based deep learning approaches such as CNN, Visual Geometry Group (VGG) network, and Residual Network (ResNet) and shows improved results in prostate radiotherapy structure name standardization. Evaluation with macro-averaged F1 score shows that our model with single-modal textual data usually performs better than previous studies. The models perform well on textual data alone, while the addition of imaging data shows that deep neural networks achieve better performance using information present in other modalities. Additionally, using masked images and masked doses along with text leads to an overall performance improvement with the CNN-based architectures than using all the modalities together. Undersampling the majority class leads to further performance enhancement. The VGG network on the masked image-dose data combined with CNNs on the text data performs the best and presents the state-of-the-art in this domain.

PURPOSE:For patients with lung cancer, it is critical to provide evidence-based radiation therapy to ensure high-quality care. The US Department of Veterans Affairs (VA) National Radiation Oncology Program partnered with the American Society for Radiation Oncology (ASTRO) as part of the VA Radiation Oncology Quality Surveillance to develop lung cancer quality metrics and assess quality of care as a pilot program in 2016. This article presents recently updated consensus quality measures and dose-volume histogram (DVH) constraints. METHODS AND MATERIALS:A series of measures and performance standards were reviewed and developed by a Blue-Ribbon Panel of lung cancer experts in conjunction with ASTRO in 2022. As part of this initiative, quality, surveillance, and aspirational metrics were developed for (1) initial consultation and workup; (2) simulation, treatment planning, and treatment delivery; and (3) follow-up. The DVH metrics for target and organ-at-risk treatment planning dose constraints were also reviewed and defined. RESULTS:Altogether, a total of 19 lung cancer quality metrics were developed. There were 121 DVH constraints developed for various fractionation regimens, including ultrahypofractionated (1, 3, 4, or 5 fractions), hypofractionated (10 and 15 fractionations), and conventional fractionation (30-35 fractions). CONCLUSIONS:The devised measures will be implemented for quality surveillance for veterans both inside and outside of the VA system and will provide a resource for lung cancer-specific quality metrics. The recommended DVH constraints serve as a unique, comprehensive resource for evidence- and expert consensus-based constraints across multiple fractionation schemas.

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.