Pragmatic randomised clinical trial of proton versus photon therapy for patients with non-metastatic breast cancer: the Radiotherapy Comparative Effectiveness (RadComp) Consortium trial protocol.

INTRODUCTION: A broad range of stakeholders have called for randomised evidence on the potential clinical benefits and harms of proton therapy, a type of radiation therapy, for patients with breast cancer. Radiation therapy is an important component of curative treatment, reducing cancer recurrence and extending survival. Compared with photon therapy, the international treatment standard, proton therapy reduces incidental radiation to the heart. Our overall objective is to evaluate whether the differences between proton and photon therapy cardiac radiation dose distributions lead to meaningful reductions in cardiac morbidity and mortality after treatment for breast cancer. METHODS: We are conducting a large scale, multicentre pragmatic randomised clinical trial for patients with breast cancer who will be followed longitudinally for cardiovascular morbidity and mortality, health-related quality of life and cancer control outcomes. A total of 1278 patients with non-metastatic breast cancer will be randomly allocated to receive either photon or proton therapy. The primary outcomes are major cardiovascular events, defined as myocardial infarction, coronary revascularisation, cardiovascular death or hospitalisation for unstable angina, heart failure, valvular disease, arrhythmia or pericardial disease. Secondary endpoints are urgent or unanticipated outpatient or emergency room visits for heart failure, arrhythmia, valvular disease or pericardial disease. The Radiotherapy Comparative Effectiveness (RadComp) Clinical Events Centre will conduct centralised, blinded adjudication of primary outcome events. ETHICS AND DISSEMINATION: The RadComp trial has been approved by the institutional review boards of all participating sites. Recruitment began in February 2016. Current version of the protocol is A3, dated 08 November 2018. Dissemination plans include presentations at scientific conferences, scientific publications, stakeholder engagement efforts and presentation to the public via lay media outlets. TRIAL REGISTRATION NUMBER: NCT02603341.

Enhancing Clinical Data and Clinical Research Data with Biomedical Ontologies - Insights from the Knowledge Representation Perspective.

OBJECTIVES: There exists a communication gap between the biomedical informatics community on one side and the computer science/artificial intelligence community on the other side regarding the meaning of the terms "semantic integration" and "knowledge representation". This gap leads to approaches that attempt to provide one-to-one mappings between data elements and biomedical ontologies. Our aim is to clarify the representational differences between traditional data management and semantic-web-based data management by providing use cases of clinical data and clinical research data re-representation. We discuss how and why one-to-one mappings limit the advantages of using Semantic Web Technologies (SWTs). METHODS: We employ commonly used SWTs, such as Resource Description Framework (RDF) and Ontology Web Language (OWL). We reuse pre-existing ontologies and ensure shared ontological commitment by selecting ontologies from a framework that fosters community-driven collaborative ontology development for biomedicine following the same set of principles. RESULTS: We demonstrate the results of providing SWT-compliant re-representation of data elements from two independent projects managing clinical data and clinical research data. Our results show how one-to-one mappings would hinder the exploitation of the advantages provided by using SWT. CONCLUSIONS: We conclude that SWT-compliant re-representation is an indispensable step, if using the full potential of SWT is the goal. Rather than providing one-to-one mappings, developers should provide documentation that links data elements to graph structures to specify the re-representation.

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.

Predicting a multi-parametric probability map of active tumor extent using random forests.

Glioblastoma Mulitforme is highly infiltrative, making precise delineation of tumor margin difficult. Multimodality or multi-parametric MR imaging sequences promise an advantage over anatomic sequences such as post contrast enhancement as methods for determining the spatial extent of tumor involvement. In considering multi-parametric imaging sequences however, manual image segmentation and classification is time-consuming and prone to error. As a preliminary step toward integration of multi-parametric imaging into clinical assessments of primary brain tumors, we propose a machine-learning based multi-parametric approach that uses radiologist generated labels to train a classifier that is able to classify tissue on a voxel-wise basis and automatically generate a tumor segmentation. A random forests classifier was trained using a leave-one-out experimental paradigm. A simple linear classifier was also trained for comparison. The random forests classifier accurately predicted radiologist generated segmentations and tumor extent.

TCIA: An information resource to enable open science.

Reusable, publicly available data is a pillar of open science. The Cancer Imaging Archive (TCIA) is an open image archive service supporting cancer research. TCIA collects, de-identifies, curates and manages rich collections of oncology image data. Image data sets have been contributed by 28 institutions and additional image collections are underway. Since June of 2011, more than 2,000 users have registered to search and access data from this freely available resource. TCIA encourages and supports cancer-related open science communities by hosting and managing the image archive, providing project wiki space and searchable metadata repositories. The success of TCIA is measured by the number of active research projects it enables (>40) and the number of scientific publications and presentations that are produced using data from TCIA collections (39).

Progression of foot deformity in Charcot neuropathic osteoarthropathy.

BACKGROUND: Charcot neuropathic osteoarthropathy associated foot deformity can result in joint instability, ulceration, and even amputation. The purpose of the present study was to follow patients with and without active Charcot osteoarthropathy for as long as two years to examine the magnitude and timing of foot alignment changes. METHODS: We studied fifteen subjects with Charcot osteoarthropathy and nineteen subjects with diabetes mellitus and peripheral neuropathy without Charcot osteoarthropathy for one year; eight of the subjects with osteoarthropathy and five of the subjects with diabetes and peripheral neuropathy were followed for two years. Bilateral weight-bearing radiographs of the foot were made at baseline for all subjects, with repeat radiographs being made at six months for the osteoarthropathy group and at one and two years for both groups. Radiographic measurements included the Meary angle, cuboid height, calcaneal pitch, and hindfoot-forefoot angle. RESULTS: The Meary angle, cuboid height, and calcaneal pitch worsened in feet with Charcot osteoarthropathy over one year as compared with the contralateral, uninvolved feet and feet in patients with diabetes and peripheral neuropathy. Cuboid height continued to worsen over the two-year follow-up in the feet with Charcot osteoarthropathy. These feet also had a greater change in the hindfoot-forefoot angle at one year as compared with the feet in patients with diabetes and peripheral neuropathy and at two years as compared with the contralateral, uninvolved feet. CONCLUSIONS: In patients with Charcot neuropathic osteoarthropathy, radiographic alignment measurements demonstrate the presence of foot deformity at the time of the initial clinical presentation and evidence of progressive changes over the first and second years. The six-month data suggest worsening of medial column alignment prior to lateral column worsening. This radiographic evidence of worsening foot alignment over time supports the need for aggressive intervention (conservative bracing or surgical fixation) to attempt to prevent limb-threatening complications.

Development of a Google-based search engine for data mining radiology reports.

The aim of this study is to develop a secure, Google-based data-mining tool for radiology reports using free and open source technologies and to explore its use within an academic radiology department. A Health Insurance Portability and Accountability Act (HIPAA)-compliant data repository, search engine and user interface were created to facilitate treatment, operations, and reviews preparatory to research. The Institutional Review Board waived review of the project, and informed consent was not required. Comprising 7.9 GB of disk space, 2.9 million text reports were downloaded from our radiology information system to a fileserver. Extensible markup language (XML) representations of the reports were indexed using Google Desktop Enterprise search engine software. A hypertext markup language (HTML) form allowed users to submit queries to Google Desktop, and Google's XML response was interpreted by a practical extraction and report language (PERL) script, presenting ranked results in a web browser window. The query, reason for search, results, and documents visited were logged to maintain HIPAA compliance. Indexing averaged approximately 25,000 reports per hour. Keyword search of a common term like "pneumothorax" yielded the first ten most relevant results of 705,550 total results in 1.36 s. Keyword search of a rare term like "hemangioendothelioma" yielded the first ten most relevant results of 167 total results in 0.23 s; retrieval of all 167 results took 0.26 s. Data mining tools for radiology reports will improve the productivity of academic radiologists in clinical, educational, research, and administrative tasks. By leveraging existing knowledge of Google's interface, radiologists can quickly perform useful searches.

Silent Cerebral Infarct Transfusion (SIT) trial imaging core: application of novel imaging information technology for rapid and central review of MRI of the brain.

The Silent Cerebral Infarct Multicenter Transfusion (SIT) Trial is a multi-institutional intervention trial in which children with silent cerebral infarcts are randomized to receive either blood transfusion therapy or observation (standard care) for 36 months. The SIT Trial is scheduled to enroll approximately 1,880 children with sickle cell disease from 29 clinical sites in the United States, Canada, UK, and France. Each child undergoes a screening magnetic resonance imaging (MRI) of the brain to detect the presence of silent cerebral infarct-like lesions, a pre-randomization (baseline) MRI and exit MRI to determine if there are new or enlarged cerebral infarcts, using a designated, prospective imaging protocol. The objective of this manuscript is to describe the innovative method used to process and adjudicate imaging studies for an international trial with a primary endpoint that includes neuroimaging. Institution investigators at each site were provided with computer hardware and software for transmission of MRI images that allow them to strip the scans of all personal information and add unique study identifiers. Three neuroradiologists at separate academic centers review MRI studies and determine the presence or absence of silent cerebral infarct-like lesions. Their findings are subsequently placed on web-based case report forms and sent to the Statistical Coordinating Center. The average time from imaging center receipt of the MRI study to the radiology committee report back to the local site is less than two working days. This novel strategy was designed to maximize efficiency and minimize cost of a complex large multicenter trial that depends heavily on neuroimaging for entry criteria and assessment for the primary outcome measures. The technology, process, and expertise used in the SIT Trial can be adapted to virtually any clinical research trial with digital imaging requirements.

Collecting 48,000 CT exams for the lung screening study of the National Lung Screening Trial.

From 2002-2004, the Lung Screening Study (LSS) of the National Lung Screening Trial (NLST) enrolled 34,614 participants, aged 55-74 years, at increased risk for lung cancer due to heavy cigarette smoking. Participants, randomized to standard chest X-ray (CXR) or computed tomography (CT) arms at ten screening centers, received up to three imaging screens for lung cancer at annual intervals. Participant medical histories and radiologist-interpreted screening results were transmitted to the LSS coordinating center, while all images were retained at local screening centers. From 2005-2007, all CT exams were uniformly de-identified and delivered to a central repository, the CT Image Library (CTIL), on external hard drives (94%) or CD/DVD (5.9%), or over a secure Internet connection (0.1%). Of 48,723 CT screens performed, only 176 (0.3%) were unavailable (lost, corrupted, compressed) while 48,547 (99.7%) were delivered to the CTIL. Described here is the experience organizing, implementing, and adapting the clinical-trial workflow surrounding the image retrieval, de-identification, delivery, and archiving of available LSS-NLST CT exams for the CTIL, together with the quality assurance procedures associated with those collection tasks. This collection of CT exams, obtained in a specific, well-defined participant population under a common protocol at evenly spaced intervals, and its attending demographic and clinical information, are now available to lung-disease investigators and developers of computer-aided-diagnosis algorithms. The approach to large scale, multi-center trial CT image collection detailed here may serve as a useful model, while the experience reported should be valuable in the planning and execution of future equivalent endeavors.

Open source software projects of the caBIG In Vivo Imaging Workspace Software special interest group.

The Cancer Bioinformatics Grid (caBIG) program was created by the National Cancer Institute to facilitate sharing of IT infrastructure, data, and applications among the National Cancer Institute-sponsored cancer research centers. The program was launched in February 2004 and now links more than 50 cancer centers. In April 2005, the In Vivo Imaging Workspace was added to promote the use of imaging in cancer clinical trials. At the inaugural meeting, four special interest groups (SIGs) were established. The Software SIG was charged with identifying projects that focus on open-source software for image visualization and analysis. To date, two projects have been defined by the Software SIG. The eXtensible Imaging Platform project has produced a rapid application development environment that researchers may use to create targeted workflows customized for specific research projects. The Algorithm Validation Tools project will provide a set of tools and data structures that will be used to capture measurement information and associated needed to allow a gold standard to be defined for the given database against which change analysis algorithms can be tested. Through these and future efforts, the caBIG In Vivo Imaging Workspace Software SIG endeavors to advance imaging informatics and provide new open-source software tools to advance cancer research.