Publications by authors named "Fred Prior"

69 Publications

A DICOM dataset for evaluation of medical image de-identification.

Sci Data 2021 07 16;8(1):183. Epub 2021 Jul 16.

Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA.

We developed a DICOM dataset that can be used to evaluate the performance of de-identification algorithms. DICOM objects (a total of 1,693 CT, MRI, PET, and digital X-ray images) were selected from datasets published in the Cancer Imaging Archive (TCIA). Synthetic Protected Health Information (PHI) was generated and inserted into selected DICOM Attributes to mimic typical clinical imaging exams. The DICOM Standard and TCIA curation audit logs guided the insertion of synthetic PHI into standard and non-standard DICOM data elements. A TCIA curation team tested the utility of the evaluation dataset. With this publication, the evaluation dataset (containing synthetic PHI) and de-identified evaluation dataset (the result of TCIA curation) are released on TCIA in advance of a competition, sponsored by the National Cancer Institute (NCI), for algorithmic de-identification of medical image datasets. The competition will use a much larger evaluation dataset constructed in the same manner. This paper describes the creation of the evaluation datasets and guidelines for their use.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41597-021-00967-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8285420PMC
July 2021

DeIDNER Corpus: Annotation of Clinical Discharge Summary Notes for Named Entity Recognition Using BRAT Tool.

Stud Health Technol Inform 2021 May;281:432-436

Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA.

Named Entity Recognition (NER) aims to identify and classify entities into predefined categories is a critical pre-processing task in Natural Language Processing (NLP) pipeline. Readily available off-the-shelf NER algorithms or programs are trained on a general corpus and often need to be retrained when applied on a different domain. The end model's performance depends on the quality of named entities generated by these NER models used in the NLP task. To improve NER model accuracy, researchers build domain-specific corpora for both model training and evaluation. However, in the clinical domain, there is a dearth of training data because of privacy reasons, forcing many studies to use NER models that are trained in the non-clinical domain to generate NER feature-set. Thus, influencing the performance of the downstream NLP tasks like information extraction and de-identification. In this paper, our objective is to create a high quality annotated clinical corpus for training NER models that can be easily generalizable and can be used in a downstream de-identification task to generate named entities feature-set.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3233/SHTI210195DOI Listing
May 2021

Consolidated EHR Workflow for Endoscopy Quality Reporting.

Stud Health Technol Inform 2021 May;281:427-431

Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA.

Although colonoscopy is the most frequently performed endoscopic procedure, the lack of standardized reporting is impeding clinical and translational research. Inadequacies in data extraction from the raw, unstructured text in electronic health records (EHR) pose an additional challenge to procedure quality metric reporting, as vital details related to the procedure are stored in disparate documents. Currently, there is no EHR workflow that links these documents to the specific colonoscopy procedure, making the process of data extraction error prone. We hypothesize that extracting comprehensive colonoscopy quality metrics from consolidated procedure documents using computational linguistic techniques, and integrating it with discrete EHR data can improve quality of screening and cancer detection rate. As a first step, we developed an algorithm that links colonoscopy, pathology and imaging documents by analyzing the chronology of various orders placed relative to the colonoscopy procedure. The algorithm was installed and validated at the University of Arkansas for Medical Sciences (UAMS). The proposed algorithm in conjunction with Natural Language Processing (NLP) techniques can overcome current limitations of manual data abstraction.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3233/SHTI210194DOI Listing
May 2021

Machine Learning Approach to Optimize Sedation Use in Endoscopic Procedures.

Stud Health Technol Inform 2021 May;281:183-187

Division of Gastroenterology and Hepatology, University of Arkansas for Medical Sciences, Little Rock, AR, USA.

Endoscopy procedures are often performed with either moderate or deep sedation. While deep sedation is costly, procedures with moderate sedation are not always well tolerated resulting in patient discomfort, and are often aborted. Due to lack of clear guidelines, the decision to utilize moderate sedation or anesthesia for a procedure is made by the providers, leading to high variability in clinical practice. The objective of this study was to build a Machine Learning (ML) model that predicts if a colonoscopy can be successfully completed with moderate sedation based on patients' demographics, comorbidities, and prescribed medications. XGBoost model was trained and tested on 10,025 colonoscopies (70% - 30%) performed at University of Arkansas for Medical Sciences (UAMS). XGBoost achieved average area under receiver operating characteristic curve (AUC) of 0.762, F1-score to predict procedures that need moderate sedation was 0.85, and precision and recall were 0.81 and 0.89 respectively. The proposed model can be employed as a decision support tool for physicians to bolster their confidence while choosing between moderate sedation and anesthesia for a colonoscopy procedure.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3233/SHTI210145DOI Listing
May 2021

Deep Learning Methods to Predict Mortality in COVID-19 Patients: A Rapid Scoping Review.

Stud Health Technol Inform 2021 May;281:799-803

Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA.

The ongoing COVID-19 pandemic has become the most impactful pandemic of the past century. The SARS-CoV-2 virus has spread rapidly across the globe affecting and straining global health systems. More than 2 million people have died from COVID-19 (as of 30 January 2021). To lessen the pandemic's impact, advanced methods such as Artificial Intelligence models are proposed to predict mortality, morbidity, disease severity, and other outcomes and sequelae. We performed a rapid scoping literature review to identify the deep learning techniques that have been applied to predict hospital mortality in COVID-19 patients. Our review findings provide insights on the important deep learning models, data types, and features that have been reported in the literature. These summary findings will help scientists build reliable and accurate models for better intervention strategies for predicting mortality in current and future pandemic situations.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3233/SHTI210285DOI Listing
May 2021

Application of Machine Learning in Intensive Care Unit (ICU) Settings Using MIMIC Dataset: Systematic Review.

Informatics (MDPI) 2021 Mar 3;8(1). Epub 2021 Mar 3.

Department of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), Little Rock, Arkansas 72205, USA.

Modern Intensive Care Units (ICUs) provide continuous monitoring of critically ill patients susceptible to many complications affecting morbidity and mortality. ICU settings require a high staff-to-patient ratio and generates a sheer volume of data. For clinicians, the real-time interpretation of data and decision-making is a challenging task. Machine Learning (ML) techniques in ICUs are making headway in the early detection of high-risk events due to increased processing power and freely available datasets such as the Medical Information Mart for Intensive Care (MIMIC). We conducted a systematic literature review to evaluate the effectiveness of applying ML in the ICU settings using the MIMIC dataset. A total of 322 articles were reviewed and a quantitative descriptive analysis was performed on 61 qualified articles that applied ML techniques in ICU settings using MIMIC data. We assembled the qualified articles to provide insights into the areas of application, clinical variables used, and treatment outcomes that can pave the way for further adoption of this promising technology and possible use in routine clinical decision-making. The lessons learned from our review can provide guidance to researchers on application of ML techniques to increase their rate of adoption in healthcare.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3390/informatics8010016DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8112729PMC
March 2021

Quality assurance in radiation oncology.

Pediatr Blood Cancer 2021 05;68 Suppl 2:e28609

Radiation Therapy, Prinses Maxima - Center for Pediatric Oncology, Utrecht, The Netherlands.

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/pbc.28609DOI Listing
May 2021

Data preparation for artificial intelligence in medical imaging: A comprehensive guide to open-access platforms and tools.

Phys Med 2021 Mar 5;83:25-37. Epub 2021 Mar 5.

Faculty of Mathematics and Computer Science, University of Barcelona, Barcelona, Spain.

The vast amount of data produced by today's medical imaging systems has led medical professionals to turn to novel technologies in order to efficiently handle their data and exploit the rich information present in them. In this context, artificial intelligence (AI) is emerging as one of the most prominent solutions, promising to revolutionise every day clinical practice and medical research. The pillar supporting the development of reliable and robust AI algorithms is the appropriate preparation of the medical images to be used by the AI-driven solutions. Here, we provide a comprehensive guide for the necessary steps to prepare medical images prior to developing or applying AI algorithms. The main steps involved in a typical medical image preparation pipeline include: (i) image acquisition at clinical sites, (ii) image de-identification to remove personal information and protect patient privacy, (iii) data curation to control for image and associated information quality, (iv) image storage, and (v) image annotation. There exists a plethora of open access tools to perform each of the aforementioned tasks and are hereby reviewed. Furthermore, we detail medical image repositories covering different organs and diseases. Such repositories are constantly increasing and enriched with the advent of big data. Lastly, we offer directions for future work in this rapidly evolving field.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.ejmp.2021.02.007DOI Listing
March 2021

API Driven On-Demand Participant ID Pseudonymization in Heterogeneous Multi-Study Research.

Healthc Inform Res 2021 Jan 31;27(1):39-47. Epub 2021 Jan 31.

Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA.

Objectives: To facilitate clinical and translational research, imaging and non-imaging clinical data from multiple disparate systems must be aggregated for analysis. Study participant records from various sources are linked together and to patient records when possible to address research questions while ensuring patient privacy. This paper presents a novel tool that pseudonymizes participant identifiers (PIDs) using a researcher-driven automated process that takes advantage of application-programming interface (API) and the Perl Open-Source Digital Imaging and Communications in Medicine Archive (POSDA) to further de-identify PIDs. The tool, on-demand cohort and API participant identifier pseudonymization (O-CAPP), employs a pseudonymization method based on the type of incoming research data.

Methods: For images, pseudonymization of PIDs is done using API calls that receive PIDs present in Digital Imaging and Communications in Medicine (DICOM) headers and returns the pseudonymized identifiers. For non-imaging clinical research data, PIDs provided by study principal investigators (PIs) are pseudonymized using a nightly automated process. The pseudonymized PIDs (P-PIDs) along with other protected health information is further de-identified using POSDA.

Results: A sample of 250 PIDs pseudonymized by O-CAPP were selected and successfully validated. Of those, 125 PIDs that were pseudonymized by the nightly automated process were validated by multiple clinical trial investigators (CTIs). For the other 125, CTIs validated radiologic image pseudonymization by API request based on the provided PID and P-PID mappings.

Conclusions: We developed a novel approach of an ondemand pseudonymization process that will aide researchers in obtaining a comprehensive and holistic view of study participant data without compromising patient privacy.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.4258/hir.2021.27.1.39DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7921568PMC
January 2021

Two SARS-CoV-2 Genome Sequences of Isolates from Rural U.S. Patients Harboring the D614G Mutation, Obtained Using Nanopore Sequencing.

Microbiol Resour Announc 2020 Dec 17;10(1). Epub 2020 Dec 17.

Department of Biomedical Informatics, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA

Two coding-complete sequences of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were obtained from samples from two patients in Arkansas, in the southeastern corner of the United States. The viral genome was obtained using the ARTIC Network protocol and Oxford Nanopore Technologies sequencing.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1128/MRA.01109-20DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8407695PMC
December 2020

Role of Machine Learning Techniques to Tackle the COVID-19 Crisis: Systematic Review.

JMIR Med Inform 2021 Jan 11;9(1):e23811. Epub 2021 Jan 11.

Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, United States.

Background: SARS-CoV-2, the novel coronavirus responsible for COVID-19, has caused havoc worldwide, with patients presenting a spectrum of complications that have pushed health care experts to explore new technological solutions and treatment plans. Artificial Intelligence (AI)-based technologies have played a substantial role in solving complex problems, and several organizations have been swift to adopt and customize these technologies in response to the challenges posed by the COVID-19 pandemic.

Objective: The objective of this study was to conduct a systematic review of the literature on the role of AI as a comprehensive and decisive technology to fight the COVID-19 crisis in the fields of epidemiology, diagnosis, and disease progression.

Methods: A systematic search of PubMed, Web of Science, and CINAHL databases was performed according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines to identify all potentially relevant studies published and made available online between December 1, 2019, and June 27, 2020. The search syntax was built using keywords specific to COVID-19 and AI.

Results: The search strategy resulted in 419 articles published and made available online during the aforementioned period. Of these, 130 publications were selected for further analyses. These publications were classified into 3 themes based on AI applications employed to combat the COVID-19 crisis: Computational Epidemiology, Early Detection and Diagnosis, and Disease Progression. Of the 130 studies, 71 (54.6%) focused on predicting the COVID-19 outbreak, the impact of containment policies, and potential drug discoveries, which were classified under the Computational Epidemiology theme. Next, 40 of 130 (30.8%) studies that applied AI techniques to detect COVID-19 by using patients' radiological images or laboratory test results were classified under the Early Detection and Diagnosis theme. Finally, 19 of the 130 studies (14.6%) that focused on predicting disease progression, outcomes (ie, recovery and mortality), length of hospital stay, and number of days spent in the intensive care unit for patients with COVID-19 were classified under the Disease Progression theme.

Conclusions: In this systematic review, we assembled studies in the current COVID-19 literature that utilized AI-based methods to provide insights into different COVID-19 themes. Our findings highlight important variables, data types, and available COVID-19 resources that can assist in facilitating clinical and translational research.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.2196/23811DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7806275PMC
January 2021

Chest imaging representing a COVID-19 positive rural U.S. population.

Sci Data 2020 11 24;7(1):414. Epub 2020 Nov 24.

Department of Radiology, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA.

As the COVID-19 pandemic unfolds, radiology imaging is playing an increasingly vital role in determining therapeutic options, patient management, and research directions. Publicly available data are essential to drive new research into disease etiology, early detection, and response to therapy. In response to the COVID-19 crisis, the National Cancer Institute (NCI) has extended the Cancer Imaging Archive (TCIA) to include COVID-19 related images. Rural populations are one population at risk for underrepresentation in such public repositories. We have published in TCIA a collection of radiographic and CT imaging studies for patients who tested positive for COVID-19 in the state of Arkansas. A set of clinical data describes each patient including demographics, comorbidities, selected lab data and key radiology findings. These data are cross-linked to SARS-COV-2 cDNA sequence data extracted from clinical isolates from the same population, uploaded to the GenBank repository. We believe this collection will help to address population imbalance in COVID-19 data by providing samples from this normally underrepresented population.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41597-020-00741-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7686304PMC
November 2020

Toolkit to Compute Time-Based Elixhauser Comorbidity Indices and Extension to Common Data Models.

Healthc Inform Res 2020 Jul 31;26(3):193-200. Epub 2020 Jul 31.

Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA.

Objective: The time-dependent study of comorbidities provides insight into disease progression and trajectory. We hypothesize that understanding longitudinal disease characteristics can lead to more timely intervention and improve clinical outcomes. As a first step, we developed an efficient and easy-to-install toolkit, the Time-based Elixhauser Comorbidity Index (TECI), which pre-calculates time-based Elixhauser comorbidities and can be extended to common data models (CDMs).

Methods: A Structured Query Language (SQL)-based toolkit, TECI, was built to pre-calculate time-specific Elixhauser comorbidity indices using data from a clinical data repository (CDR). Then it was extended to the Informatics for Integrating Biology and the Bedside (I2B2) and Observational Medical Outcomes Partnership (OMOP) CDMs.

Results: At the University of Arkansas for Medical Sciences (UAMS), the TECI toolkit was successfully installed to compute the indices from CDR data, and the scores were integrated into the I2B2 and OMOP CDMs. Comorbidity scores calculated by TECI were validated against: scores available in the 2015 quarter 1-3 Nationwide Readmissions Database (NRD) and scores calculated using the comorbidities using a previously validated algorithm on the 2015 quarter 4 NRD. Furthermore, TECI identified 18,846 UAMS patients that had changes in comorbidity scores over time (year 2013 to 2019). Comorbidities for a random sample of patients were independently reviewed, and in all cases, the results were found to be 100% accurate.

Conclusion: TECI facilitates the study of comorbidities within a time-dependent context, allowing better understanding of disease associations and trajectories, which has the potential to improve clinical outcomes.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.4258/hir.2020.26.3.193DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7438698PMC
July 2020

DICOM re-encoding of volumetrically annotated Lung Imaging Database Consortium (LIDC) nodules.

Med Phys 2020 Nov 6;47(11):5953-5965. Epub 2020 Sep 6.

University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA.

Purpose: The dataset contains annotations for lung nodules collected by the Lung Imaging Data Consortium and Image Database Resource Initiative (LIDC) stored as standard DICOM objects. The annotations accompany a collection of computed tomography (CT) scans for over 1000 subjects annotated by multiple expert readers, and correspond to "nodules ≥ 3 mm", defined as any lesion considered to be a nodule with greatest in-plane dimension in the range 3-30 mm regardless of presumed histology. The present dataset aims to simplify reuse of the data with the readily available tools, and is targeted towards researchers interested in the analysis of lung CT images.

Acquisition And Validation Methods: Open source tools were utilized to parse the project-specific XML representation of LIDC-IDRI annotations and save the result as standard DICOM objects. Validation procedures focused on establishing compliance of the resulting objects with the standard, consistency of the data between the DICOM and project-specific representation, and evaluating interoperability with the existing tools.

Data Format And Usage Notes: The dataset utilizes DICOM Segmentation objects for storing annotations of the lung nodules, and DICOM Structured Reporting objects for communicating qualitative evaluations (nine attributes) and quantitative measurements (three attributes) associated with the nodules. The total of 875 subjects contain 6859 nodule annotations. Clustering of the neighboring annotations resulted in 2651 distinct nodules. The data are available in TCIA at https://doi.org/10.7937/TCIA.2018.h7umfurq.

Potential Applications: The standardized dataset maintains the content of the original contribution of the LIDC-IDRI consortium, and should be helpful in developing automated tools for characterization of lung lesions and image phenotyping. In addition to those properties, the representation of the present dataset makes it more FAIR (Findable, Accessible, Interoperable, Reusable) for the research community, and enables its integration with other standardized data collections.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/mp.14445DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7721965PMC
November 2020

Document Oriented Graphical Analysis and Prediction.

Stud Health Technol Inform 2020 Jun;270:183-187

Department of Epidemiology, University of Arkansas for Medical Sciences, Little Rock, AR, USA.

In general, small-mid size research laboratories struggle with managing clinical and secondary datasets. In addition, faster dissemination, correlation and prediction of information from available datasets is always a bottleneck. To address these challenges, we have developed a novel approach, Document Oriented Graphical Analysis and Prediction (DO-GAP), a hybrid tool, merging strengths of Not only SQL (NoSQL) document oriented and graph databases. DO-GAP provides flexible and simple data integration mechanism using document database, data visualization and knowledge discovery with graph database. We demonstrate how the proposed tool (DO-GAP) can integrate data from heterogeneous sources such as Genomic lab findings, clinical data from Electronic Health Record (EHR) systems and provide simple querying mechanism. Application of DO-GAP can be extended to other diverse clinical studies such as supporting or identifying weakness of clinical diagnosis in comparison to molecular genetic analysis.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3233/SHTI200147DOI Listing
June 2020

PRISM: A Platform for Imaging in Precision Medicine.

JCO Clin Cancer Inform 2020 06;4:491-499

University of Arkansas for Medical Sciences, Little Rock, AR.

Purpose: Precision medicine requires an understanding of individual variability, which can only be acquired from large data collections such as those supported by the Cancer Imaging Archive (TCIA). We have undertaken a program to extend the types of data TCIA can support. This, in turn, will enable TCIA to play a key role in precision medicine research by collecting and disseminating high-quality, state-of-the-art, quantitative imaging data that meet the evolving needs of the cancer research community.

Methods: A modular technology platform is presented that would allow existing data resources, such as TCIA, to evolve into a comprehensive data resource that meets the needs of users engaged in translational research for imaging-based precision medicine. This Platform for Imaging in Precision Medicine (PRISM) helps streamline the deployment and improve TCIA's efficiency and sustainability. More importantly, its inherent modular architecture facilitates a piecemeal adoption by other data repositories.

Results: PRISM includes services for managing radiology and pathology images and features and associated clinical data. A semantic layer is being built to help users explore diverse collections and pool data sets to create specialized cohorts. PRISM includes tools for image curation and de-identification. It includes image visualization and feature exploration tools. The entire platform is distributed as a series of containerized microservices with representational state transfer interfaces.

Conclusion: PRISM is helping modernize, scale, and sustain the technology stack that powers TCIA. Repositories can take advantage of individual PRISM services such as de-identification and quality control. PRISM is helping scale image informatics for cancer research at a time when the size, complexity, and demands to integrate image data with other precision medicine data-intensive commons are mounting.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1200/CCI.20.00001DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7328100PMC
June 2020

Quantitative Imaging Informatics for Cancer Research.

JCO Clin Cancer Inform 2020 05;4:444-453

Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.

Purpose: We summarize Quantitative Imaging Informatics for Cancer Research (QIICR; U24 CA180918), one of the first projects funded by the National Cancer Institute (NCI) Informatics Technology for Cancer Research program.

Methods: QIICR was motivated by the 3 use cases from the NCI Quantitative Imaging Network. 3D Slicer was selected as the platform for implementation of open-source quantitative imaging (QI) tools. Digital Imaging and Communications in Medicine (DICOM) was chosen for standardization of QI analysis outputs. Support of improved integration with community repositories focused on The Cancer Imaging Archive (TCIA). Priorities included improved capabilities of the standard, toolkits and tools, reference datasets, collaborations, and training and outreach.

Results: Fourteen new tools to support head and neck cancer, glioblastoma, and prostate cancer QI research were introduced and downloaded over 100,000 times. DICOM was amended, with over 40 correction proposals addressing QI needs. Reference implementations of the standard in a popular toolkit and standalone tools were introduced. Eight datasets exemplifying the application of the standard and tools were contributed. An open demonstration/connectathon was organized, attracting the participation of academic groups and commercial vendors. Integration of tools with TCIA was improved by implementing programmatic communication interface and by refining best practices for QI analysis results curation.

Conclusion: Tools, capabilities of the DICOM standard, and datasets we introduced found adoption and utility within the cancer imaging community. A collaborative approach is critical to addressing challenges in imaging informatics at the national and international levels. Numerous challenges remain in establishing and maintaining the infrastructure of analysis tools and standardized datasets for the imaging community. Ideas and technology developed by the QIICR project are contributing to the NCI Imaging Data Commons currently being developed.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1200/CCI.19.00165DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7265794PMC
May 2020

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

BMJ Open 2019 10 15;9(10):e025556. Epub 2019 Oct 15.

Provision Proton Therapy Center, Knoxville, Tennessee, USA.

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1136/bmjopen-2018-025556DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6797426PMC
October 2019

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

Yearb Med Inform 2019 Aug 16;28(1):140-151. Epub 2019 Aug 16.

University of Arkansas for Medical Sciences, Arkansas, USA.

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1055/s-0039-1677912DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6697506PMC
August 2019

Factors Associated with Increased Adoption of a Research Data Warehouse.

Stud Health Technol Inform 2019 ;257:31-35

Biomedical Informatics, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR.

The increased demand of clinical data for the conduct of clinical and translational research incentivized repurposing of the University of Arkansas for Medical Sciences' enterprise data warehouse (EDW) to meet researchers' data needs. The EDW was renamed the Arkansas Clinical Data Repository (AR-CDR), underwent content enhancements, and deployed a self-service cohort estimation tool in late of 2016. In an effort to increase adoption of the AR-CDR, a team of physician informaticist and information technology professionals conducted various informational sessions across the UAMS campus to increase awareness of the AR-CDR and the informatics capabilities. The restructuring of the data warehouse resulted in four-fold utilization increase of the AR-CDR data services in 2017. To assess acceptance rates of the AR-CDR and quantify outcomes of services provided, Everett Rogers' diffusion of innovation (DOI) framework was applied, and a survey was distributed. Results show the factors that had impact on increased adoption were: presence of physician informaticist to mediate interactions between researchers and analysts, data quality, communication with and engagement of researchers, and the AR-CDR's team responsiveness and customer service mindset.
View Article and Find Full Text PDF

Download full-text PDF

Source
August 2019

Highly accurate model for prediction of lung nodule malignancy with CT scans.

Sci Rep 2018 06 18;8(1):9286. Epub 2018 Jun 18.

Department of Computer Science, Arkansas State University, Jonesboro, Arkansas, 72467, United States of America.

Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer. It remains challenging for computational approaches to achieve performance comparable to experienced radiologists. Here we present NoduleX, a systematic approach to predict lung nodule malignancy from CT data, based on deep learning convolutional neural networks (CNN). For training and validation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort. All nodules were identified and classified by four experienced thoracic radiologists who participated in the LIDC project. NoduleX achieves high accuracy for nodule malignancy classification, with an AUC of ~0.99. This is commensurate with the analysis of the dataset by experienced radiologists. Our approach, NoduleX, provides an effective framework for highly accurate nodule malignancy prediction with the model trained on a large patient population. Our results are replicable with software available at http://bioinformatics.astate.edu/NoduleX .
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41598-018-27569-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6006355PMC
June 2018

The public cancer radiology imaging collections of The Cancer Imaging Archive.

Sci Data 2017 09 19;4:170124. Epub 2017 Sep 19.

Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research, Frederick, Maryland 20892, USA.

The Cancer Imaging Archive (TCIA) is the U.S. National Cancer Institute's repository for cancer imaging and related information. TCIA contains 30.9 million radiology images representing data collected from approximately 37,568 subjects. This data is organized into collections by tumor-type with many collections also including analytic results or clinical data. TCIA staff carefully de-identify and curate all incoming collections prior to making the information available via web browser or programmatic interfaces. Each published collection within TCIA is assigned a Digital Object Identifier that references the collection. Additionally, researchers who use TCIA data may publish the subset of information used in their analysis by requesting a TCIA generated Digital Object Identifier. This data descriptor is a review of a selected subset of existing publicly available TCIA collections. It outlines the curation and publication methods employed by TCIA and makes available 15 collections of cancer imaging data.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/sdata.2017.124DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5827108PMC
September 2017

A Candidate Imaging Marker for Early Detection of Charcot Neuroarthropathy.

J Clin Densitom 2018 Oct - Dec;21(4):485-492. Epub 2017 Jun 28.

Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA.

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jocd.2017.05.008DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5745321PMC
November 2019

Persistent inflammation with pedal osteolysis 1year after Charcot neuropathic osteoarthropathy.

J Diabetes Complications 2017 Jun 14;31(6):1014-1020. Epub 2017 Feb 14.

Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AK.

Aims: To determine local and systemic markers of inflammation and bone mineral density (BMD) in the foot and central sites in participants with diabetes mellitus and peripheral neuropathy (DMPN) with and without acute Charcot neuropathic osteoarthropathy (CN).

Methods: Eighteen participants with DMPN and CN and 19 participants without CN had foot temperature assessments, serum markers of inflammation [C-reactive protein, (CRP) and erythrocyte sedimentation rate, (ESR)] and BMD of the foot, hip and lumbar spine at baseline and 1year follow-up.

Results: CN foot temperature difference was higher compared to DMPN controls at baseline (4.2±1.9°F vs. 1.2±0.9°F, P<0.01) and after 1year (2.9±3.2°F vs. 0.9±1.1°F, P<0.01). Serum inflammatory markers in the CN group were greater at baseline and remained elevated 1year later compared to DMPN controls (CRP, P=0.02, ESR, P=0.03). All pedal bones' BMD decreased an average of 3% in the CN foot with no changes in hip or lumbar spine. DMPN controls' foot, hip and lumbar spine BMD remained unchanged.

Conclusions: Local and systemic inflammation persists 1 year after CN with an accompanying pedal osteolysis that may contribute to mid foot deformity which is the hallmark of the chronic Charcot foot.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jdiacomp.2017.02.005DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5438890PMC
June 2017

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

AJR Am J Roentgenol 2016 Mar;206(3):559-65

1 Department of Radiology, University of Chicago, 5841 S Maryland Ave, MC 2026, Chicago, IL 60637.

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.2214/AJR.15.14967DOI Listing
March 2016

Spatiotemporal analysis of the appearance of gamma-band Microstates in resting state MEG.

Annu Int Conf IEEE Eng Med Biol Soc 2015 ;2015:2637-40

Spatiotemporal analysis of EEG signal has revealed a rich set of methods to quantify neuronal activity using spatially global topographic templates, called Microstates. These methods complement more traditional spectral analysis, which uses band limited source data to determine defining differences in band power and peak characteristics. The high sampling rate and increased resistance to high frequency noise of MEG data offers an opportunity to explore the utility of spatiotemporal analysis over a wider spectrum than in EEG. In this work, we explore the utility of representing band limited MEG source data using established microstate techniques, especially in gamma frequency bands - a range yet unexplored using these techniques. We develop methods for gauging the goodness-of-fit achieved by resultant microstate templates and demonstrate sensor-level dispersion characteristics across wide-band signals as well as across signals filtered by canonical bands. These analyses reveal that, while high-frequency-band derived microstate templates are visually lawful, they fail to exhibit important explained variance and dispersion characteristics present in low- and full-band data necessary to meet the requirements of a microstate model.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1109/EMBC.2015.7318933DOI Listing
August 2016

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

Stat Med 2016 Feb 25;35(4):566-80. Epub 2015 Nov 25.

Department of Medicine, Washington University, St. Louis, MO, U.S.A.

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/sim.6757DOI Listing
February 2016
-->