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Boehm D, Strantz C, Christoph J, Busch H, Ganslandt T, Unberath P. Data Visualization Support for Tumor Boards and Clinical Oncology: Protocol for a Scoping Review. JMIR Res Protoc 2024; 13:e53627. [PMID: 38441925 PMCID: PMC10951826 DOI: 10.2196/53627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 12/17/2023] [Accepted: 12/19/2023] [Indexed: 03/07/2024] Open
Abstract
BACKGROUND Complex and expanding data sets in clinical oncology applications require flexible and interactive visualization of patient data to provide the maximum amount of information to physicians and other medical practitioners. Interdisciplinary tumor conferences in particular profit from customized tools to integrate, link, and visualize relevant data from all professions involved. OBJECTIVE The scoping review proposed in this protocol aims to identify and present currently available data visualization tools for tumor boards and related areas. The objective of the review will be to provide not only an overview of digital tools currently used in tumor board settings, but also the data included, the respective visualization solutions, and their integration into hospital processes. METHODS The planned scoping review process is based on the Arksey and O'Malley scoping study framework. The following electronic databases will be searched for articles published in English: PubMed, Web of Knowledge, and SCOPUS. Eligible articles will first undergo a deduplication step, followed by the screening of titles and abstracts. Second, a full-text screening will be used to reach the final decision about article selection. At least 2 reviewers will independently screen titles, abstracts, and full-text reports. Conflicting inclusion decisions will be resolved by a third reviewer. The remaining literature will be analyzed using a data extraction template proposed in this protocol. The template includes a variety of meta information as well as specific questions aiming to answer the research question: "What are the key features of data visualization solutions used in molecular and organ tumor boards, and how are these elements integrated and used within the clinical setting?" The findings will be compiled, charted, and presented as specified in the scoping study framework. Data for included tools may be supplemented with additional manual literature searches. The entire review process will be documented in alignment with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) flowchart. RESULTS The results of this scoping review will be reported per the expanded PRISMA-ScR guidelines. A preliminary search using PubMed, Web of Knowledge, and Scopus resulted in 1320 articles after deduplication that will be included in the further review process. We expect the results to be published during the second quarter of 2024. CONCLUSIONS Visualization is a key process in leveraging a data set's potentially available information and enabling its use in an interdisciplinary setting. The scoping review described in this protocol aims to present the status quo of visualization solutions for tumor board and clinical oncology applications and their integration into hospital processes. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/53627.
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Affiliation(s)
- Dominik Boehm
- Medical Center for Information and Communication Technology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Bavarian Cancer Research Center (Bayerisches Zentrum für Krebsforschung), Erlangen, Germany
| | - Cosima Strantz
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Jan Christoph
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Junior Research Group (Bio-)medical Data Science, Faculty of Medicine, Martin-Luther-University Halle-Wittenberg, Halle, Germany
| | - Hauke Busch
- Group for Medical Systems Biology, Lübeck Institute of Experimental Dermatology, University of Lübeck, Lübeck, Germany
| | - Thomas Ganslandt
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Philipp Unberath
- Medical Center for Information and Communication Technology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- SRH Fürth University of Applied Sciences, Fürth, Germany
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Renner C, Reimer N, Christoph J, Busch H, Metzger P, Boerries M, Ustjanzew A, Boehm D, Unberath P. Extending cBioPortal for Therapy Recommendation Documentation in Molecular Tumor Boards: Development and Usability Study. JMIR Med Inform 2023; 11:e50017. [PMID: 38079196 PMCID: PMC10750236 DOI: 10.2196/50017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 09/02/2023] [Accepted: 09/17/2023] [Indexed: 12/28/2023] Open
Abstract
BACKGROUND In molecular tumor boards (MTBs), patients with rare or advanced cancers are discussed by a multidisciplinary team of health care professionals. Software support for MTBs is lacking; in particular, tools for preparing and documenting MTB therapy recommendations need to be developed. OBJECTIVE We aimed to implement an extension to cBioPortal to provide a tool for the documentation of therapy recommendations from MTB sessions in a secure and standardized manner. The developed extension should be embedded in the patient view of cBioPortal to enable easy documentation during MTB sessions. The resulting architecture for storing therapy recommendations should be integrable into various hospital information systems. METHODS On the basis of a requirements analysis and technology analysis for authentication techniques, a prototype was developed and iteratively refined through a user-centered development process. In conclusion, the tool was evaluated via a usability evaluation, including interviews, structured questionnaires, and the System Usability Scale. RESULTS The patient view of cBioPortal was extended with a new tab that enables users to document MTB sessions and therapy recommendations. The role-based access control was expanded to allow for a finer distinction among the rights to view, edit, and delete data. The usability evaluation showed overall good usability and a System Usability Scale score of 83.57. CONCLUSIONS This study demonstrates how cBioPortal can be extended to not only visualize MTB patient data but also be used as a documentation platform for therapy recommendations.
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Affiliation(s)
- Christopher Renner
- Chair of Medical Informatics, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Niklas Reimer
- Group for Medical Systems Biology, Lübeck Institute of Experimental, Universität zu Lübeck, Lübeck, Germany
- Campus Lübeck, University Cancer Center Schleswig-Holstein, University Hospital Schleswig-Holstein, Lübeck, Germany
| | - Jan Christoph
- Chair of Medical Informatics, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
- Junior Research Group (Bio-) Medical Data Science, Faculty of Medicine, Martin-Luther-University Halle-Wittenberg, Halle, Germany
| | - Hauke Busch
- Group for Medical Systems Biology, Lübeck Institute of Experimental, Universität zu Lübeck, Lübeck, Germany
- Campus Lübeck, University Cancer Center Schleswig-Holstein, University Hospital Schleswig-Holstein, Lübeck, Germany
| | - Patrick Metzger
- Institute of Medical Bioinformatics and Systems Medicine, University of Freiburg Faculty of Medicine, University Medical Center Freiburg, Freiburg, Germany
| | - Melanie Boerries
- Institute of Medical Bioinformatics and Systems Medicine, University of Freiburg Faculty of Medicine, University Medical Center Freiburg, Freiburg, Germany
- Partner Site Freiburg, German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Arsenij Ustjanzew
- Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
- University Cancer Center, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Dominik Boehm
- Medical Center for Information and Communication Technology, Universitätsklinikum Erlangen, Erlangen, Germany
| | - Philipp Unberath
- Chair of Medical Informatics, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
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Wolfien M, Ahmadi N, Fitzer K, Grummt S, Heine KL, Jung IC, Krefting D, Kühn A, Peng Y, Reinecke I, Scheel J, Schmidt T, Schmücker P, Schüttler C, Waltemath D, Zoch M, Sedlmayr M. Ten Topics to Get Started in Medical Informatics Research. J Med Internet Res 2023; 25:e45948. [PMID: 37486754 PMCID: PMC10407648 DOI: 10.2196/45948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 03/29/2023] [Accepted: 04/11/2023] [Indexed: 07/25/2023] Open
Abstract
The vast and heterogeneous data being constantly generated in clinics can provide great wealth for patients and research alike. The quickly evolving field of medical informatics research has contributed numerous concepts, algorithms, and standards to facilitate this development. However, these difficult relationships, complex terminologies, and multiple implementations can present obstacles for people who want to get active in the field. With a particular focus on medical informatics research conducted in Germany, we present in our Viewpoint a set of 10 important topics to improve the overall interdisciplinary communication between different stakeholders (eg, physicians, computational experts, experimentalists, students, patient representatives). This may lower the barriers to entry and offer a starting point for collaborations at different levels. The suggested topics are briefly introduced, then general best practice guidance is given, and further resources for in-depth reading or hands-on tutorials are recommended. In addition, the topics are set to cover current aspects and open research gaps of the medical informatics domain, including data regulations and concepts; data harmonization and processing; and data evaluation, visualization, and dissemination. In addition, we give an example on how these topics can be integrated in a medical informatics curriculum for higher education. By recognizing these topics, readers will be able to (1) set clinical and research data into the context of medical informatics, understanding what is possible to achieve with data or how data should be handled in terms of data privacy and storage; (2) distinguish current interoperability standards and obtain first insights into the processes leading to effective data transfer and analysis; and (3) value the use of newly developed technical approaches to utilize the full potential of clinical data.
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Affiliation(s)
- Markus Wolfien
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- Center for Scalable Data Analytics and Artificial Intelligence, Dresden, Germany
| | - Najia Ahmadi
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Kai Fitzer
- Core Unit Data Integration Center, University Medicine Greifswald, Greifswald, Germany
| | - Sophia Grummt
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Kilian-Ludwig Heine
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Ian-C Jung
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Dagmar Krefting
- Department of Medical Informatics, University Medical Center, Goettingen, Germany
| | - Andreas Kühn
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Yuan Peng
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Ines Reinecke
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Julia Scheel
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany
| | - Tobias Schmidt
- Institute for Medical Informatics, University of Applied Sciences Mannheim, Mannheim, Germany
| | - Paul Schmücker
- Institute for Medical Informatics, University of Applied Sciences Mannheim, Mannheim, Germany
| | - Christina Schüttler
- Central Biobank Erlangen, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Dagmar Waltemath
- Core Unit Data Integration Center, University Medicine Greifswald, Greifswald, Germany
- Department of Medical Informatics, University Medicine Greifswald, Greifswald, Germany
| | - Michele Zoch
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Martin Sedlmayr
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- Center for Scalable Data Analytics and Artificial Intelligence, Dresden, Germany
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Huang Z, Yi L, Jin L, Chen J, Han Y, Zhang Y, Shi L. Systematic analysis of virus nucleic acid sensor DDX58 in malignant tumor. Front Microbiol 2022; 13:1085086. [PMID: 36601407 PMCID: PMC9807228 DOI: 10.3389/fmicb.2022.1085086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 12/02/2022] [Indexed: 12/23/2022] Open
Abstract
Introduction In December 2019, a novel epidemic of coronavirus pneumonia (COVID-19) was reported,and population-based studies had shown that cancer was a risk factor for death from COVID-19 infection. However, the molecular mechanism between COVID-19 and cancer remains indistinct. In this paper, we analyzed the nucleic acid sensor (DDX58) of SARS-CoV-2 virus, which is a significant gene related to virus infection. For purpose of clarifying the characteristics of DDX58 expression in malignant tumors, this study began to systematically analyze the DDX58 expression profile in the entire cancer type spectrum. Methods Using TCGA pan-cancer database and related data resources, we analyzed the expression, survival analysis, methylation expression, mutation status, microsatellite instability (MSI), immune related microenvironment, gene related network, function and drug sensitivity of DDX58. Results The expression level of DDX58 mRNA in most cancers was higher than the expression level in normal tissues. Through TIMER algorithm mining, we found that DDX58 expression was closely related to various levels of immune infiltration in pan-cancer. The promoter methylation level of DDX58 was significantly increased in multiple cancers. In addition, abnormal expression of DDX58 was related to MSI and TMB in multiple cancers, and the most common type of genomic mutation was "mutation." In the protein-protein interaction (PPI) network, we found that type I interferon, phagocytosis, ubiquitinase, and tumor pathways were significantly enriched. Finally, according to the expression of DDX58 indicated potential sensitive drugs such as Cediranib, VE-821, Itraconazole, JNJ-42756493, IWR-1, and Linsitinib. Discussion In conclusion, we had gained new insights into how DDX58 might contribute to tumor development, and DDX58 could be used as an immune-related biomarker and as a potential immunotherapeutic target for COVID-19 infected cancer patients.
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Affiliation(s)
- Zhijian Huang
- Department of Breast Surgical Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China
| | - Limu Yi
- Department of Pathology, The First Affiliated Hospital of Guangdong University of Pharmacy, Guangzhou, China
| | - Liangzi Jin
- Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Kunming, China
| | - Jian Chen
- Department of Breast Surgical Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China
| | - Yuanyuan Han
- Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Kunming, China,*Correspondence: Yuanyuan Han,
| | - Yan Zhang
- Department of Pathology, The First Affiliated Hospital of Guangdong University of Pharmacy, Guangzhou, China,Department of Pathology, Maternity and Child Healthcare Hospital of Longhua District, Shenzhen, China,Yan Zhang,
| | - Libin Shi
- Department of Nuclear Medicine, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China,Libin Shi,
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Rodríguez Ruiz N, Abd Own S, Ekström Smedby K, Eloranta S, Koch S, Wästerlid T, Krstic A, Boman M. Data-driven support to decision-making in molecular tumour boards for lymphoma: A design science approach. Front Oncol 2022; 12:984021. [DOI: 10.3389/fonc.2022.984021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 10/03/2022] [Indexed: 11/17/2022] Open
Abstract
BackgroundThe increasing amount of molecular data and knowledge about genomic alterations from next-generation sequencing processes together allow for a greater understanding of individual patients, thereby advancing precision medicine. Molecular tumour boards feature multidisciplinary teams of clinical experts who meet to discuss complex individual cancer cases. Preparing the meetings is a manual and time-consuming process.PurposeTo design a clinical decision support system to improve the multimodal data interpretation in molecular tumour board meetings for lymphoma patients at Karolinska University Hospital, Stockholm, Sweden. We investigated user needs and system requirements, explored the employment of artificial intelligence, and evaluated the proposed design with primary stakeholders.MethodsDesign science methodology was used to form and evaluate the proposed artefact. Requirements elicitation was done through a scoping review followed by five semi-structured interviews. We used UML Use Case diagrams to model user interaction and UML Activity diagrams to inform the proposed flow of control in the system. Additionally, we modelled the current and future workflow for MTB meetings and its proposed machine learning pipeline. Interactive sessions with end-users validated the initial requirements based on a fictive patient scenario which helped further refine the system.ResultsThe analysis showed that an interactive secure Web-based information system supporting the preparation of the meeting, multidisciplinary discussions, and clinical decision-making could address the identified requirements. Integrating artificial intelligence via continual learning and multimodal data fusion were identified as crucial elements that could provide accurate diagnosis and treatment recommendations.ImpactOur work is of methodological importance in that using artificial intelligence for molecular tumour boards is novel. We provide a consolidated proof-of-concept system that could support the end-to-end clinical decision-making process and positively and immediately impact patients.ConclusionAugmenting a digital decision support system for molecular tumour boards with retrospective patient material is promising. This generates realistic and constructive material for human learning, and also digital data for continual learning by data-driven artificial intelligence approaches. The latter makes the future system adaptable to human bias, improving adequacy and decision quality over time and over tasks, while building and maintaining a digital log.
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