The use of dose surface maps as a tool to investigate spatial dose delivery accuracy for the rectum during prostate radiotherapy.

PURPOSE: This study aims to address the lack of spatial dose comparisons of planned and delivered rectal doses during prostate radiotherapy by using dose-surface maps (DSMs) to analyze dose delivery accuracy and comparing these results to those derived using DVHs. METHODS: Two independent cohorts were used in this study: twenty patients treated with 36.25 Gy in five fractions (SBRT) and 20 treated with 60 Gy in 20 fractions (IMRT). Daily delivered rectum doses for each patient were retrospectively calculated using daily CBCT images. For each cohort, planned and average-delivered DVHs were generated and compared, as were planned and accumulated DSMs. Permutation testing was used to identify DVH metrics and DSM regions where significant dose differences occurred. Changes in rectal volume and position between planning and delivery were also evaluated to determine possible correlation to dosimetric changes. RESULTS: For both cohorts, DVHs and DSMs reported conflicting findings on how planned and delivered rectum doses differed from each other. DVH analysis determined average-delivered DVHs were on average 7.1% ± 7.6% (p ≤ 0.002) and 5.0 ± 7.4% (p ≤ 0.021) higher than planned for the IMRT and SBRT cohorts, respectively. Meanwhile, DSM analysis found average delivered posterior rectal wall dose was 3.8 ± 0.6 Gy (p = 0.014) lower than planned in the IMRT cohort and no significant dose differences in the SBRT cohort. Observed dose differences were moderately correlated with anterior-posterior rectal wall motion, as well as PTV superior-inferior motion in the IMRT cohort. Evidence of both these relationships were discernable in DSMs. CONCLUSION: DSMs enabled spatial investigations of planned and delivered doses can uncover associations with interfraction motion that are otherwise masked in DVHs. Investigations of dose delivery accuracy in radiotherapy may benefit from using DSMs over DVHs for certain organs such as the rectum.

More than one way to skin a dose volume: the impact of dose-surface map calculation approach on study reproducibility.

Objective.Dose-surface maps (DSMs) provide spatial representations of the radiation dose to organ surfaces during radiotherapy and are a valuable tool for identifying dose deposition patterns that are predictive of radiation toxicities. Over the years, many different DSM calculation approaches have been introduced and used in dose-outcome studies. However, little consideration has been given to how these calculation approaches may be impacting the reproducibility of studies in the field. Therefore, we conducted an investigation to determine the level of equivalence of DSMs calculated with different approaches and their subsequent impact on study results.Approach.Rectum and bladder DSMs were calculated for 20 prostate radiotherapy patients using combinations of the most common slice orientation and spacing styles in the literature. Equivalence of differently calculated DSMs was evaluated using pixel-wise comparisons and DSM features (rectum only). Finally, mock cohort comparison studies were conducted with DSMs calculated using each approach to determine the level of dosimetric study reproducibility between calculation approaches.Main results.We found that rectum DSMs calculated using the planar and non-coplanar orientation styles were non-equivalent in the posterior rectal region and that equivalence of DSMs calculated with different slice spacing styles was conditional on the choice of inter-slice distance used. DSM features were highly sensitive to choice of slice orientation style and DSM sampling resolution. Finally, while general result trends were consistent between the comparison studies performed using different DSMs, statisitically significant subregions and features could vary greatly in position and magnitude.Significance.We have determined that DSMs calculated with different calculation approaches are frequently non-equivalent and can lead to differing conclusions between studies performed using the same dataset. We recommend that the DSM research community work to establish consensus calculation approaches to ensure reproducibility within the field.

Design and implementation of a prototype radiotherapy menu in a patient portal.

PURPOSE: Radiotherapy patients often face undue anxiety due to misconceptions about radiation and their inability to visualize their upcoming treatments. Access to their personal treatment plans is one way in which pre-treatment anxiety may be reduced. But radiotherapy data are quite complex, requiring specialized software for display and necessitating personalized explanations for patients to understand them. Therefore, our goal was to design and implement a novel radiotherapy menu in a patient portal to improve patient access to and understanding of their radiotherapy treatment plans. METHODS: A prototype radiotherapy menu was developed in our institution's patient portal following a participatory stakeholder co-design methodology. Customizable page templates were designed to render key radiotherapy data in the portal's patient-facing mobile phone app. DICOM-RT data were used to provide patients with relevant treatment parameters and generate pre-treatment 3D visualizations of planned treatment beams, while the mCODE data standard was used to provide post-treatment summaries of the delivered treatments. A focus group was conducted to gather initial patient feedback on the menu. RESULTS: Pre-treatment: the radiotherapy menu provides patients with a personalized treatment plan overview, including a personalized explanation of their treatment, along with an interactive 3D rendering of their body, and treatment beams for visualization. Post-treatment: a summary of the delivered radiotherapy is provided, allowing patients to retain a concise personal record of their treatment that can easily be shared with future healthcare providers. Focus group feedback was overwhelmingly positive. Patients highlighted how the intuitive presentation of their complex radiotherapy data would better prepare them for their radiation treatments. CONCLUSIONS: We successfully designed and implemented a prototype radiotherapy menu in our institution's patient portal that improves patient access to and understanding of their radiotherapy data. We used the mCODE data standard to generate post-treatment summaries in a way that is easily shareable and interoperable.

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

PURPOSE: The ongoing lack of data standardization severely undermines the potential for automated learning from the vast amount of information routinely archived in electronic health records (EHRs), radiation oncology information systems, treatment planning systems, and other cancer care and outcomes databases. We sought to create a standardized ontology for clinical data, social determinants of health, and other radiation oncology concepts and interrelationships. METHODS AND MATERIALS: The American Association of Physicists in Medicine's Big Data Science Committee was initiated in July 2019 to explore common ground from the stakeholders' collective experience of issues that typically compromise the formation of large inter- and intra-institutional databases from EHRs. The Big Data Science Committee adopted an iterative, cyclical approach to engaging stakeholders beyond its membership to optimize the integration of diverse perspectives from the community. RESULTS: We developed the Operational Ontology for Oncology (O3), which identified 42 key elements, 359 attributes, 144 value sets, and 155 relationships ranked in relative importance of clinical significance, likelihood of availability in EHRs, and the ability to modify routine clinical processes to permit aggregation. Recommendations are provided for best use and development of the O3 to 4 constituencies: device manufacturers, centers of clinical care, researchers, and professional societies. CONCLUSIONS: O3 is designed to extend and interoperate with existing global infrastructure and data science standards. The implementation of these recommendations will lower the barriers for aggregation of information that could be used to create large, representative, findable, accessible, interoperable, and reusable data sets to support the scientific objectives of grant programs. The construction of comprehensive "real-world" data sets and application of advanced analytical techniques, including artificial intelligence, holds the potential to revolutionize patient management and improve outcomes by leveraging increased access to information derived from larger, more representative data sets.

A study of indirect action's impact on simulated neutron-induced DNA damage.

Objective.The risk of radiobiological stochastic effects associated with neutrons is strongly energy dependent. Recent Monte Carlo studies simulating neutron-irradiated nuclear DNA have demonstrated that this energy dependence is correlated with the relative biological effectiveness (RBE) of neutrons to inflict DNA damage clusters that contain difficult-to-repair double-strand breaks. However, these previous investigations were either limited to modeling direct radiation action or considered the effects of both direct and indirect action together without distinguishing between the two. In this study, we aimed to quantify the influence of indirect action in neutron irradiation scenarios and acquire novel estimations of the energy-dependent neutron RBE for inducing DNA damage clusters due to both direct and indirect action.Approach.We explored the role of indirect action in neutron-induced DNA damage by integrating a validated indirect action model into our existing simulation pipeline. Using this pipeline, we performed track-structure simulations of monoenergetic neutron irradiations (1 eV to 10 MeV) in a nuclear DNA model and analyzed the resulting simple and clustered DNA lesions. We repeated the irradiation simulations for 250 keV x-rays that acted as our reference radiation.Main results.Including indirect action significantly increased the occurrence of DNA lesions. We found that indirect action tends to amplify the damage due to direct action by inducing DNA lesions in the vicinity of directly-induced lesions, resulting in additional and larger damage clusters. Our neutron RBE results are qualitatively similar to but lower in magnitude than the established radiation protection factors and the results of previous similar investigations, due to the greater relative impact of indirect action in photon-induced damage than in neutron-induced damage.Significance.Although our model for neutron-induced DNA damage has some important limitations, our findings suggest that the energy-dependent risk of neutron-induced stochastic effects may not be completely modeled alone by the relative potential of neutrons to inflict clustered lesions via direct and indirect action in DNA damage.

A Scalable Radiomics- and Natural Language Processing-Based Machine Learning Pipeline to Distinguish Between Painful and Painless Thoracic Spinal Bone Metastases: Retrospective Algorithm Development and Validation Study.

BACKGROUND: The identification of objective pain biomarkers can contribute to an improved understanding of pain, as well as its prognosis and better management. Hence, it has the potential to improve the quality of life of patients with cancer. Artificial intelligence can aid in the extraction of objective pain biomarkers for patients with cancer with bone metastases (BMs). OBJECTIVE: This study aimed to develop and evaluate a scalable natural language processing (NLP)- and radiomics-based machine learning pipeline to differentiate between painless and painful BM lesions in simulation computed tomography (CT) images using imaging features (biomarkers) extracted from lesion center point-based regions of interest (ROIs). METHODS: Patients treated at our comprehensive cancer center who received palliative radiotherapy for thoracic spine BM between January 2016 and September 2019 were included in this retrospective study. Physician-reported pain scores were extracted automatically from radiation oncology consultation notes using an NLP pipeline. BM center points were manually pinpointed on CT images by radiation oncologists. Nested ROIs with various diameters were automatically delineated around these expert-identified BM center points, and radiomics features were extracted from each ROI. Synthetic Minority Oversampling Technique resampling, the Least Absolute Shrinkage And Selection Operator feature selection method, and various machine learning classifiers were evaluated using precision, recall, F1-score, and area under the receiver operating characteristic curve. RESULTS: Radiation therapy consultation notes and simulation CT images of 176 patients (mean age 66, SD 14 years; 95 males) with thoracic spine BM were included in this study. After BM center point identification, 107 radiomics features were extracted from each spherical ROI using pyradiomics. Data were divided into 70% and 30% training and hold-out test sets, respectively. In the test set, the accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve of our best performing model (neural network classifier on an ensemble ROI) were 0.82 (132/163), 0.59 (16/27), 0.85 (116/136), and 0.83, respectively. CONCLUSIONS: Our NLP- and radiomics-based machine learning pipeline was successful in differentiating between painful and painless BM lesions. It is intrinsically scalable by using NLP to extract pain scores from clinical notes and by requiring only center points to identify BM lesions in CT images.

Technical note: rtdsm-An open-source software for radiotherapy dose-surface map generation and analysis.

BACKGROUND: Dose-outcome studies in radiation oncology have historically excluded spatial information due to dose-volume histograms being the most dominant source of dosimetric information. In recent years, dose-surface maps (DSMs) have become increasingly popular for characterization of spatial dose distributions and identification of radiosensitive subregions for hollow organs. However, methodological variations and lack of open-source, publicly offered code-sharing between research groups have limited reproducibility and wider adoption. PURPOSE: This paper presents rtdsm, an open-source software for DSM calculation with the intent to improve the reproducibility of and the access to DSM-based research in medical physics and radiation oncology. METHODS: A literature review was conducted to identify essential functionalities and prevailing calculation approaches to guide development. The described software has been designed to calculate DSMs from DICOM data with a high degree of user customizability and to facilitate DSM feature analysis. Core functionalities include DSM calculation, equivalent dose conversions, common DSM feature extraction, and simple DSM accumulation. RESULTS: A number of use cases were used to qualitatively and quantitatively demonstrate the use and usefulness of rtdsm. Specifically, two DSM slicing methods, planar and noncoplanar, were implemented and tested, and the effects of method choice on output DSMs were demonstrated. An example comparison of DSMs from two different treatments was used to highlight the use cases of various built-in analysis functions for equivalent dose conversion and DSM feature extraction. CONCLUSIONS: We developed and implemented rtdsm as a standalone software that provides all essential functionalities required to perform a DSM-based study. It has been made freely accessible under an open-source license on Github to encourage collaboration and community use.

Radiomics-based machine learning models to distinguish between metastatic and healthy bone using lesion-center-based geometric regions of interest.

Radiomics-based machine learning classifiers have shown potential for detecting bone metastases (BM) and for evaluating BM response to radiotherapy (RT). However, current radiomics models require large datasets of images with expert-segmented 3D regions of interest (ROIs). Full ROI segmentation is time consuming and oncologists often outline just RT treatment fields in clinical practice. This presents a challenge for real-world radiomics research. As such, a method that simplifies BM identification but does not compromise the power of radiomics is needed. The objective of this study was to investigate the feasibility of radiomics models for BM detection using lesion-center-based geometric ROIs. The planning-CT images of 170 patients with non-metastatic lung cancer and 189 patients with spinal BM were used. The point locations of 631 BM and 674 healthy bone (HB) regions were identified by experts. ROIs with various geometric shapes were centered and automatically delineated on the identified locations, and 107 radiomics features were extracted. Various feature selection methods and machine learning classifiers were evaluated. Our point-based radiomics pipeline was successful in differentiating BM from HB. Lesion-center-based segmentation approach greatly simplifies the process of preparing images for use in radiomics studies and avoids the bottleneck of full ROI segmentation.

Striving to Fill in Gaps between Clinical Practice and Standards: The Evolution of a Pan-Canadian Approach to Patient-Reported Outcomes Use.

Despite the known importance and necessity of the standardized collection and use of patient-reported outcomes (PROs), there remain challenges to successful clinical implementation. Facilitated through a quality improvement initiative spearheaded by the Canadian Partnership for Quality Radiotherapy (CPQR), and now guided by the Canadian Association of Radiation Oncology (CARO)'s Quality and Standards Committee, patient representatives and early-adopter radiation treatment programs continue to champion the expansion of PROs initiatives across the country. The current review discusses the evolution of a pan-Canadian approach to PROs use, striving to fill in gaps between clinical practice and guideline recommendations through multi-centre and multidisciplinary collaboration.

High-Accuracy Relative Biological Effectiveness Values Following Low-Dose Thermal Neutron Exposures Support Bimodal Quality Factor Response with Neutron Energy.

Theoretical evaluations indicate the radiation weighting factor for thermal neutrons differs from the current International Commission on Radiological Protection (ICRP) recommended value of 2.5, which has radiation protection implications for high-energy radiotherapy, inside spacecraft, on the lunar or Martian surface, and in nuclear reactor workplaces. We examined the relative biological effectiveness (RBE) of DNA damage generated by thermal neutrons compared to gamma radiation. Whole blood was irradiated by 64 meV thermal neutrons from the National Research Universal reactor. DNA damage and erroneous DNA double-strand break repair was evaluated by dicentric chromosome assay (DCA) and cytokinesis-block micronucleus (CBMN) assay with low doses ranging 6-85 mGy. Linear dose responses were observed. Significant DNA aberration clustering was found indicative of high ionizing density radiation. When the dose contribution of both the 14N(n,p)14C and 1H(n,γ)2H capture reactions were considered, the DCA and the CBMN assays generated similar maximum RBE values of 11.3 ± 1.6 and 9.0 ± 1.1, respectively. Consequently, thermal neutron RBE is approximately four times higher than the current ICRP radiation weighting factor value of 2.5. This lends support to bimodal peaks in the quality factor for RBE neutron energy response, underlining the importance of radiological protection against thermal neutron exposures.

Developing an mHealth Application to Coordinate Nurse-Provided Respite Care Services for Families Coping With Palliative-Stage Cancer: Protocol for a User-Centered Design Study.

BACKGROUND: Patients living with palliative-stage cancer frequently require intensive care from their family caregivers. Without adequate community support services, patients are at risk of receiving inadequate care, and family caregivers are at risk for depression and poor health. For such families, in-home respite care can be invaluable, particularly when the services are flexible and staffed by trusted care providers, such as nurses. Other industries are using mobile apps to make services more flexible. However, few apps have been developed to coordinate nurse-provided respite care services, and to our knowledge, none have been designed in conjunction with families affected by cancer. OBJECTIVE: The aim of this study is to develop a mobile health (mHealth) app prototype for coordinating flexible and trusted in-home respite care services provided by nurses to families coping with palliative-stage cancer in Québec, Canada. METHODS: This user-centered design research comprises the core component of the iRespite Services iRépit research program. For this study, we are recruiting 20 nurses, 15 adults with palliative-stage cancer, and 20 of their family caregivers, from two palliative oncology hospital departments and one palliative home-care community partner. Overseen by an Expert Council, remote data collection will occur over three research phases guided by the iterative Information Systems Research Framework: Phase 1, brainstorming potential app solutions to challenging respite care scenarios, for better supporting the respite needs of both family caregivers and care recipients; Phase 2, evaluating low-fidelity proofs of concept for potential app designs; and Phase 3, usability testing of a high-fidelity interactive proof of concept that will then be programmed into an app prototype. Qualitative and quantitative data will be descriptively analyzed within each phase and triangulated to refine the app features. RESULTS: We anticipate that preliminary results will be available by Spring 2022. CONCLUSIONS: An app prototype will be developed that has sufficient complimentary evidence to support future pilot testing in the community. Such an app could improve the delivery of community respite care services provided to families with palliative-stage cancer in Québec, supporting death at home, which is where most patients and their families wish to be. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/34652.

Natural language processing and machine learning to assist radiation oncology incident learning.

PURPOSE: To develop a Natural Language Processing (NLP) and Machine Learning (ML) pipeline that can be integrated into an Incident Learning System (ILS) to assist radiation oncology incident learning by semi-automating incident classification. Our goal was to develop ML models that can generate label recommendations, arranged according to their likelihoods, for three data elements in Canadian NSIR-RT taxonomy. METHODS: Over 6000 incident reports were gathered from the Canadian national ILS as well as our local ILS database. Incident descriptions from these reports were processed using various NLP techniques. The processed data with the expert-generated labels were used to train and evaluate over 500 multi-output ML algorithms. The top three models were identified and tuned for each of three different taxonomy data elements, namely: (1) process step where the incident occurred, (2) problem type of the incident and (3) the contributing factors of the incident. The best-performing model after tuning was identified for each data element and tested on unseen data. RESULTS: The MultiOutputRegressor extended Linear SVR models performed best on the three data elements. On testing, our models ranked the most appropriate label 1.48 ± 0.03, 1.73 ± 0.05 and 2.66 ± 0.08 for process-step, problem-type and contributing factors respectively. CONCLUSIONS: We developed NLP-ML models that can perform incident classification. These models will be integrated into our ILS to generate a drop-down menu. This semi-automated feature has the potential to improve the usability, accuracy and efficiency of our radiation oncology ILS.

Towards the characterization of neutron carcinogenesis through direct action simulations of clustered DNA damage.

Neutron exposure poses a unique radiation protection concern because neutrons have a large, energy-dependent relative biological effectiveness (RBE) for stochastic effects. Recent computational studies on the microdosimetric properties of neutron dose deposition have implicated clustered DNA damage as a likely contributor to this marked energy dependence. So far, publications have focused solely on neutron RBE for inducing clusters of DNA damage containing two or more DNA double strand breaks (DSBs). In this study, we have conducted a novel assessment of neutron RBE for inducing all types of clustered DNA damage that contain two or more lesions, stratified by whether the clusters contain DSBs (complex DSB clusters) or not (non-DSB clusters). This assessment was conducted for eighteen initial neutron energies between 1 eV and 10 MeV as well as a reference radiation of 250 keV x-rays. We also examined the energy dependence of cluster length and cluster complexity because these factors are believed to impact the DNA repair process. To carry out our investigation, we developed a user-friendly TOPAS-nBio application that includes a custom nuclear DNA model and a novel algorithm for recording clustered DNA damage. We found that neutron RBE for inducing complex DSB clusters exhibited similar energy dependence to the canonical neutron RBE for stochastic radiobiological effects, at multiple depths in human tissue. Qualitatively similar results were obtained for non-DSB clusters, although the quantitative agreement was lower. Additionally we identified a significant neutron energy dependence in the average length and complexity of clustered lesions. These results support the idea that many types of clustered DNA damage contribute to the energy dependence of neutron RBE for stochastic radiobiological effects and imply that the size and constituent lesions of individual clusters should be taken into account when modeling DNA repair. Our results were qualitatively consistent for (i) multiple radiation doses (including a low-dose 0.1 Gy irradiation), (ii) variations in the maximal lesion separation distance used to define a cluster, and (iii) two distinct collections of physics models used to govern particle transport. Our complete TOPAS-nBio application has been released under an open-source license to enable others to independently validate our work and to expand upon it.

Development of a generalizable natural language processing pipeline to extract physician-reported pain from clinical reports: Generated using publicly-available datasets and tested on institutional clinical reports for cancer patients with bone metastases.

OBJECTIVE: The majority of cancer patients suffer from severe pain at the advanced stage of their illness. In most cases, cancer pain is underestimated by clinical staff and is not properly managed until it reaches a critical stage. Therefore, detecting and addressing cancer pain early can potentially improve the quality of life of cancer patients. The objective of this research project was to develop a generalizable Natural Language Processing (NLP) pipeline to find and classify physician-reported pain in the radiation oncology consultation notes of cancer patients with bone metastases. MATERIALS AND METHODS: The texts of 1249 publicly-available hospital discharge notes in the i2b2 database were used as a training and validation set. The MetaMap and NegEx algorithms were implemented for medical terms extraction. Sets of NLP rules were developed to score pain terms in each note. By averaging pain scores, each note was assigned to one of the three verbally-declared pain (VDP) labels, including no pain, pain, and no mention of pain. Without further training, the generalizability of our pipeline in scoring individual pain terms was tested independently using 30 hospital discharge notes from the MIMIC-III database and 30 consultation notes of cancer patients with bone metastasis from our institution's radiation oncology electronic health record. Finally, 150 notes from our institution were used to assess the pipeline's performance at assigning VDP. RESULTS: Our NLP pipeline successfully detected and quantified pain in the i2b2 summary notes with 93% overall precision and 92% overall recall. Testing on the MIMIC-III database achieved precision and recall of 91% and 86% respectively. The pipeline successfully detected pain with 89% precision and 82% recall on our institutional radiation oncology corpus. Finally, our pipeline assigned a VDP to each note in our institutional corpus with 84% and 82% precision and recall, respectively. CONCLUSION: Our NLP pipeline enables the detection and classification of physician-reported pain in our radiation oncology corpus. This portable and ready-to-use pipeline can be used to automatically extract and classify physician-reported pain from clinical notes where the pain is not otherwise documented through structured data entry.

Satisfaction among Cancer Patients Undergoing Radiotherapy during the COVID-19 Pandemic: An Institutional Experience.

The COVID-19 pandemic has shifted oncology practices to prioritize patient safety while maintaining necessary treatment delivery. We obtained patient feedback on pandemic-based practices in our radiotherapy department to improve quality of patient care and amend policies as needed. We developed a piloted questionnaire which quantitatively and qualitatively assessed patients' pandemic-related concerns and satisfaction with specific elements of their care. Adult patients who were treated at our Centre between 23 March and 31 May 2020, had initial consultation via telemedicine, and received at least five outpatient fractions of radiotherapy were invited to complete the survey by telephone or online. Relative frequencies of categorical and ordinal responses were then calculated. Fifty-three (48%) out of 110 eligible patients responded: 32 patients by phone and 21 patients online. Eighteen participants (34%) admitted to feeling anxious about hospital appointments, and only five (9%) reported treatment delays. Forty-eight patients (91%) reported satisfaction with their initial telemedicine appointment. The majority of patients indicated that healthcare workers took appropriate precautions, making them feel safe. Overall, all 53 patients (100%) reported being satisfied with their treatment experience during the pandemic. Patient feedback is needed to provide the highest quality of patient care as we adapt to the current reality.

Acceptability of a Patient Portal (Opal) in HIV Clinical Care: A Feasibility Study.

Opal (opalmedapps.com), a patient portal in use at the Cedars Cancer Centre of the McGill University Health Centre (MUHC) (Montreal, Canada), gives cancer patients access to their medical records, collects information on patient-reported outcome measures (PROMs), and has demonstrated patient satisfaction with care. This feasibility study aims to evaluate Opal's potential acceptability in the context of HIV care. People living with HIV (PLWH) and their healthcare providers (HCPs) completed cross-sectional surveys from August 2019 to February 2020 at large HIV centers, including the Chronic Viral Illness Service of the MUHC, and other HIV clinical sites in Montreal and Paris, France. This study comprised 114 PLWH (mean age 48 years old, SD = 12.4), including 74% men, 24% women, and 2% transgender or other; and 31 HCPs (mean age 46.5 years old, SD = 11.4), including 32% men, 65% women, and 3% other. Ownership of smartphones and tablets was high (93% PLWH, 96% HCPs), and participants were willing to use Opal (74% PLWH, 68% HCPs). Participants were interested in most Opal functions and PROMs, particularly PROMs capturing quality of life (89% PLWH, 77% HCPs), experience of healthcare (86% PLWH, 97% HCPs), and HIV self-management (92% PLWH, 97% HCPs). This study suggests Opal has high acceptability and potential usefulness as perceived by PLWH and HCPs.

Exploring Cancer Patients' Perceptions of Accessing and Experience with Using the Educational Material in the Opal Patient Portal.

PURPOSE: Opal is a new patient-centered mobile application that gives cancer patients access to their real time medical data in conjunction with disease- and treatment-specific patient education material. Few studies have focused on patients' experiences with such mobile applications. This study's objectives were to (1) explore cancer patients' perceptions of accessing the educational materials through Opal and (2) explore their experiences using these educational materials. METHODS: A qualitative descriptive design was used. Patients were invited to participate in the study via Opal itself. Semi-structured individual interviews were done in person or over the phone, transcribed verbatim and analyzed using qualitative content analysis. RESULTS: Nine women were interviewed. Three themes were identified as participants spoke about their perceptions of and experiences with Opal. First, Opal makes me feel like I have more control, conveying how learning more about their diagnosis and treatments allowed patients to advocate for themselves and plan their care. Second, Opal tends to reassure me, illustrating that having access to information was reassuring. Lastly, Opal is just starting to have information which could help meet my needs, reflecting patients' belief Opal is on the right track but could provide more of their medical record, treating team contact information and education material. CONCLUSION: Patients can feel more empowered when using patient-centered mobile applications, and mobile applications have potential for improving collaboration with healthcare professionals and care coordination. Healthcare professionals, including oncologists and nurses, should support patients' use of mobile applications and integrate them in their patient interactions.

Implementation of a DVH Registry to provide constraints and continuous quality monitoring for pediatric CSI treatment planning.

Craniospinal irradiation (CSI) is a complex radiation therapy technique that is used for patients, often children and teenagers/young adults, with tumors that have a propensity to spread throughout the central nervous system such as medulloblastoma. CSI is associated with important long-term side effects, the risk of which may be affected by numerous factors including radiation modality and technique. Lack of standardization for a technique that is used even in larger radiation oncology departments only a few times each year may be one such factor and the current ad hoc manner of planning new CSI patients may be greatly improved by implementing a dose-volume histogram registry (DVHR) to use previous patient data to facilitate prospective constraint guidance for organs at risk. In this work, we implemented a DVHR and used it to provide standardized constraints for CSI planning. Mann-Whitney U tests and mean differences at 95% confidence intervals were used to compare two cohorts (pre- and post-DVHR intervention) at specific dosimetric points to determine if observed improvements in standardization were statistically significant. Through this approach, we have shown that the implementation of dosimetric constraints based on DVHR-derived data helped improve the standardization of pediatric CSI planning at our center. The DVHR also provided guidance for a change in CSI technique, helping to achieve practice standardization across TomoTherapy and IMRT.

The impact of treatment parameter variation on secondary neutron spectra in high-energy electron beam radiotherapy.

High-energy electron treatment procedures in radiotherapy pose a potential iatrogenic cancer risk as well as a critical health risk to patients with cardiac implantable electronic devices due to the generation of secondary neutrons in the linac head, the treatment room, and the patient. It may be argued that the neutron production from photons is well characterized, but the same is not true for electrons. Therefore, to assess the risk involved in an electron treatment, one must determine the neutron flux spectrum generated by the treatment procedure. The neutron spectrum depends on the treatment parameters used and therefore it is crucial to study its dependence on these parameters. In this work, eight experiments were devised to analyze how eight electron treatment parameters impacted the neutron spectrum. The parameters we considered were the electron beam energy, location of measurement, cutout size, collimator size, applicator size, collimator angle, choice of treatment room, and the presence or absence of a solid water phantom. For each experiment, we used a Nested Neutron Spectrometer™ (NNS) to measure the neutron flux spectra for multiple settings of the treatment parameter of interest. The resulting spectra were plotted and compared. We found that the electron beam energy and the location of measurement had the most impact on the neutron flux spectra, while the other parameters had a smaller or insignificant impact. This report may serve as a reference tool for medical physicists to help estimate the risk associated with a particular high-energy electron treatment procedure.