Quality assurance in radiation oncology.

The Children's Oncology Group (COG) has a strong quality assurance (QA) program managed by the Imaging and Radiation Oncology Core (IROC). This program consists of credentialing centers and providing real-time management of each case for protocol compliant target definition and radiation delivery. In the International Society of Pediatric Oncology (SIOP), the lack of an available, reliable online data platform has been a challenge and the European Society for Paediatric Oncology (SIOPE) quality and excellence in radiotherapy and imaging for children and adolescents with cancer across Europe in clinical trials (QUARTET) program currently provides QA review for prospective clinical trials. The COG and SIOP are fully committed to a QA program that ensures uniform execution of protocol treatments and provides validity of the clinical data used for analysis.

A Candidate Imaging Marker for Early Detection of Charcot Neuroarthropathy.

Inflammation-mediated foot osteopenia may play a pivotal role in the etiogenesis, pathogenesis, and therapeutic outcomes in individuals with diabetes mellitus (DM), peripheral neuropathy (PN), and Charcot neuroarthropathy (CN). Our objective was to establish a volumetric quantitative computed tomography-derived foot bone measurement as a candidate prognostic imaging marker to identify individuals with DMPN who were at risk of developing CN. We studied 3 groups: 16 young controls (27 ± 5 years), 20 with DMPN (57 ± 11 years), and 20 with DMPN and CN (55 ± 9 years). Computed tomography image analysis was used to measure metatarsal and tarsal bone mineral density in both feet. The mean of 12 right (7 tarsals and 5 metatarsals) and 12 left foot bone mineral densities, maximum percent difference in bone mineral density between paired bones of the right and the left feet, and the mean difference of the 12 right and the 12 left bone mineral density measurements were used as input variables in different classification analysis methods to determine the best classifier. Classification tree analysis produced no misclassification of the young controls and individuals with DMPN and CN. The tree classifier found 7 of 20 (35%) individuals with DMPN to be classified as CN (1 participant developed CN during follow-up) and 13 (65%) to be classified as healthy. These results indicate that a decision tree employing 3 measurements derived from volumetric quantitative computed tomography foot bone mineral density defines a candidate prognostic imaging marker to identify individuals with diabetes and PN who are at risk of developing CN.

Gibbs distribution for statistical analysis of graphical data with a sample application to fcMRI brain images.

This paper develops object-oriented data analysis (OODA) statistical methods that are novel and complementary to existing methods of analysis of human brain scan connectomes, defined as graphs representing brain anatomical or functional connectivity. OODA is an emerging field where classical statistical approaches (e.g., hypothesis testing, regression, estimation, and confidence intervals) are applied to data objects such as graphs or functions. By analyzing data objects directly we avoid loss of information that occurs when data objects are transformed into numerical summary statistics. By providing statistical tools that analyze sets of connectomes without loss of information, new insights into neurology and medicine may be achieved. In this paper we derive the formula for statistical model fitting, regression, and mixture models; test their performance in simulation experiments; and apply them to connectomes from fMRI brain scans collected during a serial reaction time task study. Software for fitting graphical object-oriented data analysis is provided.

Quantitative Multiparametric MRI Features and PTEN Expression of Peripheral Zone Prostate Cancer: A Pilot Study.

OBJECTIVE: The objective of our study was to investigate associations between quantitative image features of multiparametric MRI of the prostate and PTEN expression of peripheral zone prostate cancer. MATERIALS AND METHODS: A total of 45 peripheral zone cancer foci from 30 patients who had undergone multiparametric prostate MRI before prostatectomy were identified by a genitourinary pathologist and a radiologist who reviewed histologic findings and MR images. Histologic sections of cancer foci underwent immunohistochemical analysis and were scored according to the percentage of tumor-positive cells expressing PTEN as negative (0-20%), mixed (20-80%), or positive (80-100%). Average and 10th percentile apparent diffusion coefficient (ADC) values, skewness of T2-weighted signal intensity histogram, and quantitative perfusion parameters (i.e., forward volume transfer constant [K(trans)], extravascular extracellular volume fraction [ve], and reverse reflux rate constant between the extracellular space and plasma [k(ep)]) from the Tofts model were calculated for each cancer focus. Associations between the quantitative image features and PTEN expression were analyzed with the Spearman rank correlation coefficient (r). RESULTS: Analysis of the 45 cancer foci revealed that 21 (47%) were PTEN-positive, 12 (27%) were PTEN-negative, and 12 (27%) were mixed. There was a weak but significant negative correlation between Gleason score and PTEN expression (r = -0.30, p = 0.04) and between k(ep) and PTEN expression (r = -0.35, p = 0.02). There was no significant correlation between other multiparametric MRI features and PTEN expression. CONCLUSION: This preliminary study of radiogenomics of peripheral zone prostate cancer revealed weak-but significant-associations between the quantitative dynamic contrast-enhanced MRI feature k(ep) and Gleason score with PTEN expression. These findings warrant further investigation and validation with the aim of using multiparametric MRI to improve risk assessment of patients with prostate cancer.

De-identification of Medical Images with Retention of Scientific Research Value.

Online public repositories for sharing research data allow investigators to validate existing research or perform secondary research without the expense of collecting new data. Patient data made publicly available through such repositories may constitute a breach of personally identifiable information if not properly de-identified. Imaging data are especially at risk because some intricacies of the Digital Imaging and Communications in Medicine (DICOM) format are not widely understood by researchers. If imaging data still containing protected health information (PHI) were released through a public repository, a number of different parties could be held liable, including the original researcher who collected and submitted the data, the original researcher's institution, and the organization managing the repository. To minimize these risks through proper de-identification of image data, one must understand what PHI exists and where that PHI resides, and one must have the tools to remove PHI without compromising the scientific integrity of the data. DICOM public elements are defined by the DICOM Standard. Modality vendors use private elements to encode acquisition parameters that are not yet defined by the DICOM Standard, or the vendor may not have updated an existing software product after DICOM defined new public elements. Because private elements are not standardized, a common de-identification practice is to delete all private elements, removing scientifically useful data as well as PHI. Researchers and publishers of imaging data can use the tools and process described in this article to de-identify DICOM images according to current best practices.

A Query Tool for Investigator Access to the Data and Images of the National Lung Screening Trial.

The National Cancer Institute (NCI), in conjunction with blinded university, provides a mechanism to enable public access to the study data, CT radiology images, and pathology images from the National Lung Screening Trial (NLST). Access to the data and images is through the NCI-sponsored, blinded university-hosted The Cancer Imaging Archive (TCIA), a repository of more than 40 study collections of cancer images. Once access to the NLST data has been granted by NCI, a Query Tool within TCIA is used to access the NLST data and images. The Query Tool is a simple-to-use menu-driven database application designed to quickly pose queries and retrieve/save results (from 53,452 NLST participants), download CT images (~20 million available), and view pathology images (~1200 available). NLST study data are contained in 17 Query Tool tables with ~370 variables to query. This paper describes Query Tool design, functionality, and usefulness for researchers, clinicians, and software developers to query data, save query results, and download/view images.

Summary of the National Cancer Institute 2023 Virtual Workshop on Medical Image De-identification-Part 1: Report of the MIDI Task Group - Best Practices and Recommendations, Tools for Conventional Approaches to De-identification, International Approaches to De-identification, and Industry Panel on Image De-identification.

De-identification of medical images intended for research is a core requirement for data-sharing initiatives, particularly as the demand for data for artificial intelligence (AI) applications grows. The Center for Biomedical Informatics and Information Technology (CBIIT) of the US National Cancer Institute (NCI) convened a virtual workshop with the intent of summarizing the state of the art in de-identification technology and processes and exploring interesting aspects of the subject. This paper summarizes the highlights of the first day of the workshop, the recordings, and presentations of which are publicly available for review. The topics covered included the report of the Medical Image De-Identification Initiative (MIDI) Task Group on best practices and recommendations, tools for conventional approaches to de-identification, international approaches to de-identification, and an industry panel.

Feasibility of regional center telehealth visits utilizing a rural research network in people with Parkinson's disease.

Background: Impaired motor and cognitive function can make travel cumbersome for People with Parkinson's disease (PwPD). Over 50% of PwPD cared for at the University of Arkansas for Medical Sciences (UAMS) Movement Disorders Clinic reside over 30 miles from Little Rock. Improving access to clinical care for PwPD is needed. Objective: To explore the feasibility of remote clinic-to-clinic telehealth research visits for evaluation of multi-modal function in PwPD. Methods: PwPD residing within 30 miles of a UAMS Regional health center were enrolled and clinic-to-clinic telehealth visits were performed. Motor and non-motor disease assessments were administered and quantified. Results were compared to participants who performed at-home telehealth visits using the same protocols during the height of the COVID pandemic. Results: Compared to the at-home telehealth visit group (n = 50), the participants from regional centers (n = 13) had similar age and disease duration, but greater disease severity with higher total Unified Parkinson's disease rating scale scores (Z = -2.218, p = 0.027) and lower Montreal Cognitive Assessment scores (Z = -3.350, p < 0.001). Regional center participants had lower incomes (Pearson's chi = 21.3, p < 0.001), higher costs to attend visits (Pearson's chi = 16.1, p = 0.003), and lived in more socioeconomically disadvantaged neighborhoods (Z = -3.120, p = 0.002). Prior research participation was lower in the regional center group (Pearson's chi = 4.5, p = 0.034) but both groups indicated interest in future research participation. Conclusions: Regional center research visits in PwPD in medically underserved areas are feasible and could help improve access to care and research participation in these traditionally underrepresented populations.

Privacy-preserving record linkage across disparate institutions and datasets to enable a learning health system: The national COVID cohort collaborative (N3C) experience.

Introduction: Research driven by real-world clinical data is increasingly vital to enabling learning health systems, but integrating such data from across disparate health systems is challenging. As part of the NCATS National COVID Cohort Collaborative (N3C), the N3C Data Enclave was established as a centralized repository of deidentified and harmonized COVID-19 patient data from institutions across the US. However, making this data most useful for research requires linking it with information such as mortality data, images, and viral variants. The objective of this project was to establish privacy-preserving record linkage (PPRL) methods to ensure that patient-level EHR data remains secure and private when governance-approved linkages with other datasets occur. Methods: Separate agreements and approval processes govern N3C data contribution and data access. The Linkage Honest Broker (LHB), an independent neutral party (the Regenstrief Institute), ensures data linkages are robust and secure by adding an extra layer of separation between protected health information and clinical data. The LHB's PPRL methods (including algorithms, processes, and governance) match patient records using "deidentified tokens," which are hashed combinations of identifier fields that define a match across data repositories without using patients' clear-text identifiers. Results: These methods enable three linkage functions: Deduplication, Linking Multiple Datasets, and Cohort Discovery. To date, two external repositories have been cross-linked. As of March 1, 2023, 43 sites have signed the LHB Agreement; 35 sites have sent tokens generated for 9 528 998 patients. In this initial cohort, the LHB identified 135 037 matches and 68 596 duplicates. Conclusion: This large-scale linkage study using deidentified datasets of varying characteristics established secure methods for protecting the privacy of N3C patient data when linked for research purposes. This technology has potential for use with registries for other diseases and conditions.

Vitamin D Oral Replacement in Children With Obesity Related Asthma: VDORA1 Randomized Clinical Trial.

Children with asthma and obesity are more likely to have lower vitamin D levels, but the optimal replacement dose is unknown in this population. The objective of this study is identifying a vitamin D dose in children with obesity-related asthma that safely achieves serum vitamin D levels of ≥ 40 ng/mL. This prospective multisite randomized controlled trial recruited children/adolescents with asthma and body mass index ≥ 85% for age/sex. Part 1 (dose finding), evaluated 4 oral vitamin D regimens for 16 weeks to identify a replacement dose that achieved serum vitamin D levels ≥ 40 ng/mL. Part 2 compared the replacement dose calculated from part 1 (50,000 IU loading dose with 8,000 IU daily) to standard of care (SOC) for 16 weeks to identify the proportion of children achieving target serum 25(OH)D level. Part 1 included 48 randomized participants. Part 2 included 64 participants. In Part 1, no SOC participants achieved target serum level, but 50-72.7% of participants in cohorts A-C achieved the target serum level. In part 2, 78.6% of replacement dose participants achieved target serum level compared with none in the SOC arm. No related serious adverse events were reported. This trial confirmed a 50,000 IU loading dose plus 8,000 IU daily oral vitamin D as safe and effective in increasing serum 25(OH)D levels in children/adolescents with overweight/obesity to levels ≥ 40 ng/mL. Given the critical role of vitamin D in many conditions complicating childhood obesity, these data close a critical gap in our understanding of vitamin D dosing in children.

Documenting the de-identification process of clinical and imaging data for AI for health imaging projects.

Artificial intelligence (AI) is revolutionizing the field of medical imaging, holding the potential to shift medicine from a reactive "sick-care" approach to a proactive focus on healthcare and prevention. The successful development of AI in this domain relies on access to large, comprehensive, and standardized real-world datasets that accurately represent diverse populations and diseases. However, images and data are sensitive, and as such, before using them in any way the data needs to be modified to protect the privacy of the patients. This paper explores the approaches in the domain of five EU projects working on the creation of ethically compliant and GDPR-regulated European medical imaging platforms, focused on cancer-related data. It presents the individual approaches to the de-identification of imaging data, and describes the problems and the solutions adopted in each case. Further, lessons learned are provided, enabling future projects to optimally handle the problem of data de-identification. CRITICAL RELEVANCE STATEMENT: This paper presents key approaches from five flagship EU projects for the de-identification of imaging and clinical data offering valuable insights and guidelines in the domain. KEY POINTS: ΑΙ models for health imaging require access to large amounts of data. Access to large imaging datasets requires an appropriate de-identification process. This paper provides de-identification guidelines from the AI for health imaging (AI4HI) projects.

Increased Incidence of Vestibular Disorders in Patients With SARS-CoV-2.

Objective: Determine the incidence of vestibular disorders in patients with SARS-CoV-2 compared to the control population. Study Design: Retrospective. Setting: Clinical data in the National COVID Cohort Collaborative database (N3C). Methods: Deidentified patient data from the National COVID Cohort Collaborative database (N3C) were queried based on variant peak prevalence (untyped, alpha, delta, omicron 21K, and omicron 23A) from covariants.org to retrospectively analyze the incidence of vestibular disorders in patients with SARS-CoV-2 compared to control population, consisting of patients without documented evidence of COVID infection during the same period. Results: Patients testing positive for COVID-19 were significantly more likely to have a vestibular disorder compared to the control population. Compared to control patients, the odds ratio of vestibular disorders was significantly elevated in patients with untyped (odds ratio [OR], 2.39; confidence intervals [CI], 2.29-2.50; P < 0.001), alpha (OR, 3.63; CI, 3.48-3.78; P < 0.001), delta (OR, 3.03; CI, 2.94-3.12; P < 0.001), omicron 21K variant (OR, 2.97; CI, 2.90-3.04; P < 0.001), and omicron 23A variant (OR, 8.80; CI, 8.35-9.27; P < 0.001). Conclusions: The incidence of vestibular disorders differed between COVID-19 variants and was significantly elevated in COVID-19-positive patients compared to the control population. These findings have implications for patient counseling and further research is needed to discern the long-term effects of these findings.

The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository.

The National Institutes of Health have placed significant emphasis on sharing of research data to support secondary research. Investigators have been encouraged to publish their clinical and imaging data as part of fulfilling their grant obligations. Realizing it was not sufficient to merely ask investigators to publish their collection of imaging and clinical data, the National Cancer Institute (NCI) created the open source National Biomedical Image Archive software package as a mechanism for centralized hosting of cancer related imaging. NCI has contracted with Washington University in Saint Louis to create The Cancer Imaging Archive (TCIA)-an open-source, open-access information resource to support research, development, and educational initiatives utilizing advanced medical imaging of cancer. In its first year of operation, TCIA accumulated 23 collections (3.3 million images). Operating and maintaining a high-availability image archive is a complex challenge involving varied archive-specific resources and driven by the needs of both image submitters and image consumers. Quality archives of any type (traditional library, PubMed, refereed journals) require management and customer service. This paper describes the management tasks and user support model for TCIA.

Automated, foot-bone registration using subdivision-embedded atlases for spatial mapping of bone mineral density.

We present an atlas-based registration method for bones segmented from quantitative computed tomography (QCT) scans, with the goal of mapping their interior bone mineral densities (BMDs) volumetrically. We introduce a new type of deformable atlas, called subdivision-embedded atlas, which consists of a control grid represented as a tetrahedral subdivision mesh and a template bone surface embedded within the grid. Compared to a typical lattice-based deformation grid, the subdivision control grid possesses a relatively small degree of freedom tailored to the shape of the bone, which allows efficient fitting onto subjects. Compared with previous subdivision atlases, the novelty of our atlas lies in the addition of the embedded template surface, which further increases the accuracy of the fitting. Using this new atlas representation, we developed an efficient and fully automated pipeline for registering atlases of 12 tarsal and metatarsal bones to a segmented QCT scan of a human foot. Our evaluation shows that the mapping of BMD enabled by the registration is consistent for bones in repeated scans, and the regional BMD automatically computed from the mapping is not significantly different from expert annotations. The results suggest that our improved subdivision-based registration method is a reliable, efficient way to replace manual labor for measuring regional BMD in foot bones in QCT scans.

medigan: a Python library of pretrained generative models for medical image synthesis.

Purpose: Deep learning has shown great promise as the backbone of clinical decision support systems. Synthetic data generated by generative models can enhance the performance and capabilities of data-hungry deep learning models. However, there is (1) limited availability of (synthetic) datasets and (2) generative models are complex to train, which hinders their adoption in research and clinical applications. To reduce this entry barrier, we explore generative model sharing to allow more researchers to access, generate, and benefit from synthetic data. Approach: We propose medigan, a one-stop shop for pretrained generative models implemented as an open-source framework-agnostic Python library. After gathering end-user requirements, design decisions based on usability, technical feasibility, and scalability are formulated. Subsequently, we implement medigan based on modular components for generative model (i) execution, (ii) visualization, (iii) search & ranking, and (iv) contribution. We integrate pretrained models with applications across modalities such as mammography, endoscopy, x-ray, and MRI. Results: The scalability and design of the library are demonstrated by its growing number of integrated and readily-usable pretrained generative models, which include 21 models utilizing nine different generative adversarial network architectures trained on 11 different datasets. We further analyze three medigan applications, which include (a) enabling community-wide sharing of restricted data, (b) investigating generative model evaluation metrics, and (c) improving clinical downstream tasks. In (b), we extract Fréchet inception distances (FID) demonstrating FID variability based on image normalization and radiology-specific feature extractors. Conclusion: medigan allows researchers and developers to create, increase, and domain-adapt their training data in just a few lines of code. Capable of enriching and accelerating the development of clinical machine learning models, we show medigan's viability as platform for generative model sharing. Our multimodel synthetic data experiments uncover standards for assessing and reporting metrics, such as FID, in image synthesis studies.

Informatics infrastructure in a rural pediatric clinical trials network: Matching specific clinical research needs with best practices and industry guidelines.

Children have historically been underrepresented in randomized controlled trials and multi-center studies. This is particularly true for children who reside in rural and underserved areas. Conducting multi-center trials in rural areas presents unique informatics challenges. These challenges call for increased attention towards informatics infrastructure and the need for development and application of sound informatics approaches to the collection, processing, and management of data for clinical studies. By modifying existing local infrastructure and utilizing open source tools, we have been able to successfully deploy a multi-site data coordinating and operations center. We report our implementation decisions for data collection and management for the IDeA States Pediatric Clinical Trial Network (ISPCTN) based on the functionality needed for the ISPCTN, our synthesis of the extant literature in data collection and management methodology, and Good Clinical Data Management Practices.

A machine learning method to process voice samples for identification of Parkinson's disease.

Machine learning approaches have been used for the automatic detection of Parkinson's disease with voice recordings being the most used data type due to the simple and non-invasive nature of acquiring such data. Although voice recordings captured via telephone or mobile devices allow much easier and wider access for data collection, current conflicting performance results limit their clinical applicability. This study has two novel contributions. First, we show the reliability of personal telephone-collected voice recordings of the sustained vowel /a/ in natural settings by collecting samples from 50 people with specialist-diagnosed Parkinson's disease and 50 healthy controls and applying machine learning classification with voice features related to phonation. Second, we utilize a novel application of a pre-trained convolutional neural network (Inception V3) with transfer learning to analyze the spectrograms of the sustained vowel from these samples. This approach considers speech intensity estimates across time and frequency scales rather than collapsing measurements across time. We show the superiority of our deep learning model for the task of classifying people with Parkinson's disease as distinct from healthy controls.

Gait Declines Differentially in, and Improves Prediction of, People with Parkinson's Disease Converting to a Freezing of Gait Phenotype.

BACKGROUND: Freezing of gait (FOG) is a debilitating, variably expressed motor symptom in people with Parkinson's disease (PwPD) with limited treatments. OBJECTIVE: To determine if the rate of progression in spatiotemporal gait parameters in people converting from a noFOG to a FOG phenotype (FOGConv) was faster than non-convertors, and determine if gait parameters can help predict this conversion. METHODS: PwPD were objectively monitored longitudinally, approximately every 6 months. Non-motor assessments were performed at the initial visit. Steady-state gait in the levodopa ON-state was collected using a gait mat (Protokinetics) at each visit. The rate of progression in 8 spatiotemporal gait parameters was calculated. FOG convertors (FOGConv) were classified if they did not have FOG at initial visit and developed FOG at a subsequent visit. RESULTS: Thirty freezers (FOG) and 30 non-freezers were monitored an average of 3.5 years, with 10 non-freezers developing FOG (FOGConv). FOGConv and FOG had faster decline in mean stride-length, swing-phase-percent, and increase in mean total-double-support percent, coefficient of variability (CV) foot-strike-length and CV swing-phase-percent than the remaining non-freezers (noFOG). On univariate modeling, progression rates of mean stride-length, stride-velocity, swing-phase-percent, total-double-support-percent and of CV swing-phase-percent had high discriminative power (AUC > 0.83) for classification of the FOGConv and noFOG groups. CONCLUSION: FOGConv had a faster temporal decline in objectively quantified gait than noFOG, and progression rates of spatiotemporal gait parameters were more predictive of FOG phenotype conversion than initial (static) parameters Objectively monitoring gait in disease prediction models may help define FOG prone groups for testing putative treatments.

Assessment of the Frequency, Phenotypes, and Outcomes of Acute Liver Injury Associated with Amoxicillin/Clavulanate in 1.4 Million Patients in the Veterans Health Administration.

INTRODUCTION: Drug-induced liver injury is a significant health issue, yet the exposure-based incidence remains to be characterized. OBJECTIVE: We aimed to assess the frequency, phenotypes, and outcomes of acute liver injury associated with amoxicillin/clavulanate using a large electronic health record system. METHODS: Using the Veterans Health Administration electronic health record system, we developed the framework to identify unexplained acute liver injury, defined by alanine aminotransferase and/or alkaline phosphatase elevation temporally linked to prescription records of amoxicillin/clavulanate, a major culprit of clinically significant drug-induced liver injury, excluding other competing causes. The population was subcategorized by pre-existing liver conditions and inpatient status at the time of exposure for the analysis. RESULTS: Among 1,445,171 amoxicillin/clavulanate first exposures in unique individuals [92% men; mean age (standard deviation): 59 (15) years], 6476 (incidence: 0.448%) acute liver injuries were identified. Of these, 4427 (65%) had alternative causes, yielding 2249 (incidence: 0.156%) with unexplained acute liver injuries. The incidence of unexplained acute liver injury was lowest in outpatients without underlying liver disease (0.067%) and highest in inpatients with pre-existing liver conditions (0.719%). Older age, male sex, and American Indian or Alaska Native (vs White) were associated with a higher incidence of unexplained acute liver injury. Cholestatic injury affected 74%, exhibiting a higher frequency with advanced age, inpatient exposure, and pre-existing liver conditions. Hepatocellular injury with bilirubin elevation affected 0.003%, with a higher risk at age >45 years. During a 12-month follow-up, patients with unexplained acute liver injury had a higher adjusted overall mortality risk than those without evident acute liver injury. CONCLUSIONS: This framework identifies unexplained acute liver injury following drug exposure in large electronic health record datasets. After validating in other systems, this framework can aid in deducing drug-induced liver injury in the general patient population and regulatory decision making to promote drug safety and public health.

The Importance of Quality Assurance in Radiation Oncology Clinical Trials.

Clinical trials have been the center of progress in modern medicine. In oncology, we are fortunate to have a structure in place through the National Clinical Trials Network (NCTN). The NCTN provides the infrastructure and a forum for scientific discussion to develop clinical concepts for trial design. The NCTN also provides a network group structure to administer trials for successful trial management and outcome analyses. There are many important aspects to trial design and conduct. Modern trials need to ensure appropriate trial conduct and secure data management processes. Of equal importance is the quality assurance of a clinical trial. If progress is to be made in oncology clinical medicine, investigators and patient care providers of service need to feel secure that trial data is complete, accurate, and well-controlled in order to be confident in trial analysis and move trial outcome results into daily practice. As our technology has matured, so has our need to apply technology in a uniform manner for appropriate interpretation of trial outcomes. In this article, we review the importance of quality assurance in clinical trials involving radiation therapy. We will include important aspects of institution and investigator credentialing for participation as well as ongoing processes to ensure that each trial is being managed in a compliant manner. We will provide examples of the importance of complete datasets to ensure study interpretation. We will describe how successful strategies for quality assurance in the past will support new initiatives moving forward.