Oncology Information System Research Articles

Best practice guidelines for use of reference points in radiation oncology information systems to aggregate longitudinal dosimetric data.

Tracking patient dose in radiation oncology is challenging due to disparate electronic systems from various vendors. Treatment planning systems (TPS), radiation oncology information systems (ROIS), and electronic health records (EHR) lack uniformity, complicating dose tracking and reporting. To address this, we examined practices in multiple radiation oncology settings and proposed guidelines for current systems. A survey was conducted among members of various professional groups to understand dose reporting practices in TPS, ROIS, and EHR systems. The aim was to identify consistent components and develop guidelines. We identified six treatment scenarios where current ROIS defaults fail in accurately representing dose totals. A standardized approach involving three reference point types - Primary Treatment Plan Reference, Dose Check, and Prescription Tracking - was proposed to address these scenarios. Standardizing naming conventions for reference points was also recommended for easier integration with EHRs. The approach requires minimal modifications to existing systems and facilitates easier data transfer and display in EHRs. Standardizing reference points in commercial TPS and ROIS can bridge infrastructure gaps and improve dose tracking in complex clinical scenarios. This standardization, aligned with AAPM's TG-263, paves the way for continual development of automated, standardized, interoperable tools, enhancing the ease of sharing reference point information.

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.

Automation of ePROMs in radiation oncology and its impact on patient response and bias

PurposeThis study evaluates the impact of integrating a novel, in-house developed electronic Patient-Reported Outcome Measures (ePROMs) tool with a commercial Oncology Information System (OIS) on patient response rates and potential biases in real-world data science applications. Materials and MethodsWe designed an ePROMs tool using the NodeJS web application framework, automatically sending e-mail questionnaires to patients based on their treatment schedules in the OIS. The tool is used across various treatment sites to collect PROMs data in a real-world setting. This research examined the effects of increasing automation levels on both recruitment and response rates, as well as potential biases across different patient cohorts. Automation was implemented in three escalating levels, from telephone reminders for missing reports to minimal intervention from study nurses. ResultsFrom August 2020 to December 2023, 1,944 patients participated in the PROMs study. Our findings indicate that automating the workflows substantially reduced the patient management workload. However, higher levels of automation led to lower response rates, particularly in collecting late-phase symptoms in breast and head-and-neck cancer cohorts. Additionally, email-based PROMs introduced an age bias when recruiting new patients for the ePROMs study. Nevertheless, age was not a significant predictor of early dropout or missing symptom reports among patients participating. Notably, increased automation was significantly correlated with lower response rates in breast (p = 0.026) and head-and-neck cancer patients (p < 0.001). ConclusionIntegrating ePROMs within the OIS can significantly reduce workload and personnel resources. However, this efficiency may compromise patient responses in certain groups. A balance must be achieved between workload, resource allocation, and the sensitivity needed to detect clinically significant effects. This may necessitate customized automation levels tailored to specific cancer groups, highlighting a fundamental trade-off between operational efficiency and data quality.

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.

Assessment of the Impact of Inaccuracies in the Head and Neck Tumors Patient Positioning on the Halcyon Accelerator by the Amount of Dose-Volumetric Characteristics When Planning Radiotherapy

Purpouse. To evaluate the impact of inaccuracies in patient positioning during irradiation of tumors in the head and neck region under the control of a portal imaging device on the Halcyon accelerator on the value of CTV-PTV offsets at the contouring stage.&#x0D; Material and methods. Treatment of patients and monitoring of the position of 35 patients on the therapeutic table using a portal visualization device during each irradiation session was carried out on a Halcyon tunnel-type electron accelerator, including an irradiation planning system (Eclipse version 16.1) and the Aria oncology information system (version 15.5).&#x0D; Resuits. Based on the results of positioning and analysis of a sample of 35 patients, it was concluded that it is possible to change the CTV-PTV offsets during contouring to a smaller side for the head area and a larger one for the neck area below the C3 vertebra. These results are explained by the strong fixation of the head in the thermoplastic mask, and the high mobility of the neck due to air gaps.&#x0D; Conclusions. When creating a treatment plan, changing the CTV PTV offsets leads to a reduction in the volume of the target and moving it away from critical structures. This makes it possible to increase the accuracy of the treatment and reduce the dose load on the normal tissues surrounding the tumor. The results obtained have high practical value.

Leveraging a Real-Time Machine Learning Platform to Personalize Quality Assurance at a Large Multi-Site Oncology Practice

Peer review is a critical component of safe and effective oncology care and provides care teams with an opportunity to improve patient outcomes with predictive analytics. We describe a paradigm for incorporating predictive analytics into daily prospective peer review at a large multisite oncology center. Previously, we deployed a department-wide proprietary oncology analytics platform connected to the Electronic Medical Record System (outpatient and inpatient), Oncology Information System, Tumor Registry, Treatment Planning System and Radiology PACS. Variables extracted and calculated by the analytics platform on a nightly basis include patient demographics, past medical history, detailed pathology data, recent laboratory data, vitals, prior systemic cancer therapies, prior surgical treatments, prior radiation treatments, recent hospitalization, vital status, and current treatment details. Using this platform, we trained machine learning models for critical patient events, such as unplanned hospitalization, mortality, occurrence of grade 3 or higher toxicities, and treatment interruption. Importantly, these models leverage so-called interpretable machine learning techniques, allowing us to interrogate the models and obtain explanations for why a patient is predicted to be at higher or lower risk of an event. All such models were previously evaluated for performance on standalone testing sets. In this abstract, we propose a paradigm to include predictive analytics in our daily prospective peer review process by creating a list of patients at high risk of critical events (hospitalization, mortality, treatment interruption, and serious adverse events) and reviewing those lists in conjunction with our daily peer review conference. We also propose a process to review any gaps in clinical care (e.g., abnormal labs, decreases in patient weight, or missed progress visits) to recommend suitable interventions to the care team to reduce patient risk. A total of 64,286 radiation treatment cases were included in our analytics platform and used to train and validate our predictive models. Moving forward, we aim to peer review 20 patient charts per day using this paradigm. While numerous studies in the field of radiation oncology have focused on the benefits of machine learning when applied to contouring and personalized radiation dose, few have focused on how machine learning can improve supportive care. In this abstract, we propose a framework to introduce machine learning into quality assurance, with the goal of improving patient outcomes.

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.

Closing the gap in Indigenous Australian cancer care: Initiatives to foster cultural safety and improve access to radiation therapy

IntroductionAboriginal and Torres Strait Islander peoples receive a poorer cancer prognosis compared to non-Indigenous Australians. Indigenous access to cancer care is limited by several factors, including differences in cultural understanding surrounding cancer and distrust of mainstream health institutions. It is believed that radiation therapy is being under-utilised by the Indigenous community. In response, several strategies were implemented to increase presentation and attendance by Indigenous Australians at a rural, private radiation therapy practice. MethodsA local Indigenous health service, Traditional Owners and Elders and the clinic's Reconciliation Action Plan Working Group were consulted to implement initiatives to reduce barriers to engaging in healthcare. One of these initiatives involved community consultation to select a culturally safe, Indigenous word to name one of the clinic's linear accelerators. Indigenous patient referrals into the clinic were tracked using an oncology information system. It is expected that by implementing several culturally safe strategies, there would be an increase in utilisation of the radiation therapy service and therefore, a decrease in Indigenous cancer burden across the local region. If strategies were yielding positive outcomes, there would be scope to implement these at other sites across a broader monitored network. ResultsCurrently, service utilisation is being monitored to determine whether initiatives have been successful. Initial data evaluation has been conducted and results are showing increased service utilisation by the Indigenous community. ConclusionWork has been undertaken to implement strategies to improve cultural safety and improve access to radiation therapy for Indigenous cancer patients. Preliminary results show an increase in the number of presentations to the clinic for radiation therapy treatment.

Inter-hospital variation in data collection, radiotherapy treatment, and survival in patients with head and neck cancer: A multisite study

Background and purposeInter-hospital inequalities in head and neck cancer (HNC) survival may exist due to variation in radiotherapy treatment-related factors. This study investigated inter-hospital variation in data collection, primary radiotherapy treatment, and survival in HNC patients from an Australian setting. Materials and methodsData collected in oncology information systems (OIS) from seven Australian hospitals was extracted for 3,182 adults treated with curative radiotherapy, with or without surgery or chemotherapy, for primary, non-metastatic squamous cell carcinoma of the head and neck (2000–2017). Death data was sourced from the National Death Index using record linkage. Multivariable Cox regression was used to assess the association between survival and hospital. ResultsInter-hospital variation in data collection, primary radiotherapy dose, and five-year HNC-related death was detected. Completion of eleven fields ranged from 66%-98%. Primary radiotherapy treated Tis-T1N0 glottic and any stage oral cavity and oropharynx cancers received significantly different time-corrected biologically equivalent dose in two gray fractions (EQD2T) by hospital, with observed deviation from Australian radiotherapy guidelines. Increased EQD2T dose was associated with a reduced risk of five-year HNC-related death in all patients and those treated with primary radiotherapy. Hospital, tumour site, and T and N classification were also identified as independent prognostic factors for five-year HNC-related death in all patients treated with radiotherapy. ConclusionUnexplained variation exists in HNC-related death in patients treated at Australian hospitals. Available routinely collected data in OIS are insufficient to explain variation in survival. Innovative data collection, extraction, and classification practices are needed to inform clinical practice.

Increasing Quality and Efficiency of the Radiotherapy Treatment Planning Process by Constructing and Implementing a Workflow-Monitoring Application.

To demonstrate how the efficiency of the treatment planning processes of a university radiation oncology department (2,500 new patients/year) could be improved by constructing and implementing a workflow-monitoring application. A web-based application was developed in house, which enhanced the process management tools of the clinic's oncology information system. The application calculates the days left for the next task in the treatment planning process and visualizes the information on a browser-based whiteboard. Workflow monitoring considers tumor types (breast, prostate, lung, etc) and treatment techniques and is backward planned from the planned start of treatment. The effect of introducing this application was analyzed over four phases: (1) baseline data without the workflow-monitoring application, (2) after introducing workflow visualization via a browser-based whiteboard, (3) after upgrading the whiteboard and introducing backend rules, and (4) after updating these rules on the basis of data from the previous phase. Implementing the workflow-monitoring application and the introduced measures significantly reduced delays and, consequently, stress and a negative working atmosphere in the treatment planning process. Most notably, the amount of last-minute physics checks (on the day of the treatment start) could be reduced by 50%. The study showed what measures can help organize and prioritize the treatment planning workflow. The increased efficiency is believed to improve the quality and reduce the risk of human error.

Impact of log file source and data frequency on accuracy of log file-based patient specific quality assurance

Performing phantom measurements for patient-specific quality assurance (PSQA) adds a significant amount of time to the adaptive radiotherapy procedure. Log file based PSQA can be used to increase the efficiency of this process. This study compared the dosimetric accuracy of high-frequency linear accelerator (Linac) log files and low-frequency log data stored in the oncology information system (OIS). Thirty patients were included, that were recently treated in the head and neck (HN), brain, and prostate region with volumetric modulated arc therapy (VMAT) and an additional ten patients treated using stereotactic body radiation therapy (SBRT) with 3D-conformal radiotherapy (3D-CRT) technique. Log data containing a single fraction were used to calculate the dose distributions. The dosimetric differences between Linac log files and OIS logs were evaluated with a gamma analysis with 2%/2 mm criterion and dose threshold of 30%. The original treatment plan was used as a reference. Moreover, DVH parameters of D98%, D50%, and D2% of the planning-target volume (PTV) and dose to several organs at risk (OARs) were reported. Significant differences in dose distributions between the two log types and the original dose were observed for PTV D98% and D2% (r < 0.001) for HN cases, PTV D98% (r = 0.005) for brain cases, and PTV D50% (r = 0.015) for prostate cases. No significant differences were found between the two log types with respect to D50%. The root mean square (RMS) error of the leaf positions of the OIS log was approximately twice the RMS error of the Linac log file for VMAT plans, but identical for 3D-CRT plans. The relationship between the gamma pass rate and the RMS error showed a moderate correlation for the Linac log files (r = −0.58, p < 0.001) and strong correlation for OIS logs (r = −0.71, p < 0.001). Furthermore, all doses calculated using Linac log files and OIS log data had a GPR >90% for an RMS error < 3.3 mm. Based on these findings, a tolerance limit of RMS error of 3.3 mm for considering OIS log based PSQA was established. Nevertheless, the OIS log data quality should be improved to achieve adequate PSQA.

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.

Automatic quality assurance of radiotherapy treatment plans using Bayesian networks: A multi-institutional study.

Artificial intelligence applications in radiation oncology have been the focus of study in the last decade. The introduction of automated and intelligent solutions for routine clinical tasks, such as treatment planning and quality assurance, has the potential to increase safety and efficiency of radiotherapy. In this work, we present a multi-institutional study across three different institutions internationally on a Bayesian network (BN)-based initial plan review assistive tool that alerts radiotherapy professionals for potential erroneous or suboptimal treatment plans. Clinical data were collected from the oncology information systems in three institutes in Europe (Maastro clinic - 8753 patients treated between 2012 and 2020) and the United States of America (University of Vermont Medical Center [UVMMC] - 2733 patients, University of Washington [UW] - 6180 patients, treated between 2018 and 2021). We trained the BN model to detect potential errors in radiotherapy treatment plans using different combinations of institutional data and performed single-site and cross-site validation with simulated plans with embedded errors. The simulated errors consisted of three different categories: i) patient setup, ii) treatment planning and iii) prescription. We also compared the strategy of using only diagnostic parameters or all variables as evidence for the BN. We evaluated the model performance utilizing the area under the receiver-operating characteristic curve (AUC). The best network performance was observed when the BN model is trained and validated using the dataset in the same center. In particular, the testing and validation using UVMMC data has achieved an AUC of 0.92 with all parameters used as evidence. In cross-validation studies, we observed that the BN model performed better when it was trained and validated in institutes with similar technology and treatment protocols (for instance, when testing on UVMMC data, the model trained on UW data achieved an AUC of 0.84, compared with an AUC of 0.64 for the model trained on Maastro data). Also, combining training data from larger clinics (UW and Maastro clinic) and using it on smaller clinics (UVMMC) leads to satisfactory performance with an AUC of 0.85. Lastly, we found that in general the BN model performed better when all variables are considered as evidence. We have developed and validated a Bayesian network model to assist initial treatment plan review using multi-institutional data with different technology and clinical practices. The model has shown good performance even when trained on data from clinics with divergent profiles, suggesting that the model is able to adapt to different data distributions.

An evaluation of radiation therapy patient body mass index trends and potential impact on departmental resource planning.

Radiation therapy (RT) offers a less invasive management option for bariatric cancer patients. As the proportion of Australians categorised overweight or obese approaches 70%, it is not well understood how this growth will impact RT departments. The aim of this study was to evaluate the current and potential future body mass index (BMI) of RT patients at one centre, with the purpose of identifying variables that may impact resource planning decisions. De-identified demographic data including gender, age, diagnosis code, activity code and BMI were obtained from MOSAIQ® oncology information system for 5548 courses of RT commenced between 2017 and 2020, and retrospectively analysed. Descriptive statistics were used to summarise the data. Simple and multiple linear regression was used to analyse for statistically significant relationships between variables. Of all patient courses, 64% were overweight or obese. Average BMI increased over time by 0.3 kg/m2 per year. Courses related to the young and elderly had a lower average BMI. Breast, brain/skull, and pelvis/prostate treatment sites had a significant association with a higher average BMI. Thorax treatment sites had a lower average BMI, but this average is increasing at the fastest rate of all treatment sites. Prone breast courses had an average BMI 5.58 kg/m2 higher than IMRT/VMAT courses. Results demonstrate that patient BMI is increasing. Resources related to breast courses (breast board, prone board) and thorax courses (lung board) may experience increased strain in the future. Modifications to department workflow and scheduling are likely required. Further research into staffing implications is recommended.

The Utility of Oncology Information Systems for Prognostic Modelling in Head and Neck Cancer

Cancer centres rely on electronic information in oncology information systems (OIS) to guide patient care. We investigated the completeness and accuracy of routinely collected head and neck cancer (HNC) data sourced from an OIS for suitability in prognostic modelling and other research. Three hundred and fifty-three adults diagnosed from 2000 to 2017 with head and neck squamous cell carcinoma, treated with radiotherapy, were eligible. Thirteen clinically relevant variables in HNC prognosis were extracted from a single-centre OIS and compared to that compiled separately in a research dataset. These two datasets were compared for agreement using Cohen’s kappa coefficient for categorical variables, and intraclass correlation coefficients for continuous variables. Research data was 96% complete compared to 84% for OIS data. Agreement was perfect for gender (κ = 1.000), high for age (κ = 0.993), site (κ = 0.992), T (κ = 0.851) and N (κ = 0.812) stage, radiotherapy dose (κ = 0.889), fractions (κ = 0.856), and duration (κ = 0.818), and chemotherapy treatment (κ = 0.871), substantial for overall stage (κ = 0.791) and vital status (κ = 0.689), moderate for grade (κ = 0.547), and poor for performance status (κ = 0.110). Thirty-one other variables were poorly captured and could not be statistically compared. Documentation of clinical information within the OIS for HNC patients is routine practice; however, OIS data was less correct and complete than data collected for research purposes. Substandard collection of routine data may hinder advancements in patient care. Improved data entry, integration with clinical activities and workflows, system usability, data dictionaries, and training are necessary for OIS data to generate robust research. Data mining from clinical documents may supplement structured data collection.

Machine Learning and Nomogram Prognostic Modeling for 2-Year Head and Neck Cancer-Specific Survival Using Electronic Health Record Data: A Multisite Study.

There is limited knowledge of the prediction of 2-year cancer-specific survival (CSS) in the head and neck cancer (HNC) population. The aim of this study is to develop and validate machine learning models and a nomogram for the prediction of 2-year CSS in patients with HNC using real-world data collected by major teaching and tertiary referral hospitals in New South Wales (NSW), Australia. Data collected in oncology information systems at multiple NSW Cancer Centres were extracted for 2,953 eligible adults diagnosed between 2000 and 2017 with squamous cell carcinoma of the head and neck. Death data were sourced from the National Death Index using record linkage. Machine learning and Cox regression/nomogram models were developed and internally validated in Python and R, respectively. Machine learning models demonstrated highest performance (C-index) in the larynx and nasopharynx cohorts (0.82), followed by the oropharynx (0.79) and the hypopharynx and oral cavity cohorts (0.73). In the whole HNC population, C-indexes of 0.79 and 0.70 and Brier scores of 0.10 and 0.27 were reported for the machine learning and nomogram model, respectively. Cox regression analysis identified age, T and N classification, and time-corrected biologic equivalent dose in two gray fractions as independent prognostic factors for 2-year CSS. N classification was the most important feature used for prediction in the machine learning model followed by age. Machine learning and nomogram analysis predicted 2-year CSS with high performance using routinely collected and complete clinical information extracted from oncology information systems. These models function as visual decision-making tools to guide radiotherapy treatment decisions and provide insight into the prediction of survival outcomes in patients with HNC.

Treatment time of image-guided radiotherapy with a Halcyon 2.0 system

IntroductionThe treatment fraction time is a key indicator of the external beam radiotherapy process. The Halcyon system was designed to improve the clinical workflow, according to the manufacturer (Varian Medical Systems). Few works studied the actual delivery efficiency of the Halcyon system. This work analyzed the treatment time on a Halcyon 2.0 unit for a variety of sites along a period of 9 months. Materials and MethodsTreatment time included patient setup, image acquisition, image-guided online couch correction, and radiation delivery time. Data were extracted from the ARIA oncology information system and were studied as a function of the treatment site, the delivery modality, and the time from the first day of treatments with the Halcyon 2.0 system in our institution. ResultsA total of 8599 fractions were delivered during the analyzed period (69.5% from VMAT plans, and 30.5% from IMRT plans). The number of fractions by site ranged from 30 for anal canal to 1933 for prostate. Five sites (prostate, lung, pelvis with prostate, breast, and gynecological sites) accounted for the 84% of the fractions. After a 2-week adaptation period of the staff, the daily mean treatment time was reduced to less than 12 min. The mean treatment time of all the fractions amounted to 10.5 ± 3.8 min. ConclusionsThe Halcyon 2.0 allowed delivering online image-guided radiation therapy in all fractions with total treatment time consistently below the 12-min standard time slot, for most of the analyzed treatment sites.