Radiation Oncology Information Research Articles

Technical note: An automated document verification tool in radiation oncology EMR: Application for LDR prostate brachytherapy.

This study aims to illustrate how a script-based automated tool can efficiently verify documentation for LDR prostate brachytherapy. An in-house Python-scripts-based tool was developed to automatically verify the specific checklists, aligned with our institutional practice guidelines for prostate seed implants (PSI). The scripts, compatible with our radiation oncology information system, could be executed with an optional web-based middleware to access and evaluate Aria documents. Optimized based on data from the previous 400 patients, the automated tool was applied to a random cohort of 50 LDR patients. It evaluated the adequacy of specific EMR documents by performing checks for data completeness, consistency, and allowable value range. We analyzed the efficiency of using this tool against conventional manual checks in two LDR processes: seed ordering and monthly audits for our PSI programs. The automated tool effectively performed chart checks on the involved PSI documents. Human errors, such as typos and inconsistent information, were identified in 7 out of 50 patients during the seed ordering process and in 2 out of 50 patients during the monthly audit. Meanwhile, this automation reduced the majority of manual chart-checking time by an average of 5 and 10 min per patient for these processes, respectively. The anticipated efficiency gains will continue to accrue as more check items are digitalized and assessable to the scripts. The implementation of an automated tool tailored for LDR prostate brachytherapy has demonstrated its efficiency benefits. Such an approach can help other clinics substantially enhance routine chart checks, periodic audits, and other applications in similar clinical settings.

Outcomes of De Novo Oligometastatic Breast Cancer Treated With Surgery of Primary and Metastasis Directed Radiotherapy.

With sensitive imaging for breast cancer, the question arises whether present-day oncologists treat dOMBC with palliative systemic therapy (ST), which, a few years earlier, would have been treated with curative intent. We retrospectively analyzed outcomes of dOMBC treated with curative intent using a combination of surgery, metastasis-directed radiotherapy (RT), and adjuvant/neoadjuvant ST and have also explored the possible role of total lesional glycolysis of metastases and p53 immunohistochemistry in predicting outcomes. Data were collected from a prospectively maintained database using electronic medical records and Radiation Oncology Information System. In the study, dOMBC was defined as up to 3 metastatic sites, all amenable to treatment with ablative RT and primary and axillary disease amenable to curative surgery. Patients were treated with surgery, ST, and RT. Patients underwent either breast conservation surgery or modified radical mastectomy. Patients were treated with 6 to 8 cycles of chemotherapy in the neoadjuvant and/or adjuvant setting. Hormone receptor-positive patients received either tamoxifen or aromatase inhibitors. Trastuzumab was offered to Her-2-neu receptor-positive patients. RT included locoregional RT and metastases-directed ablative body RT. The median progression-free survival was 39 months (95% CI: -28.7 to 50.1mo). Two and 3 year estimated disease-free survival (DFS) was 79% and 60.5%, respectively. The median overall survival was not reached. The estimated 3-year overall survival was 87.3%. Total lesional glycolysis of metastases score and p53 status did not affect DFS. Combination treatment of surgery, metastases-directed ablative RT, and ST may provide prolonged DFS in dOMBC.

AAPM WGPE report 394: Simulated error training for the physics plan and chart review.

Simulated error training is a method to practice error detection in situations where the occurrence of error is low. Such is the case for the physics plan and chart review where a physicist may check several plans before encountering a significant problem. By simulating potentially hazardous errors, physicists can become familiar with how they manifest and learn from mistakes made during a simulated plan review. The purpose of this project was to develop a series of training datasets that allows medical physicists and trainees to practice plan and chart reviews in a way that is familiar and accessible, and to provide exposure to the various failure modes (FMs) encountered in clinical scenarios. A series of training datasets have been developed that include a variety of embedded errors based on the risk-assessment performed by American Association of Physicists in Medicine (AAPM) Task Group 275 for the physics plan and chart review. The training datasets comprise documentation, screen shots, and digital content derived from common treatment planning and radiation oncology information systems and are available via the Cloud-based platform ProKnow. Overall, 20 datasets have been created incorporating various software systems (Mosaiq, ARIA, Eclipse, RayStation, Pinnacle) and delivery techniques. A total of 110 errors representing 50 different FMs were embedded with the 20 datasets. The project was piloted at the 2021 AAPM Annual Meeting in a workshop where participants had the opportunity to review cases and answer survey questions related to errors they detected and their perception of the project's efficacy. In general, attendees detected higher-priority FMs at a higher rate, though no correlation was found between detection rate and the detectability of the FMs. Familiarity with a given system appeared to play a role in detecting errors, specifically when related to missing information at different locations within a given software system. Overall, 96% of respondents either agreed or strongly agreed that the ProKnow portal and training datasets were effective as a training tool, and 75% of respondents agreed or strongly agreed that they planned to use the tool at their local institution. The datasets and digital platform provide a standardized and accessible tool for training, performance assessment, and continuing education regarding the physics plan and chart review. Work is ongoing to expand the project to include more modalities, radiation oncology treatment planning and information systems, and FMs based on emerging techniques such as auto-contouring and auto-planning.

A script-enabled interactive checklist document for efficient management of electronic devices in a busy multimodality radiotherapy clinic.

Develop an efficient, interactive, and instructive checklist document for the management of implanted electronic medical devices in a multimodality radiotherapy clinic. The built-in scripting and interactivity of a popular commercial word processor was used to develop an interactive document that changes the information presented to the user based on drop-down selections. The interactivity and scripting were compatible with the radiation oncology information system (ROIS) which allows the document to be accessible by all team members and serve as a permanent record in a patient's electronic chart. The final interactive document, which was clinically deployed after beta testing with a group consisting of nurses and medical physicists, presents information and action plans to the user based on multiple departmental medical device decision trees that are specific to the combination of device, treatment modality, rhythm-pacing dependence for cardiac devices, and distance from the device to the treatment volume. A script-enabled interactive document was developed for a busy multimodality clinic, condensing multiple comprehensive departmental guidelines spanning multiple device types and treatment modalities into a single interactive checklist accessible within the ROIS. Given the wide accessibility of the commercial word processor, this approach could be adopted by other clinics to streamline their own respective workflows.

RADCURE: An open-source head and neck cancer CT dataset for clinical radiation therapy insights.

This manuscript presents RADCURE, one of the most extensive head and neck cancer (HNC) imaging datasets accessible to the public. Initially collected for clinical radiation therapy (RT) treatment planning, this dataset has been retrospectively reconstructed for use in imaging research. RADCURE encompasses data from 3346 patients, featuring computed tomography (CT) RT simulation images with corresponding target and organ-at-risk contours. These CT scans were collected using systems from three different manufacturers. Standard clinical imaging protocols were followed, and contours were manually generated and reviewed at weekly RT quality assurance rounds. RADCURE imaging and structure set data was extracted from our institution's radiation treatment planning and oncology information systems using a custom-built data mining and processing system. Furthermore, images were linked to our clinical anthology of outcomes data for each patient and includes demographic, clinical and treatment information based on the 7th edition TNM staging system (Tumor-Node-Metastasis Classification System of Malignant Tumors). The median patient age is 63, with the final dataset including 80% males. Half of the cohort is diagnosed with oropharyngeal cancer, while laryngeal, nasopharyngeal, and hypopharyngeal cancers account for 25%, 12%, and 5% of cases, respectively. The median duration of follow-up is five years, with 60% of the cohort surviving until the last follow-up point. The dataset provides images and contours in DICOM CT and RT-STRUCT formats, respectively. We have standardized the nomenclature for individual contours-such as the gross primary tumor, gross nodal volumes, and 19 organs-at-risk-to enhance the RT-STRUCT files' utility. Accompanying demographic, clinical, and treatment data are supplied in a comma-separated values (CSV) file format. This comprehensive dataset is publicly accessible via The Cancer Imaging Archive. RADCURE's amalgamation of imaging, clinical, demographic, and treatment data renders it an invaluable resource for a broad spectrum of radiomics image analysis research endeavors. Researchers can utilize this dataset to advance routine clinical procedures using machine learning or artificial intelligence, to identify new non-invasive biomarkers, or to forge prognostic models.

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

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). 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. 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. 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.

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

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. 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. 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. 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.

Contralateral Nodal Relapse (CLNR) in Well Lateralized Oral Cavity Cancer Treated Ipsilaterally with Surgery and Adjuvant Radiotherapy with or without Concurrent Chemotherapy: A Retrospective Audit

<h3>Purpose/Objective(s)</h3> Elective treatment of contralateral neck is a matter of debate in well lateralized oral squamous cell carcinoma (OSCC). The current study was done to evaluate the incidence, pattern of contralateral nodal relapse (CLNR), outcomes and risk factors predicting CLNR in well lateralized OSCC treated with unilateral surgery and adjuvant ipsilateral radiotherapy with or without concurrent chemotherapy. <h3>Materials/Methods</h3> Consecutive patients of well lateralized OSCC treated with surgery and adjuvant radiotherapy ± chemotherapy between 2012 to 2017 were included in this study. Data regarding demography, histo-pathological features, treatment and outcomes were retrieved from hospital case files and electronic medical records (EMR), Radiation Oncology Information System (ROIS) and Treatment Planning System (TPS). Primary cancers of tongue, floor of mouth, disease in the midline or crossing midline and patients receiving elective treatment to contralateral neck were excluded. The primary endpoint of the study was to determine the incidence of CLNR and contralateral nodal relapse free survival (CLNRFS). CLNRFS was determined from the date of surgery till documented clinico-radiological or histological evidence of CLNR or death or last follow up. Univariable and multivariable analysis was done to identify all potential predictive factors for CLNR. <h3>Results</h3> Of 1120 patients of OSCC treated with surgery and radiotherapy, 208 patients were eligible for the current study. At a media follow-up for 45 months, 21 (10%) patients had isolated contralateral nodal relapse and additional 9 (4.3%) patients had bilateral nodal relapse. CLNR was most common at level IB (n=13, 61.9%) followed by level II in patients with isolated CLNR. Only 1 patient had isolated contralateral level IV metastasis. Total six (28.6%) patients could be salvaged with surgery with and without radiotherapy. 3 of the 6 patients developed distant metastasis and succumbed to the disease and 3 were alive and diseases free at last follow up. Local recurrence was the most common pattern of failure (21%) in the entire group. The 5-year CLNRFS was 82.5% and 5-year overall survival was 57.7%. Higher rate of CLNR was observed in patients with ipsilateral nodal disease (p=0.001), ≥2 positive lymph nodes (0.006, level IB (0.002) or II note(p=0.000), extranodal extension (p=0.001) and perineural invasion (p=0.021) on univariable analysis. More than equal to 2 positive ipsilateral positive lymph node was identified as independent prognostic factor for CLNR on multivariable analysis (p=0.007). <h3>Conclusion</h3> The incidence of contralateral nodal failure is low in ipsilaterally located and unilaterally treated OSCC. Patients with multiple ipsilateral nodal disease may be considered for elective treatment of contralateral neck especially level IB either with surgery or radiotherapy.

Patterns of Failure in Sinonasal Cancers after Curative Intensity Modulated Radiation Therapy

<h3>Purpose/Objective(s)</h3> To study the pattern of failure in sinonasal cancers treated with curative intent radiotherapy using IMRT/ VMAT techniques, with or without chemotherapy. and also, to document the disease outcomes and toxicity. <h3>Materials/Methods</h3> Patients with sinonasal cancers who had been treated with curative intent radiation therapy using IMRT/ VMAT technique at our institute between January 2007 to December 2019 were evaluated. All demographic, disease, treatment and outcomes related details were retrieved from the electronic medical records (EMR), radiation oncology information system (ROIS) and treatment planning system (TPS). Locoregional failure (LRF) was defined either as persistent disease post treatment less than 6 months or reappearance of tumor at either primary site or draining regional lymph-nodes which was histologically or radiologically proven. MRI/PET-CT done at the time of recurrence was imported into the TPS and volume of failure (Vf) was contoured slice by slice. Overlap volume between Vf and 95% isodose of PTV1/PTV2/PTV3 was generated using Boolean tool in TPS. Failure was defined as in-field if >95% volume of recurrent tumor fell within the 95% isodose line of PTV1, marginal if 20- 95% of the volume of recurrence was within the 95% isodose line and outfield if <20% of the recurrent tumor was in the 95% isodose line of PTV1. Similar definitions were considered for PTV2 and PTV3 if any. Treatment related toxicity and control rates were also documented. <h3>Results</h3> A total of 126 patients were treated with IMRT of which 39 patients developed recurrences either at local, regional or distant sites. Of these 28 patients had developed LRF with or without distant metastases while 11 patients had distant metastases alone. Imaging at recurrence was available in 19/28 patients with LRF which forms the cohort for the present study. Eight of these patients had persistent residual disease at primary, 2 patients had local recurrence only, 2 patients had local and regional recurrence,7 patients had a regional recurrence. In patients with local failure 6 had in- field failure, 5 patients had marginal and 1 patient had out of field failure. Four of 5 patients with marginal recurrence had recurrence and PTV close to the critical structures resulting in compromised doses. All the regional recurrences were out of field failure in un-irradiated neck. However, there was no difference in regional failure in irradiated vs non-irradiated neck (p=0.73). The median follow-up of the entire cohort was 42.5 months. The 5-year OS, DFS, LC, RC and LRC were 74.4%, 62%, 76.8%, 89.1% and 69.6% respectively. The grade 3 and grade 4 late ocular toxicity were 4.8% (n=6) and 1.6%(n=2), respectively. <h3>Conclusion</h3> Local recurrence with in-field recurrence was the most common type of failure. Majority of the marginal failures were close to the critical structures. All the regional recurrences were observed in the unirradiated neck.

Evaluation of an automated Presidio anonymisation model for unstructured radiation oncology electronic medical records in an Australian setting

BackgroundElectronic medical records (EMRs) contain valuable information for clinical research, however, the presence of personally identifying information (PII) restricts their use. Anonymisation of PII from EMRs enables clinical information to be shared for research purposes. Since there is limited research relating to the anonymisation of Australian EMRs, the performance of Microsoft Presidio with customisation on clinical documents from an Australian radiation oncology information system (OIS) was evaluated. MethodsA random sample of 300 unstructured free-text clinical documents were extracted from the Prince of Wales Cancer Centre OIS on patients diagnosed with cancer of the head and neck between 2000 and 2017. Anonymisation of clinical text was performed using Microsoft Presidio, implemented in Python programming language. Each clinical document was manually compared pre- and post-anonymisation for the identification and redaction of 13 PII. Model performance was evaluated using three classification criteria; correct, partial, and missed classification, to determine recall, precision, and F1-score. These three metrics were performed under relaxed conditions, where partial classifications were considered correct, and under strict conditions, where only correct classifications were considered correct. ResultsA total of 8,713 PII were identified, of which 7,026 (81%) were classified as correct, 850 (10%) as partial, and 837 (9%) as missed. There were 245 instances of incorrect classifications. Evaluation of the model demonstrated an average precision of 0.8921, recall (strict) of 0.8064, F1-score (strict) of 0.8471, recall (relaxed) of 0.9039, and F1-score (relaxed) of 0.8980. ConclusionThis is the first example of an open-source anonymisation model to be customised and tested on clinical documents from an Australian radiation oncology EMR. These findings support the use of Presidio for the safe use and sharing of cancer data within Australia for certain PII, however, additional checks are required to ensure person names are successfully anonymised.

Review on Radiation Therapy Information Systems

Aim: Clinical and pre-clinical radiotherapy data represent one of the most valuable assets for radiation therapy and oncology institutions. The present study aimed to review various clinical information systems used in the field of radiotherapy and the critical success factors influencing the system implementation.&#x0D; Method: This review study was completed in 2022. In this study, papers related to clinical information systems in the field of oncology were retrieved by using keywords. The databases and the search engine were Scopus, PubMed, Science Direct, Web of Knowledge, Ovid, Medline, and Google Scholar and the time frame was between 2010 and 2022. Initially, a total of 427 papers were retrieved, and finally, 18 related papers were selected.&#x0D; Results: The main clinical information systems used in radiotherapy included developing a patient’s electronic chart checks (two studies), establishing a web-based Integrated Radiation Oncology Information Platform, implementing a web-based Electronic Data Capture system, developing a data archiving system, developing a prototype software for the anonymization of radiation therapy treatment plans, creating a national radiation oncology registry, identifying deficiencies in treatment plans and radiotherapy simulation, creating a web-based radiotherapy system and other related studies (nine studies).&#x0D; Conclusion: The use of clinical information systems in the field of radiotherapy is inevitable, mainly due to the wide range of benefits that these systems have. However, to implement these systems successfully, proper choice of technology, user training, application of standards, use of big data, periodic evaluations, as well as workflow identification are required for effective use of these systems.

A National Survey on the Awareness of Artificial Intelligence Technologies Among Radiation OncologistsTurkish Society of Radiation Oncology Artificial Intelligence and Information Technology Group Study

A National Survey on the Awareness of Artificial Intelligence Technologies Among Radiation OncologistsTurkish Society of Radiation Oncology Artificial Intelligence and Information Technology Group Study

#radonc: Growth of the global radiation oncology Twitter network

IntroductionSocial media connects people globally and may enhance access to radiation oncology information. We characterized the global growth of the radiation oncology Twitter community using the hashtag #radonc. Materials and MethodsWe analyzed all public tweets bearing the hashtag #radonc from 2014 to 2019 using Symplur Signals. We collected data on #radonc activity and growth, stakeholder distribution, user geolocation, and languages. We obtained global Twitter user data and calculated average annual growth rates for users and tweets. We analyzed growth rates by stakeholder. We conducted thematic analysis on a sample of tweets in each three-year period using frequently occurring two-word combinations. ResultsWe identified 193,115 tweets including #radonc composed by 16,645 Twitter users. Globally, users wrote in 35 languages and came from 122 countries, with the known highest users from the United States, United Kingdom, and Spain. Use of #radonc expanded from 23 countries in 2014 to 116 in 2019. The average annual growth rate in #radonc users and tweets was 70.5% and 69.2%, respectively. The annual growth rate of #radonc users was significantly higher than for all Twitter users (p = 0.004). While doctors were the source of 46.9% of all tweets, research and government organizations had annual increases in tweet volume of 84.6% and 211.4%, respectively. From 2014 to 2016, promotion of the radiation oncology community was the most active theme, though this dropped to 7th in 2017–2019 as discussion increased regarding aspects of radiation and treated disease sites. ConclusionUse of #radonc has grown rapidly into a global community. Focused discussion related to radiation oncology has outpaced the growth of general Twitter use, both among physicians and non-physicians. #radonc has grown into a self-sustaining community. Further research is necessary to define the risks and benefits of social media in medicine and to determine whether it adds value to oncology practice.

A practical cyberattack contingency plan for radiation oncology.

PurposeThis article presents a solution for continuing radiation therapy without interruption in the event of a cyberattack to the radiation oncology information systems (ROIS). This process could be easily deployed to any radiation oncology practice, with little clinical overhead or burden.Methods and MaterialsThe solution automatically retrieves all essential information from the clinical ROIS for each under‐treatment patient and periodically (e.g., daily) saves these data to a dedicated secure server for recovery. In the event that the clinical ROIS is not functioning as a result of a cyberattack, this essential information is used to build a new secondary ROIS server to continue radiotherapy treatments until the main ROIS is recovered. Once the cyberattack threat is cleared, the clinical ROIS server is rebuilt from the institution’s enterprise backup. The newly accumulated treatment information for each patient is then exported from the secondary ROIS to bring the clinical ROIS up to date.ResultsThe Department of Radiation Oncology at the University of Maryland Medical System implemented this solution for clinical use with the Varian ARIA ROIS in the management of ~250 daily radiotherapy treatments, inclusive of a proton center. This solution was determined to be a feasible and affordable business continuity plan for the radiation oncology practice by minimizing radiation treatment downtime to a couple of hours in a simulated cyberattack drill.ConclusionsThe proposed solution can achieve continuation of radiation therapy treatment without treatment breaks in the event of a cyberattack. It also provides cushion time for radiation oncology departments to rebuild their clinical ROIS systems from the enterprise data backup.

A systematic quality assurance framework for the upgrade of radiation oncology information systems

In spite of its importance, no systematic and comprehensive quality assurance (QA) program for radiation oncology information systems (ROIS) to verify clinical and treatment data integrity and mitigate against data errors/corruption and/or data loss risks is available. Based on data organization, format and purpose, data in ROISs falls into five different categories: (1) the ROIS relational database and associated files; (2) the ROIS DICOM data stream; (3) treatment machine beam data and machine configuration data; (4) electronic medical record (EMR) documents; and (5) user-generated clinical and treatment reports from the ROIS. For each data category, this framework proposes a corresponding data QA strategy to very data integrity. This approach verified every bit of data in the ROIS, including billions of data records in the ROIS SQL database, tens of millions of ROIS database-associated files, tens of thousands of DICOM data files for a group of selected patients, almost half a million EMR documents, and tens of thousands of machine configuration files and beam data files. The framework has been validated through intentional modifications with test patient data. Despite the ‘big data’ nature of ROIS, the multiprocess and multithread nature of our QA tools enabled the whole ROIS data QA process to be completed within hours without clinical interruptions. The QA framework suggested in this study proved to be robust, efficient and comprehensive without labor-intensive manual checks and has been implemented for our routine ROIS QA and ROIS upgrades.

A Radiation Oncology–Specific Automated Trigger Indicator Tool for High-Risk, Near-Miss Safety Events

PurposeError detection in radiation oncology relies heavily on voluntary reporting, and many adverse events and near misses likely go undetected. Trigger tools use existing data in patient charts to identify otherwise-unaccounted-for events and have been successfully employed in other areas of medicine. We developed an automated radiation oncology–specific trigger tool and validated it against near-miss data from a high-volume incident learning system (ILS). Methods and MaterialsTwenty triggers were derived from an electronic radiation oncology information system. Data from the systems over an approximately 3.5-year period were split randomly into training and test sets. The probability of a high-grade (grade 3-4) near miss for each treatment course in the training set was estimated using a regularized logistic regression model. The predictive model was applied to the test set. Records for 25 flagged treatment courses with an ILS entry were reviewed to explore the association between triggers and near misses, and 25 flagged courses without an ILS entry were reviewed to detect unreported near misses. ResultsOf the 3159 treatment courses analyzed, 357 had a grade 3 to 4 ILS entry; 2210 courses composed the training set, and the test set had 949 courses. Areas under the curve on the training and test sets were 0.650 and 0.652, respectively. Of 20 triggers, 9 reached statistical significance on univariate analysis. Fifty percent of the 25 treatment courses in the test set with the highest predicted likelihood of a high-grade near miss with an ILS entry had a direct relationship between the triggers and the near miss. Review of the 25 treatment courses with the highest predicted likelihood of high-grade near miss without an ILS entry found 2 unreported near-miss events. ConclusionsThe radiation oncology–specific automated trigger tool performed modestly and identified additional treatment courses with near-miss events. Radiation oncology trigger tools deserve further exploration.

Collaborating with the Community: Improving Patient Access to Palliative Radiation Therapy

PurposePalliative radiation therapy (PRT) has an essential role in cancer symptom control but is underutilized in Ontario. This initiative aimed to implement an educational outreach intervention to improve knowledge of and access to PRT among interprofessional palliative health care teams across an Ontario Local Health Integration Network. Methods and MaterialsA needs assessment was completed from June to September 2018 with interprofessional palliative health care teams. Participants completed a survey to identify perceived opportunities, barriers, and enablers to recommending or referring patients for PRT. Thematic analysis informed content of the educational outreach intervention and included how to access PRT, common indications, case studies, and side-effect management after completing PRT. The educational outreach intervention was completed from October 2018 to January 2019. Participants completed a survey, and results were analyzed using descriptive statistics. The number of patients who received PRT was determined by cross-referencing the regional database with the radiation oncology information system. ResultsAlthough 22.9% of participants had previously recommended or referred patients, 96.2% of participants agreed or strongly agreed that they are likely to recommend or refer patients for PRT after the educational outreach intervention (n = 131). An increase was observed in the number of patients receiving PRT from the community during the intervention period. ConclusionsThe educational outreach intervention improved knowledge and the likelihood of interprofessional palliative health care teams providing access to PRT for patients in the community. More patients now receive PRT, conveying improved symptom control and quality of life.

Comparison of Prospectively-Collected Physician-Reported and Patient-Reported Toxicities Among Gynecological Cancer Patients Receiving Radiation Therapy

Evaluation of correlations between observer (physician) versus patient-reported outcomes (ObsROs and PROs) was conducted among a group of patients receiving external beam radiation therapy for gynecological cancer. Concurrent assessments of Common Terminology Criteria for Adverse Events (CTCAE v4), and PRO CTCAE questionnaires were acquired during radiation oncology clinic visits (July 2017 to January 2018) for gynecological cancer patients. Gastrointestinal toxicities (GI: anorexia, nausea, vomiting, and diarrhea) and genitourinary toxicities (GU: urinary incontinence, urinary frequency and dysuria) were prospectively graded by patients and observers during weekly on-treatment visits from week 1 to 5 of radiation therapy. PROs were captured via electronic tablets and ObsROs were collected using assessment forms in the radiation oncology information system. A Pearson’s correlation was used to determine the relationship between ObsROs and PROs. Of the 27 patients treated, 27 had ObsROs, and 23 had concurrent PROs. The capture efficiency over this 6 month trial period was 85%. The median age at diagnosis was 61 years old (22-77 years old). The median total prescription radiation dose was 45Gy (28-45 Gy). 15 patients had pelvis and 8 had extended fields to paraaortic lymph nodes treatment. All the ObsROs ranged from 0-2; while PROs ranged more widely: decrease appetite (0-3), nausea (0-4), vomiting (0-4), diarrhea (0-3), urinary incontinence (0-4), urinary frequency (0-4) and painful urination (0-3). For GI toxicities, anorexia, nausea, vomiting and diarrhea ObsROs were strongly correlated with their respective PROs (anorexia, r =0.88; nausea, r=0.83; vomiting, r=0.87; diarrhea, r=0.65, all p<0.05). For GU toxicities, urinary incontinence, frequency, and dysuria ObsROs were moderately correlated with their respective PROs (urinary incontinence r=0.56; urinary frequency, r=0.64; painful urination r=0.56, all p<0.05). For the GI toxicities management, 19 patients (82%) received bowel movement medications while the remaining 4 patients tolerated well without need for intervention. For GU toxicities management, 3 (13%) patients received urinary symptom treatment. Agreement among ObsPROs and PROs appears to be stronger in GI than in GU outcomes during the treatment. GI toxicities might be more easily observed and also assessed more thoroughly due to the ease of intervention with existing medications. GU toxicities may be less easy to observe and less amenable to treatment, and therefore, remain underreported by physicians. Future healthcare providers may consider PROs to better measure and monitor symptoms and direct therapeutic interventions for patients receiving pelvic radiation.

Enhancing the Reporting of Radiation Oncology Treatment Details to Central Cancer Registries and the SEER Program: A Report of Pilot Studies in Progress

Reporting cancer treatment data to state cancer registries provides a framework for assessing national treatment and outcome trends and the foundation for health services and epidemiologic research. All cancer treatments are reportable to state cancer registries; however, radiation oncology details are generally limited to whether radiation was administered or not, without any details. By establishing standardized, scalable, and easy-to-adopt methods to acquire data derived from commercial radiation oncology information systems (ROIS) we hope to demonstrate that details of radiation oncology treatments can be successfully imported into central registries, thus facilitating new research on radiation treatments and outcomes at the population level. To assess the feasibility and requirements of enhancing and automating detailed reporting from ROIS to registries, we are facilitating the acquisition of radiation oncology data from two leading commercial systems that – combined – represent greater than 90% of US radiation oncology clinics. Independently, the systems will establish a secure interface to export data directly to one central cancer registry. Upon receipt, the central cancer registry will perform a linkage between the ROIS data and the registry data. A set of desired data elements was developed in collaboration with the National Cancer Institute (NCI) Surveillance, Epidemiology and End Results (SEER) program and the project team of radiation oncology experts. The elements include patient demographics, diagnosis code, diagnosis date, treatment site, date of service, primary oncologist, facility, dose per fraction, total radiation dose, total number of fractions, number of sessions, dates of treatment, course dose delivered, frequency, and technique. The pilots will run at least 6 months at one large health system with multiple radiation oncology departments using ROIS from both venders. The two pilot projects will capture the data elements and export them to state registries. Exportable reports are being designed to account for industry standards of the data elements that currently exist in each ROIS. We anticipate presenting results separately after testing, but the goal is to make this process feasible regardless of site ROIS customization. To our knowledge, this is the first effort with collaboration from industry to develop a rich dataset of radiation treatment information to contribute to state and SEER registries. If feasible and scalable, these pilots will enable broad use of standard and efficient ways of reporting detailed radiation treatment data to registries. This reporting would fulfill radiation oncology reporting requirements and capture both initial and subsequent therapy. This enhanced data may be used for research to assess the utilization and outcomes of cancer treatments and improve the lives of cancer patients.

An Audit for Radiotherapy Planning and Treatment Errors From a Low–Middle-Income Country Centre

AimsTo report the findings of an audit for radiotherapy errors from a low–middle-income country (LMICs) centre. This would serve as baseline data for radiotherapy error rates, their severity and causes, in such centres where modern error reporting and learning processes still do not exist. Materials and methodsA planned cross-sectional weekly audit of electronic radiotherapy charts at the radiotherapy planning and delivery step for all patients treated with curative intent was conducted. Detailed analysis was carried out to determine the step of origin of error, time and contributing factors. They were graded as per indigenous institutional (TMC) radiotherapy error grading (TREG) system and the contributing factors identified were prioritised using the product of frequency, severity and ease of detection. ResultsIn total, 1005 consecutive radically treated patients' charts were audited, 67 radiotherapy errors affecting 60 patients, including 42 incidents and 25 near-misses were identified. Transcriptional errors (29%) were the most common type. Most errors occurred at the time of treatment planning (59.7%), with “plan information transfer to the radiation oncology information system” being the most frequently affected sub-step of the radiotherapy process (47.8%). More errors were noted at cobalt units (52/67; 77.6%) than at linear accelerators. Trend analysis showed an increased number of radiotherapy incidents on Fridays and near-misses on Mondays. Trend for increased radiotherapy errors noted in the evening over other shifts. On severity grading, most of the errors (54/60; 90%) were clinically insignificant (grade I/II). Inadequacies and non-adherence towards standard operating procedures, poor documentation and lack of continuing education were the three most prominent causes. ConclusionPreliminary data suggest a vulnerability of LMIC set-up to radiotherapy errors and emphasises the need for the development of longitudinal prospective processes, such as voluntary reporting and a continued education system, to ensure robust and comprehensive safe practises on par with centres in developed countries.