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Tripathi A, Waqas A, Venkatesan K, Yilmaz Y, Rasool G. Building Flexible, Scalable, and Machine Learning-Ready Multimodal Oncology Datasets. SENSORS (BASEL, SWITZERLAND) 2024; 24:1634. [PMID: 38475170 DOI: 10.3390/s24051634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Revised: 01/25/2024] [Accepted: 02/28/2024] [Indexed: 03/14/2024]
Abstract
The advancements in data acquisition, storage, and processing techniques have resulted in the rapid growth of heterogeneous medical data. Integrating radiological scans, histopathology images, and molecular information with clinical data is essential for developing a holistic understanding of the disease and optimizing treatment. The need for integrating data from multiple sources is further pronounced in complex diseases such as cancer for enabling precision medicine and personalized treatments. This work proposes Multimodal Integration of Oncology Data System (MINDS)-a flexible, scalable, and cost-effective metadata framework for efficiently fusing disparate data from public sources such as the Cancer Research Data Commons (CRDC) into an interconnected, patient-centric framework. MINDS consolidates over 41,000 cases from across repositories while achieving a high compression ratio relative to the 3.78 PB source data size. It offers sub-5-s query response times for interactive exploration. MINDS offers an interface for exploring relationships across data types and building cohorts for developing large-scale multimodal machine learning models. By harmonizing multimodal data, MINDS aims to potentially empower researchers with greater analytical ability to uncover diagnostic and prognostic insights and enable evidence-based personalized care. MINDS tracks granular end-to-end data provenance, ensuring reproducibility and transparency. The cloud-native architecture of MINDS can handle exponential data growth in a secure, cost-optimized manner while ensuring substantial storage optimization, replication avoidance, and dynamic access capabilities. Auto-scaling, access controls, and other mechanisms guarantee pipelines' scalability and security. MINDS overcomes the limitations of existing biomedical data silos via an interoperable metadata-driven approach that represents a pivotal step toward the future of oncology data integration.
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Affiliation(s)
- Aakash Tripathi
- Department of Machine Learning, Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
- Department of Electrical Engineering, University of South Florida, Tampa, FL 33620, USA
| | - Asim Waqas
- Department of Machine Learning, Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
- Department of Electrical Engineering, University of South Florida, Tampa, FL 33620, USA
| | - Kavya Venkatesan
- Department of Machine Learning, Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
| | - Yasin Yilmaz
- Department of Electrical Engineering, University of South Florida, Tampa, FL 33620, USA
| | - Ghulam Rasool
- Department of Machine Learning, Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
- Department of Electrical Engineering, University of South Florida, Tampa, FL 33620, USA
- Department of Neuro-Oncology, Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
- Department of Oncologic Sciences, University of South Florida, Tampa, FL 33612, USA
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Kumar B, Lorusso E, Fosso B, Pesole G. A comprehensive overview of microbiome data in the light of machine learning applications: categorization, accessibility, and future directions. Front Microbiol 2024; 15:1343572. [PMID: 38419630 PMCID: PMC10900530 DOI: 10.3389/fmicb.2024.1343572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 01/29/2024] [Indexed: 03/02/2024] Open
Abstract
Metagenomics, Metabolomics, and Metaproteomics have significantly advanced our knowledge of microbial communities by providing culture-independent insights into their composition and functional potential. However, a critical challenge in this field is the lack of standard and comprehensive metadata associated with raw data, hindering the ability to perform robust data stratifications and consider confounding factors. In this comprehensive review, we categorize publicly available microbiome data into five types: shotgun sequencing, amplicon sequencing, metatranscriptomic, metabolomic, and metaproteomic data. We explore the importance of metadata for data reuse and address the challenges in collecting standardized metadata. We also, assess the limitations in metadata collection of existing public repositories collecting metagenomic data. This review emphasizes the vital role of metadata in interpreting and comparing datasets and highlights the need for standardized metadata protocols to fully leverage metagenomic data's potential. Furthermore, we explore future directions of implementation of Machine Learning (ML) in metadata retrieval, offering promising avenues for a deeper understanding of microbial communities and their ecological roles. Leveraging these tools will enhance our insights into microbial functional capabilities and ecological dynamics in diverse ecosystems. Finally, we emphasize the crucial metadata role in ML models development.
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Affiliation(s)
- Bablu Kumar
- Università degli Studi di Milano, Milan, Italy
- Department of Biosciences, Biotechnology and Environment, University of Bari A. Moro, Bari, Italy
| | - Erika Lorusso
- Department of Biosciences, Biotechnology and Environment, University of Bari A. Moro, Bari, Italy
- National Research Council, Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, Bari, Italy
| | - Bruno Fosso
- Department of Biosciences, Biotechnology and Environment, University of Bari A. Moro, Bari, Italy
| | - Graziano Pesole
- Department of Biosciences, Biotechnology and Environment, University of Bari A. Moro, Bari, Italy
- National Research Council, Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, Bari, Italy
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3
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Brancato V, Cerrone M, Garbino N, Salvatore M, Cavaliere C. Current status of magnetic resonance imaging radiomics in hepatocellular carcinoma: A quantitative review with Radiomics Quality Score. World J Gastroenterol 2024; 30:381-417. [PMID: 38313230 PMCID: PMC10835534 DOI: 10.3748/wjg.v30.i4.381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/05/2023] [Accepted: 01/10/2024] [Indexed: 01/26/2024] Open
Abstract
BACKGROUND Radiomics is a promising tool that may increase the value of magnetic resonance imaging (MRI) for different tasks related to the management of patients with hepatocellular carcinoma (HCC). However, its implementation in clinical practice is still far, with many issues related to the methodological quality of radiomic studies. AIM To systematically review the current status of MRI radiomic studies concerning HCC using the Radiomics Quality Score (RQS). METHODS A systematic literature search of PubMed, Google Scholar, and Web of Science databases was performed to identify original articles focusing on the use of MRI radiomics for HCC management published between 2017 and 2023. The methodological quality of radiomic studies was assessed using the RQS tool. Spearman's correlation (ρ) analysis was performed to explore if RQS was correlated with journal metrics and characteristics of the studies. The level of statistical signi-ficance was set at P < 0.05. RESULTS One hundred and twenty-seven articles were included, of which 43 focused on HCC prognosis, 39 on prediction of pathological findings, 16 on prediction of the expression of molecular markers outcomes, 18 had a diagnostic purpose, and 11 had multiple purposes. The mean RQS was 8 ± 6.22, and the corresponding percentage was 24.15% ± 15.25% (ranging from 0.0% to 58.33%). RQS was positively correlated with journal impact factor (IF; ρ = 0.36, P = 2.98 × 10-5), 5-years IF (ρ = 0.33, P = 1.56 × 10-4), number of patients included in the study (ρ = 0.51, P < 9.37 × 10-10) and number of radiomics features extracted in the study (ρ = 0.59, P < 4.59 × 10-13), and time of publication (ρ = -0.23, P < 0.0072). CONCLUSION Although MRI radiomics in HCC represents a promising tool to develop adequate personalized treatment as a noninvasive approach in HCC patients, our study revealed that studies in this field still lack the quality required to allow its introduction into clinical practice.
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Affiliation(s)
- Valentina Brancato
- Department of Information Technology, IRCCS SYNLAB SDN, Naples 80143, Italy
| | - Marco Cerrone
- Department of Radiology, IRCCS SYNLAB SDN, Naples 80143, Italy
| | - Nunzia Garbino
- Department of Radiology, IRCCS SYNLAB SDN, Naples 80143, Italy
| | - Marco Salvatore
- Department of Radiology, IRCCS SYNLAB SDN, Naples 80143, Italy
| | - Carlo Cavaliere
- Department of Radiology, IRCCS SYNLAB SDN, Naples 80143, Italy
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Huang W, Tao Z, Younis MH, Cai W, Kang L. Nuclear medicine radiomics in digestive system tumors: Concept, applications, challenges, and future perspectives. VIEW 2023; 4:20230032. [PMID: 38179181 PMCID: PMC10766416 DOI: 10.1002/viw.20230032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 07/20/2023] [Indexed: 01/06/2024] Open
Abstract
Radiomics aims to develop novel biomarkers and provide relevant deeper subvisual information about pathology, immunophenotype, and tumor microenvironment. It uses automated or semiautomated quantitative analysis of high-dimensional images to improve characterization, diagnosis, and prognosis. Recent years have seen a rapid increase in radiomics applications in nuclear medicine, leading to some promising research results in digestive system oncology, which have been driven by big data analysis and the development of artificial intelligence. Although radiomics advances one step further toward the non-invasive precision medical analysis, it is still a step away from clinical application and faces many challenges. This review article summarizes the available literature on digestive system tumors regarding radiomics in nuclear medicine. First, we describe the workflow and steps involved in radiomics analysis. Subsequently, we discuss the progress in clinical application regarding the utilization of radiomics for distinguishing between various diseases and evaluating their prognosis, and demonstrate how radiomics advances this field. Finally, we offer our viewpoint on how the field can progress by addressing the challenges facing clinical implementation.
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Affiliation(s)
- Wenpeng Huang
- Department of Nuclear Medicine, Peking University First Hospital, Beijing, China
| | - Zihao Tao
- Department of Nuclear Medicine, Peking University First Hospital, Beijing, China
| | - Muhsin H. Younis
- Departments of Radiology and Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Weibo Cai
- Departments of Radiology and Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Lei Kang
- Department of Nuclear Medicine, Peking University First Hospital, Beijing, China
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Post AR, Ho N, Rasmussen E, Post I, Cho A, Hofer J, Maness AT, Parnell T, Nix DA. Hypermedia-based software architecture enables Test-Driven Development. JAMIA Open 2023; 6:ooad089. [PMID: 37860604 PMCID: PMC10582517 DOI: 10.1093/jamiaopen/ooad089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 08/12/2023] [Accepted: 10/04/2023] [Indexed: 10/21/2023] Open
Abstract
Objectives Using agile software development practices, develop and evaluate an architecture and implementation for reliable and user-friendly self-service management of bioinformatic data stored in the cloud. Materials and methods Comprehensive Oncology Research Environment (CORE) Browser is a new open-source web application for cancer researchers to manage sequencing data organized in a flexible format in Amazon Simple Storage Service (S3) buckets. It has a microservices- and hypermedia-based architecture, which we integrated with Test-Driven Development (TDD), the iterative writing of computable specifications for how software should work prior to development. Relying on repeating patterns found in hypermedia-based architectures, we hypothesized that hypermedia would permit developing test "templates" that can be parameterized and executed for each microservice, maximizing code coverage while minimizing effort. Results After one-and-a-half years of development, the CORE Browser backend had 121 test templates and 875 custom tests that were parameterized and executed 3031 times, providing 78% code coverage. Discussion Architecting to permit test reuse through a hypermedia approach was a key success factor for our testing efforts. CORE Browser's application of hypermedia and TDD illustrates one way to integrate software engineering methods into data-intensive networked applications. Separating bioinformatic data management from analysis distinguishes this platform from others in bioinformatics and may provide stable data management while permitting analysis methods to advance more rapidly. Conclusion Software engineering practices are underutilized in informatics. Similar informatics projects will more likely succeed through application of good architecture and automated testing. Our approach is broadly applicable to data management tools involving cloud data storage.
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Affiliation(s)
- Andrew R Post
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, United States
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT 84112, United States
| | - Nancy Ho
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, United States
| | - Erik Rasmussen
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, United States
| | - Ivan Post
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, United States
| | - Aika Cho
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, United States
| | - John Hofer
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, United States
| | - Arthur T Maness
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, United States
| | - Timothy Parnell
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, United States
| | - David A Nix
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, United States
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6
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Bayer JM, Scully RA, Dlabola EK, Courtwright JL, Hirsch CL, Hockman-Wert D, Miller SW, Roper BB, Saunders WC, Snyder MN. Sharing FAIR monitoring program data improves discoverability and reuse. ENVIRONMENTAL MONITORING AND ASSESSMENT 2023; 195:1141. [PMID: 37665400 DOI: 10.1007/s10661-023-11788-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 08/24/2023] [Indexed: 09/05/2023]
Abstract
Data resulting from environmental monitoring programs are valuable assets for natural resource managers, decision-makers, and researchers. These data are often collected to inform specific reporting needs or decisions with a specific timeframe. While program-oriented data and related publications are effective for meeting program goals, sharing well-documented data and metadata allows users to research aspects outside initial program intentions. As part of an effort to integrate data from four long-term large-scale US aquatic monitoring programs, we evaluated the original datasets against the FAIR (Findable, Accessible, Interoperable, Reusable) data principles and offer recommendations and lessons learned. Differences in data governance across these programs resulted in considerable effort to access and reuse the original datasets. Requirements, guidance, and resources available to support data publishing and documentation are inconsistent across agencies and monitoring programs, resulting in various data formats and storage locations that are not easily found, accessed, or reused. Making monitoring data FAIR will reduce barriers to data discovery and reuse. Programs are continuously striving to improve data management, data products, and metadata; however, provision of related tools, consistent guidelines and standards, and more resources to do this work is needed. Given the value of these data and the significant effort required to access and reuse them, actions and steps intended on improving data documentation and accessibility are described.
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Affiliation(s)
- Jennifer M Bayer
- U.S. Geological Survey, Pacific Northwest Aquatic Monitoring Partnership, Cook, WA, 98605, USA.
| | - Rebecca A Scully
- U.S. Geological Survey, Pacific Northwest Aquatic Monitoring Partnership, Cook, WA, 98605, USA
| | - Erin K Dlabola
- U.S. Geological Survey, Forest and Rangeland Ecosystem Science Center, Corvallis, OR, 97331, USA
| | - Jennifer L Courtwright
- Watershed Sciences Department, College of Natural Resources, Utah State University, Logan, UT, 84322, USA
| | - Christine L Hirsch
- United States Forest Service, Pacific Northwest Research Station, Corvallis, OR, 97331, USA
| | - David Hockman-Wert
- United States Forest Service, Pacific Northwest Research Station, Corvallis, OR, 97331, USA
| | - Scott W Miller
- Bureau of Land Management, National Operations Center, Denver, CO, 80225, USA
| | - Brett B Roper
- United States Forest Service, National Stream and Aquatic Ecology Center, Logan, UT, 84332, USA
| | - W Carl Saunders
- PACFISH/INFISH Biological Opinion Monitoring Program, United States Forest Service, Logan, UT, 84332, USA
| | - Marcía N Snyder
- United States Forest Service, Pacific Northwest Research Station, Corvallis, OR, 97331, USA
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7
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Peng W, Qiao H, Mo L, Guo Y. Progress in the diagnosis of lymph node metastasis in rectal cancer: a review. Front Oncol 2023; 13:1167289. [PMID: 37519802 PMCID: PMC10374255 DOI: 10.3389/fonc.2023.1167289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 06/28/2023] [Indexed: 08/01/2023] Open
Abstract
Historically, the chief focus of lymph node metastasis research has been molecular and clinical studies of a few essential pathways and genes. Recent years have seen a rapid accumulation of massive omics and imaging data catalyzed by the rapid development of advanced technologies. This rapid increase in data has driven improvements in the accuracy of diagnosis of lymph node metastasis, and its analysis further demands new methods and the opportunity to provide novel insights for basic research. In fact, the combination of omics data, imaging data, clinical medicine, and diagnostic methods has led to notable advances in our basic understanding and transformation of lymph node metastases in rectal cancer. Higher levels of integration will require a concerted effort among data scientists and clinicians. Herein, we review the current state and future challenges to advance the diagnosis of lymph node metastases in rectal cancer.
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Affiliation(s)
- Wei Peng
- Medical Big Data and Bioinformatics Research Centre, First Affiliated Hospital of Gannan Medical University, Ganzhou, Jiangxi, China
- School of Public Health and Health Management, Gannan Medical University, Ganzhou, Jiangxi, China
| | - Huimin Qiao
- Medical Big Data and Bioinformatics Research Centre, First Affiliated Hospital of Gannan Medical University, Ganzhou, Jiangxi, China
- School of Public Health and Health Management, Gannan Medical University, Ganzhou, Jiangxi, China
| | - Linfeng Mo
- School of Health and Medicine, Guangzhou Huashang Vocational College, Guangzhou, Guangdong, China
| | - You Guo
- Medical Big Data and Bioinformatics Research Centre, First Affiliated Hospital of Gannan Medical University, Ganzhou, Jiangxi, China
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8
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Touré V, Krauss P, Gnodtke K, Buchhorn J, Unni D, Horki P, Raisaro JL, Kalt K, Teixeira D, Crameri K, Österle S. FAIRification of health-related data using semantic web technologies in the Swiss Personalized Health Network. Sci Data 2023; 10:127. [PMID: 36899064 PMCID: PMC10006404 DOI: 10.1038/s41597-023-02028-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 02/17/2023] [Indexed: 03/12/2023] Open
Abstract
The Swiss Personalized Health Network (SPHN) is a government-funded initiative developing federated infrastructures for a responsible and efficient secondary use of health data for research purposes in compliance with the FAIR principles (Findable, Accessible, Interoperable and Reusable). We built a common standard infrastructure with a fit-for-purpose strategy to bring together health-related data and ease the work of both data providers to supply data in a standard manner and researchers by enhancing the quality of the collected data. As a result, the SPHN Resource Description Framework (RDF) schema was implemented together with a data ecosystem that encompasses data integration, validation tools, analysis helpers, training and documentation for representing health metadata and data in a consistent manner and reaching nationwide data interoperability goals. Data providers can now efficiently deliver several types of health data in a standardised and interoperable way while a high degree of flexibility is granted for the various demands of individual research projects. Researchers in Switzerland have access to FAIR health data for further use in RDF triplestores.
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Affiliation(s)
- Vasundra Touré
- Personalized Health Informatics Group, SIB Swiss Institute of Bioinformatics, 4051, Basel, Switzerland
| | - Philip Krauss
- Trivadis - Part of Accenture, 4051, Basel, Switzerland
| | - Kristin Gnodtke
- Personalized Health Informatics Group, SIB Swiss Institute of Bioinformatics, 4051, Basel, Switzerland
| | | | - Deepak Unni
- Personalized Health Informatics Group, SIB Swiss Institute of Bioinformatics, 4051, Basel, Switzerland
| | - Petar Horki
- Personalized Health Informatics Group, SIB Swiss Institute of Bioinformatics, 4051, Basel, Switzerland
| | - Jean Louis Raisaro
- Health Informatics and Data Privacy Group, Biomedical Data Science Center, 1010 Lausanne University Hospital, Lausanne, Switzerland
| | - Katie Kalt
- Clinical Data Platform Research, Directorate of Research and Education, Zurich University Hospital, 8091, Zurich, Switzerland
| | - Daniel Teixeira
- DSI - Data Group, Geneva University Hospital, 1205, Geneva, Switzerland
| | - Katrin Crameri
- Personalized Health Informatics Group, SIB Swiss Institute of Bioinformatics, 4051, Basel, Switzerland
| | - Sabine Österle
- Personalized Health Informatics Group, SIB Swiss Institute of Bioinformatics, 4051, Basel, Switzerland.
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Du X, Dastmalchi F, Ye H, Garrett TJ, Diller MA, Liu M, Hogan WR, Brochhausen M, Lemas DJ. Evaluating LC-HRMS metabolomics data processing software using FAIR principles for research software. Metabolomics 2023; 19:11. [PMID: 36745241 DOI: 10.1007/s11306-023-01974-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 01/20/2023] [Indexed: 02/07/2023]
Abstract
BACKGROUND Liquid chromatography-high resolution mass spectrometry (LC-HRMS) is a popular approach for metabolomics data acquisition and requires many data processing software tools. The FAIR Principles - Findability, Accessibility, Interoperability, and Reusability - were proposed to promote open science and reusable data management, and to maximize the benefit obtained from contemporary and formal scholarly digital publishing. More recently, the FAIR principles were extended to include Research Software (FAIR4RS). AIM OF REVIEW This study facilitates open science in metabolomics by providing an implementation solution for adopting FAIR4RS in the LC-HRMS metabolomics data processing software. We believe our evaluation guidelines and results can help improve the FAIRness of research software. KEY SCIENTIFIC CONCEPTS OF REVIEW We evaluated 124 LC-HRMS metabolomics data processing software obtained from a systematic review and selected 61 software for detailed evaluation using FAIR4RS-related criteria, which were extracted from the literature along with internal discussions. We assigned each criterion one or more FAIR4RS categories through discussion. The minimum, median, and maximum percentages of criteria fulfillment of software were 21.6%, 47.7%, and 71.8%. Statistical analysis revealed no significant improvement in FAIRness over time. We identified four criteria covering multiple FAIR4RS categories but had a low %fulfillment: (1) No software had semantic annotation of key information; (2) only 6.3% of evaluated software were registered to Zenodo and received DOIs; (3) only 14.5% of selected software had official software containerization or virtual machine; (4) only 16.7% of evaluated software had a fully documented functions in code. According to the results, we discussed improvement strategies and future directions.
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Affiliation(s)
- Xinsong Du
- Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, USA
| | - Farhad Dastmalchi
- Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, USA
| | - Hao Ye
- Health Science Center Libraries, University of Florida, Florida, USA
| | - Timothy J Garrett
- Department of Pathology, Immunology and Laboratory Medicine, College of Medicine, University of Florida, Florida, USA
| | - Matthew A Diller
- Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, USA
| | - Mei Liu
- Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, USA
| | - William R Hogan
- Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, USA
| | - Mathias Brochhausen
- Department of Biomedical Informatics, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, USA
| | - Dominick J Lemas
- Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, USA.
- Department of Obstetrics and Gynecology, University of Florida College of Medicine, Florida, Gainesville, United States.
- Center for Perinatal Outcomes Research, University of Florida College of Medicine, Gainesville, United States.
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Alvarellos M, Sheppard HE, Knarston I, Davison C, Raine N, Seeger T, Prieto Barja P, Chatzou Dunford M. Democratizing clinical-genomic data: How federated platforms can promote benefits sharing in genomics. Front Genet 2023; 13:1045450. [PMID: 36704354 PMCID: PMC9871385 DOI: 10.3389/fgene.2022.1045450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 12/19/2022] [Indexed: 01/12/2023] Open
Abstract
Since the first sequencing of the human genome, associated sequencing costs have dramatically lowered, leading to an explosion of genomic data. This valuable data should in theory be of huge benefit to the global community, although unfortunately the benefits of these advances have not been widely distributed. Much of today's clinical-genomic data is siloed and inaccessible in adherence with strict governance and privacy policies, with more than 97% of hospital data going unused, according to one reference. Despite these challenges, there are promising efforts to make clinical-genomic data accessible and useful without compromising security. Specifically, federated data platforms are emerging as key resources to facilitate secure data sharing without having to physically move the data from outside of its organizational or jurisdictional boundaries. In this perspective, we summarize the overarching progress in establishing federated data platforms, and highlight critical considerations on how they should be managed to ensure patient and public trust. These platforms are enabling global collaboration and improving representation of underrepresented groups, since sequencing efforts have not prioritized diverse population representation until recently. Federated data platforms, when combined with advances in no-code technology, can be accessible to the diverse end-users that make up the genomics workforce, and we discuss potential strategies to develop sustainable business models so that the platforms can continue to enable research long term. Although these platforms must be carefully managed to ensure appropriate and ethical use, they are democratizing access and insights to clinical-genomic data that will progress research and enable impactful therapeutic findings.
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11
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Yan G, Yan G, Li H, Liang H, Peng C, Bhetuwal A, McClure MA, Li Y, Yang G, Li Y, Zhao L, Fan X. Radiomics and Its Applications and Progress in Pancreatitis: A Current State of the Art Review. Front Med (Lausanne) 2022; 9:922299. [PMID: 35814756 PMCID: PMC9259974 DOI: 10.3389/fmed.2022.922299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Accepted: 05/31/2022] [Indexed: 11/25/2022] Open
Abstract
Radiomics involves high-throughput extraction and analysis of quantitative information from medical images. Since it was proposed in 2012, there are some publications on the application of radiomics for (1) predicting recurrent acute pancreatitis (RAP), clinical severity of acute pancreatitis (AP), and extrapancreatic necrosis in AP; (2) differentiating mass-forming chronic pancreatitis (MFCP) from pancreatic ductal adenocarcinoma (PDAC), focal autoimmune pancreatitis (AIP) from PDAC, and functional abdominal pain (functional gastrointestinal diseases) from RAP and chronic pancreatitis (CP); and (3) identifying CP and normal pancreas, and CP risk factors and complications. In this review, we aim to systematically summarize the applications and progress of radiomics in pancreatitis and it associated situations, so as to provide reference for related research.
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Affiliation(s)
- Gaowu Yan
- Department of Radiology, Suining Central Hospital, Suining, China
| | - Gaowen Yan
- Department of Radiology, The First Hospital of Suining, Suining, China
| | - Hongwei Li
- Department of Radiology, The Third Hospital of Mianyang and Sichuan Mental Health Center, Mianyang, China
| | - Hongwei Liang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Chen Peng
- Department of Gastroenterology, The First Hospital of Suining, Suining, China
| | - Anup Bhetuwal
- Sichuan Key Laboratory of Medical Imaging, Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Morgan A. McClure
- Department of Radiology and Imaging, Institute of Rehabilitation and Development of Brain Function, The Second Clinical Medical College of North Sichuan Medical College, Nanchong Central Hospital, Nanchong, China
| | - Yongmei Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- *Correspondence: Yongmei Li
| | - Guoqing Yang
- Department of Radiology, Suining Central Hospital, Suining, China
- Guoqing Yang
| | - Yong Li
- Department of Radiology, Suining Central Hospital, Suining, China
- Yong Li
| | - Linwei Zhao
- Department of Radiology, Suining Central Hospital, Suining, China
| | - Xiaoping Fan
- Department of Radiology, Suining Central Hospital, Suining, China
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12
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A Systematic Review of the Current Status and Quality of Radiomics for Glioma Differential Diagnosis. Cancers (Basel) 2022; 14:cancers14112731. [PMID: 35681711 PMCID: PMC9179305 DOI: 10.3390/cancers14112731] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 05/26/2022] [Accepted: 05/30/2022] [Indexed: 02/01/2023] Open
Abstract
Simple Summary Gliomas can be difficult to discern clinically and radiologically from other brain lesions (either neoplastic or non-neoplastic) since their clinical manifestations as well as preoperative imaging features often overlap and appear misleading. Radiomics could be extremely helpful for non-invasive glioma differential diagnosis (DDx). However, implementation in clinical practice is still distant and concerns have been raised regarding the methodological quality of radiomic studies. In this context, we aimed to summarize the current status and quality of radiomic studies concerning glioma DDx in a systematic review. In total, 42 studies were selected and examined in our work. Our study revealed that, despite promising and encouraging results, current studies on radiomics for glioma DDx still lack the quality required to allow its introduction into clinical practice. This work could provide new insights and help to reach a consensus on the use of the radiomic approach for glioma DDx. Abstract Radiomics is a promising tool that may increase the value of imaging in differential diagnosis (DDx) of glioma. However, implementation in clinical practice is still distant and concerns have been raised regarding the methodological quality of radiomic studies. Therefore, we aimed to systematically review the current status of radiomic studies concerning glioma DDx, also using the radiomics quality score (RQS) to assess the quality of the methodology used in each study. A systematic literature search was performed to identify original articles focused on the use of radiomics for glioma DDx from 2015. Methodological quality was assessed using the RQS tool. Spearman’s correlation (ρ) analysis was performed to explore whether RQS was correlated with journal metrics and the characteristics of the studies. Finally, 42 articles were selected for the systematic qualitative analysis. Selected articles were grouped and summarized in terms of those on DDx between glioma and primary central nervous system lymphoma, those aiming at differentiating glioma from brain metastases, and those based on DDx of glioma and other brain diseases. Median RQS was 8.71 out 36, with a mean RQS of all studies of 24.21%. Our study revealed that, despite promising and encouraging results, current studies on radiomics for glioma DDx still lack the quality required to allow its introduction into clinical practice. This work could provide new insights and help to reach a consensus on the use of the radiomic approach for glioma DDx.
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13
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Perceptions and behavior of clinical researchers and research support staff regarding data FAIRification. Sci Data 2022; 9:241. [PMID: 35624282 PMCID: PMC9142513 DOI: 10.1038/s41597-022-01325-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 04/20/2022] [Indexed: 11/11/2022] Open
Abstract
The FAIR Data Principles are being rapidly adopted by many research institutes and funders worldwide. This study aimed to assess the awareness and attitudes of clinical researchers and research support staff regarding data FAIRification. A questionnaire was distributed to researchers and support staff in six Dutch University Medical Centers and Electronic Data Capture platform users. 164 researchers and 21 support staff members completed the questionnaire. 62.8% of the researchers and 81.0% of the support staff are currently undertaking at least some effort to achieve any aspect of FAIR, 11.0% and 23.8%, respectively, address all aspects. Only 46.6% of the researchers add metadata to their datasets, 39.7% add metadata to data elements, and 35.9% deposit their data in a repository. 94.7% of the researchers are aware of the usefulness of their data being FAIR for others and 89.3% are, given the right resources and support, willing to FAIRify their data. Institutions and funders should, therefore, develop FAIRification training and tools and should (financially) support researchers and staff throughout the process.
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14
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van der Velde KJ, Singh G, Kaliyaperumal R, Liao X, de Ridder S, Rebers S, Kerstens HHD, de Andrade F, van Reeuwijk J, De Gruyter FE, Hiltemann S, Ligtvoet M, Weiss MM, van Deutekom HWM, Jansen AML, Stubbs AP, Vissers LELM, Laros JFJ, van Enckevort E, Stemkens D, 't Hoen PAC, Beliën JAM, van Gijn ME, Swertz MA. FAIR Genomes metadata schema promoting Next Generation Sequencing data reuse in Dutch healthcare and research. Sci Data 2022; 9:169. [PMID: 35418585 PMCID: PMC9008059 DOI: 10.1038/s41597-022-01265-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Accepted: 03/25/2022] [Indexed: 11/08/2022] Open
Abstract
The genomes of thousands of individuals are profiled within Dutch healthcare and research each year. However, this valuable genomic data, associated clinical data and consent are captured in different ways and stored across many systems and organizations. This makes it difficult to discover rare disease patients, reuse data for personalized medicine and establish research cohorts based on specific parameters. FAIR Genomes aims to enable NGS data reuse by developing metadata standards for the data descriptions needed to FAIRify genomic data while also addressing ELSI issues. We developed a semantic schema of essential data elements harmonized with international FAIR initiatives. The FAIR Genomes schema v1.1 contains 110 elements in 9 modules. It reuses common ontologies such as NCIT, DUO and EDAM, only introducing new terms when necessary. The schema is represented by a YAML file that can be transformed into templates for data entry software (EDC) and programmatic interfaces (JSON, RDF) to ease genomic data sharing in research and healthcare. The schema, documentation and MOLGENIS reference implementation are available at https://fairgenomes.org .
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Affiliation(s)
- K Joeri van der Velde
- University of Groningen and University Medical Center Groningen, Genomics Coordination Center, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands
- University of Groningen and University Medical Center Groningen, Department of Genetics, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands
| | - Gurnoor Singh
- Radboud University Medical Center, Radboud Institute for Molecular Life Sciences, Center for Molecular and Biomolecular Informatics, Geert Grooteplein 28, 6525 GA, Nijmegen, The Netherlands
| | - Rajaram Kaliyaperumal
- Leiden University Medical Center, Department of Human Genetics, Einthovenweg 20, 2333 ZC, Leiden, The Netherlands
| | - XiaoFeng Liao
- Radboud University Medical Center, Radboud Institute for Molecular Life Sciences, Center for Molecular and Biomolecular Informatics, Geert Grooteplein 28, 6525 GA, Nijmegen, The Netherlands
| | - Sander de Ridder
- Amsterdam University Medical Center, University of Amsterdam, Department of Pathology, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
| | - Susanne Rebers
- The Netherlands Cancer Institute, Division of Molecular Pathology, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Hindrik H D Kerstens
- Prinses Máxima Center for Pediatric Oncology, Kemmeren group, Heidelberglaan 25, 3584 CS, Utrecht, The Netherlands
| | - Fernanda de Andrade
- University of Groningen and University Medical Center Groningen, Genomics Coordination Center, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands
| | - Jeroen van Reeuwijk
- Radboud University Medical Center, Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Geert Grooteplein 10, 6525 GA, Nijmegen, The Netherlands
| | - Fini E De Gruyter
- University Medical Center Utrecht, Department of Genetics, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
| | - Saskia Hiltemann
- Erasmus Medical Center, Department of Pathology, Doctor Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
| | - Maarten Ligtvoet
- Nictiz - Dutch competence centre for electronic exchange of health and care information, Oude Middenweg 55, 2491 AC, The Hague, The Netherlands
| | - Marjan M Weiss
- Radboud University Medical Center, Department of Human Genetics, Geert Grooteplein 10, 6525 GA, Nijmegen, The Netherlands
| | - Hanneke W M van Deutekom
- University Medical Center Utrecht, Department of Genetics, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
| | - Anne M L Jansen
- University Medical Center Utrecht, Department of Pathology, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
| | - Andrew P Stubbs
- Erasmus Medical Center, Department of Pathology, Doctor Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
| | - Lisenka E L M Vissers
- Radboud University Medical Center, Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Geert Grooteplein 10, 6525 GA, Nijmegen, The Netherlands
| | - Jeroen F J Laros
- Leiden University Medical Center, Department of Human Genetics, Einthovenweg 20, 2333 ZC, Leiden, The Netherlands
- Leiden University Medical Center, Department of Clinical Genetics, Einthovenweg 20, 2333 ZC, Leiden, The Netherlands
- Rijksinstituut voor Volksgezondheid en Milieu, Antonie van Leeuwenhoeklaan 9, 3721 MA, Bilthoven, The Netherlands
| | - Esther van Enckevort
- University of Groningen and University Medical Center Groningen, Genomics Coordination Center, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands
| | - Daphne Stemkens
- VSOP - Patient Alliance for Rare and Genetic Diseases The Netherlands, Koninginnelaan 23, 3762 DA, Soest, The Netherlands
| | - Peter A C 't Hoen
- Radboud University Medical Center, Radboud Institute for Molecular Life Sciences, Center for Molecular and Biomolecular Informatics, Geert Grooteplein 28, 6525 GA, Nijmegen, The Netherlands
| | - Jeroen A M Beliën
- Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Department of Pathology, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Mariëlle E van Gijn
- University of Groningen and University Medical Center Groningen, Department of Genetics, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands
| | - Morris A Swertz
- University of Groningen and University Medical Center Groningen, Genomics Coordination Center, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands.
- University of Groningen and University Medical Center Groningen, Department of Genetics, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands.
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15
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Silva MC, Eugénio P, Faria D, Pesquita C. Ontologies and Knowledge Graphs in Oncology Research. Cancers (Basel) 2022; 14:cancers14081906. [PMID: 35454813 PMCID: PMC9029532 DOI: 10.3390/cancers14081906] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 03/25/2022] [Accepted: 04/07/2022] [Indexed: 11/16/2022] Open
Abstract
The complexity of cancer research stems from leaning on several biomedical disciplines for relevant sources of data, many of which are complex in their own right. A holistic view of cancer—which is critical for precision medicine approaches—hinges on integrating a variety of heterogeneous data sources under a cohesive knowledge model, a role which biomedical ontologies can fill. This study reviews the application of ontologies and knowledge graphs in cancer research. In total, our review encompasses 141 published works, which we categorized under 14 hierarchical categories according to their usage of ontologies and knowledge graphs. We also review the most commonly used ontologies and newly developed ones. Our review highlights the growing traction of ontologies in biomedical research in general, and cancer research in particular. Ontologies enable data accessibility, interoperability and integration, support data analysis, facilitate data interpretation and data mining, and more recently, with the emergence of the knowledge graph paradigm, support the application of Artificial Intelligence methods to unlock new knowledge from a holistic view of the available large volumes of heterogeneous data.
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16
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Schwanitz VJ, Wierling A, Biresselioglu ME, Celino M, Demir MH, Bałazińska M, Kruczek M, Paier M, Suna D. Current state and call for action to accomplish findability, accessibility, interoperability, and reusability of low carbon energy data. Sci Rep 2022; 12:5208. [PMID: 35338179 PMCID: PMC8956656 DOI: 10.1038/s41598-022-08774-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 03/11/2022] [Indexed: 11/19/2022] Open
Abstract
With the continued digitization of the energy sector, the problem of sunken scholarly data investments and forgone opportunities of harvesting existing data is exacerbating. It compounds the problem that the reproduction of knowledge is incomplete, impeding the transparency of science-based targets for the choices made in the energy transition. The FAIR data guiding principles are widely acknowledged as a way forward, but their operationalization is yet to be agreed upon within different research domains. We comprehensively test FAIR data practices in the low carbon energy research domain. 80 databases representative for data needed to support the low carbon energy transition are screened. Automated and manual tests are used to document the state-of-the art and provide insights on bottlenecks from the human and machine perspectives. We propose action items for overcoming the problem with FAIR energy data and suggest how to prioritize activities.
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Affiliation(s)
- Valeria Jana Schwanitz
- Western Norway University of Applied Sciences, Department of Environmental Sciences, Sogndal, 6856, Norway.
| | - August Wierling
- Western Norway University of Applied Sciences, Department of Environmental Sciences, Sogndal, 6856, Norway
| | | | - Massimo Celino
- ENEA, Lungotevere Thaon di Revel, 76, 00196, Roma, Italia
| | - Muhittin Hakan Demir
- Izmir University of Economics, Sustainable Energy Division, Izmir, 35330, Turkey
| | | | | | - Manfred Paier
- AIT Austrian Institute of Technology, Vienna, 1210, Austria
| | - Demet Suna
- AIT Austrian Institute of Technology, Vienna, 1210, Austria
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17
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Aiello M, Esposito G, Pagliari G, Borrelli P, Brancato V, Salvatore M. How does DICOM support big data management? Investigating its use in medical imaging community. Insights Imaging 2021; 12:164. [PMID: 34748101 PMCID: PMC8574146 DOI: 10.1186/s13244-021-01081-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 08/25/2021] [Indexed: 12/15/2022] Open
Abstract
The diagnostic imaging field is experiencing considerable growth, followed by increasing production of massive amounts of data. The lack of standardization and privacy concerns are considered the main barriers to big data capitalization. This work aims to verify whether the advanced features of the DICOM standard, beyond imaging data storage, are effectively used in research practice. This issue will be analyzed by investigating the publicly shared medical imaging databases and assessing how much the most common medical imaging software tools support DICOM in all its potential. Therefore, 100 public databases and ten medical imaging software tools were selected and examined using a systematic approach. In particular, the DICOM fields related to privacy, segmentation and reporting have been assessed in the selected database; software tools have been evaluated for reading and writing the same DICOM fields. From our analysis, less than a third of the databases examined use the DICOM format to record meaningful information to manage the images. Regarding software, the vast majority does not allow the management, reading and writing of some or all the DICOM fields. Surprisingly, if we observe chest computed tomography data sharing to address the COVID-19 emergency, there are only two datasets out of 12 released in DICOM format. Our work shows how the DICOM can potentially fully support big data management; however, further efforts are still needed from the scientific and technological community to promote the use of the existing standard, encouraging data sharing and interoperability for a concrete development of big data analytics.
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Affiliation(s)
- Marco Aiello
- IRCCS SDN, Via Emanuele Gianturco 113, 80143, Naples, Italy.
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18
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Kourou K, Exarchos KP, Papaloukas C, Sakaloglou P, Exarchos T, Fotiadis DI. Applied machine learning in cancer research: A systematic review for patient diagnosis, classification and prognosis. Comput Struct Biotechnol J 2021; 19:5546-5555. [PMID: 34712399 PMCID: PMC8523813 DOI: 10.1016/j.csbj.2021.10.006] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 10/04/2021] [Accepted: 10/04/2021] [Indexed: 02/08/2023] Open
Abstract
Artificial Intelligence (AI) has recently altered the landscape of cancer research and medical oncology using traditional Machine Learning (ML) algorithms and cutting-edge Deep Learning (DL) architectures. In this review article we focus on the ML aspect of AI applications in cancer research and present the most indicative studies with respect to the ML algorithms and data used. The PubMed and dblp databases were considered to obtain the most relevant research works of the last five years. Based on a comparison of the proposed studies and their research clinical outcomes concerning the medical ML application in cancer research, three main clinical scenarios were identified. We give an overview of the well-known DL and Reinforcement Learning (RL) methodologies, as well as their application in clinical practice, and we briefly discuss Systems Biology in cancer research. We also provide a thorough examination of the clinical scenarios with respect to disease diagnosis, patient classification and cancer prognosis and survival. The most relevant studies identified in the preceding year are presented along with their primary findings. Furthermore, we examine the effective implementation and the main points that need to be addressed in the direction of robustness, explainability and transparency of predictive models. Finally, we summarize the most recent advances in the field of AI/ML applications in cancer research and medical oncology, as well as some of the challenges and open issues that need to be addressed before data-driven models can be implemented in healthcare systems to assist physicians in their daily practice.
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Affiliation(s)
- Konstantina Kourou
- Unit of Medical Technology and Intelligent Information Systems, Dept. of Materials Science and Engineering, University of Ioannina, Ioannina, Greece
- Foundation for Research and Technology-Hellas, Institute of Molecular Biology and Biotechnology, Dept. of Biomedical Research, Ioannina GR45110, Greece
| | | | - Costas Papaloukas
- Dept. of Biological Applications and Technology, University of Ioannina, Ioannina, Greece
| | - Prodromos Sakaloglou
- Dept. of Precision and Molecular Medicine, Unit of Liquid Biopsy in Oncology, Ioannina University Hospital, Ioannina, Greece
- Laboratory of Medical Genetics in Clinical Practice, School of Health Sciences, Faculty of Medicine, University of Ioannina, Ioannina, Greece
| | | | - Dimitrios I. Fotiadis
- Unit of Medical Technology and Intelligent Information Systems, Dept. of Materials Science and Engineering, University of Ioannina, Ioannina, Greece
- Foundation for Research and Technology-Hellas, Institute of Molecular Biology and Biotechnology, Dept. of Biomedical Research, Ioannina GR45110, Greece
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19
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Campos MLM, Silva E, Cerceau R, da Cruz SMS, Silva FAB, Gouveia FC, Jardim R, Kotowski N, Lopes GR, Dávila AMR. Towards Machine-Readable (Meta) Data and the FAIR Value for Artificial Intelligence Exploration of COVID-19 and Cancer Research Data. Front Big Data 2021; 4:656553. [PMID: 34527943 PMCID: PMC8437372 DOI: 10.3389/fdata.2021.656553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 07/14/2021] [Indexed: 11/24/2022] Open
Affiliation(s)
- Maria Luiza. M. Campos
- Instituto de Computação, Universidade Federal do Rio de Janeiro, UFRJ, Rio de Janeiro, Brazil
| | - Eugênio Silva
- Unidade de Computação (Ucomp), Centro Universitario Estadual da Zona Oeste (UEZO), Rio de Janeiro, Brazil
| | - Renato Cerceau
- Instituto Nacional de Cardiologia, INC, Rio de Janeiro, Brazil
- Universidade do Estado do Rio de Janeiro, UERJ, Rio de Janeiro, Brazil
| | - Sérgio Manuel Serra da Cruz
- Instituto de Computação, Universidade Federal do Rio de Janeiro, UFRJ, Rio de Janeiro, Brazil
- Departamento de Ciências da Computação, Universidade Federal Rural do Rio de Janeiro, UFRRJ, Seropédica, Brazil
| | | | | | - Rodrigo Jardim
- Laboratório de Biologia Computacional e Sistemas, Instituto Oswaldo Cruz, FIOCRUZ, Rio de Janeiro, Brazil
| | - Nelson Kotowski
- Laboratório de Biologia Computacional e Sistemas, Instituto Oswaldo Cruz, FIOCRUZ, Rio de Janeiro, Brazil
| | - Giseli Rabello Lopes
- Instituto de Computação, Universidade Federal do Rio de Janeiro, UFRJ, Rio de Janeiro, Brazil
| | - Alberto. M. R. Dávila
- Laboratório de Biologia Computacional e Sistemas, Instituto Oswaldo Cruz, FIOCRUZ, Rio de Janeiro, Brazil
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20
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Delnord M, Van Valckenborgh E, Hebrant A, Antoniou A, Van Hoof W, Waeytens A, Van den Bulcke M. Precision cancer medicine: What has translated into clinical use in Belgium? Semin Cancer Biol 2021; 84:255-262. [PMID: 34129914 DOI: 10.1016/j.semcancer.2021.06.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 05/15/2021] [Accepted: 06/04/2021] [Indexed: 12/20/2022]
Abstract
RATIONALE In 2016, Belgium launched the Next Generation Sequencing (NGS) Roadbook, consisting in 10 Actions, across the health care system, to facilitate the uptake of NGS in routine clinical practice. We compiled feedback on deployment of the NGS Roadbook from governmental stakeholders and beneficiaries: health policy makers, insurance providers, pathologists, geneticists, and oncologists. MAIN FINDINGS The Roadbook ensured the establishment of a Commission on Personalized Medicine and NGS testing guidelines. A national benchmarking trial ensued, and 10 networks of hospitals and laboratories adopted a reimbursement convention with the Belgian Health Insurance Agency. The Healthdata.be platform centralizes the collection of NGS metrics, and citizens were consulted on their views about NGS and genomics. CONCLUSION The Roadbook facilitated the implementation of NGS in routine (hemato-)oncology care in Belgium. Some challenges remain linked to data sharing and access by a wider range of stakeholders. Next steps include continuous monitoring of health outcomes and the budgetary impact.
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Affiliation(s)
- M Delnord
- Cancer Center, Department of Epidemiology and Public Health, Sciensano, Brussels, Belgium.
| | - Els Van Valckenborgh
- Cancer Center, Department of Epidemiology and Public Health, Sciensano, Brussels, Belgium
| | - Aline Hebrant
- Cancer Center, Department of Epidemiology and Public Health, Sciensano, Brussels, Belgium
| | | | - Wannes Van Hoof
- Cancer Center, Department of Epidemiology and Public Health, Sciensano, Brussels, Belgium
| | - Anouk Waeytens
- Health Care Department, National Institute for Health and Disability Insurance, Brussels, Belgium
| | - M Van den Bulcke
- Cancer Center, Department of Epidemiology and Public Health, Sciensano, Brussels, Belgium
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21
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Inau ET, Sack J, Waltemath D, Zeleke AA. Initiatives, Concepts, and Implementation Practices of FAIR (Findable, Accessible, Interoperable, and Reusable) Data Principles in Health Data Stewardship Practice: Protocol for a Scoping Review. JMIR Res Protoc 2021; 10:e22505. [PMID: 33528373 PMCID: PMC7886612 DOI: 10.2196/22505] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Revised: 12/02/2020] [Accepted: 12/08/2020] [Indexed: 01/21/2023] Open
Abstract
Background Data stewardship is an essential driver of research and clinical practice. Data collection, storage, access, sharing, and analytics are dependent on the proper and consistent use of data management principles among the investigators. Since 2016, the FAIR (findable, accessible, interoperable, and reusable) guiding principles for research data management have been resonating in scientific communities. Enabling data to be findable, accessible, interoperable, and reusable is currently believed to strengthen data sharing, reduce duplicated efforts, and move toward harmonization of data from heterogeneous unconnected data silos. FAIR initiatives and implementation trends are rising in different facets of scientific domains. It is important to understand the concepts and implementation practices of the FAIR data principles as applied to human health data by studying the flourishing initiatives and implementation lessons relevant to improved health research, particularly for data sharing during the coronavirus pandemic. Objective This paper aims to conduct a scoping review to identify concepts, approaches, implementation experiences, and lessons learned in FAIR initiatives in the health data domain. Methods The Arksey and O’Malley stage-based methodological framework for scoping reviews will be used for this review. PubMed, Web of Science, and Google Scholar will be searched to access relevant primary and grey publications. Articles written in English and published from 2014 onwards with FAIR principle concepts or practices in the health domain will be included. Duplication among the 3 data sources will be removed using a reference management software. The articles will then be exported to a systematic review management software. At least two independent authors will review the eligibility of each article based on defined inclusion and exclusion criteria. A pretested charting tool will be used to extract relevant information from the full-text papers. Qualitative thematic synthesis analysis methods will be employed by coding and developing themes. Themes will be derived from the research questions and contents in the included papers. Results The results will be reported using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-analyses Extension for Scoping Reviews) reporting guidelines. We anticipate finalizing the manuscript for this work in 2021. Conclusions We believe comprehensive information about the FAIR data principles, initiatives, implementation practices, and lessons learned in the FAIRification process in the health domain is paramount to supporting both evidence-based clinical practice and research transparency in the era of big data and open research publishing. International Registered Report Identifier (IRRID) PRR1-10.2196/22505
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Affiliation(s)
- Esther Thea Inau
- Medical Informatics, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Jean Sack
- International Health Department, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Dagmar Waltemath
- Medical Informatics, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Atinkut Alamirrew Zeleke
- Medical Informatics, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
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22
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Abstract
The term axial spondyloarthritis (axSpA) encompasses a heterogeneous group of diseases that have variable presentations, extra-articular manifestations and clinical outcomes, and that will respond differently to treatments. The prototypical type of axSpA, ankylosing spondylitis, is thought to be caused by interaction between the genetically primed host immune system and gut microbiota. Currently used biomarkers such as HLA-B27 status, C-reactive protein and erythrocyte sedimentation rate have, at best, moderate diagnostic and predictive value. Improved biomarkers are needed for axSpA to assist with early diagnosis and to better predict treatment responses and long-term outcomes. Advances in a range of 'omics' technologies and statistical approaches, including genomics approaches (such as polygenic risk scores), microbiome profiling and, potentially, transcriptomic, proteomic and metabolomic profiling, are making it possible for more informative biomarker sets to be developed for use in such clinical applications. Future developments in this field will probably involve combinations of biomarkers that require novel statistical approaches to analyse and to produce easy to interpret metrics for clinical application. Large publicly available datasets from well-characterized case-cohort studies that use extensive biological sampling, particularly focusing on early disease and responses to medications, are required to establish successful biomarker discovery and validation programmes.
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23
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Development of a Precision Medicine Workflow in Hematological Cancers, Aalborg University Hospital, Denmark. Cancers (Basel) 2020; 12:cancers12020312. [PMID: 32013121 PMCID: PMC7073219 DOI: 10.3390/cancers12020312] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Revised: 01/10/2020] [Accepted: 01/27/2020] [Indexed: 12/17/2022] Open
Abstract
Within recent years, many precision cancer medicine initiatives have been developed. Most of these have focused on solid cancers, while the potential of precision medicine for patients with hematological malignancies, especially in the relapse situation, are less elucidated. Here, we present a demographic unbiased and observational prospective study at Aalborg University Hospital Denmark, referral site for 10% of the Danish population. We developed a hematological precision medicine workflow based on sequencing analysis of whole exome tumor DNA and RNA. All steps involved are outlined in detail, illustrating how the developed workflow can provide relevant molecular information to multidisciplinary teams. A group of 174 hematological patients with progressive disease or relapse was included in a non-interventional and population-based study, of which 92 patient samples were sequenced. Based on analysis of small nucleotide variants, copy number variants, and fusion transcripts, we found variants with potential and strong clinical relevance in 62% and 9.5% of the patients, respectively. The most frequently mutated genes in individual disease entities were in concordance with previous studies. We did not find tumor mutational burden or micro satellite instability to be informative in our hematologic patient cohort.
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