Identifications of Similarity Metrics for Patients With Cancer: Protocol for a Scoping Review.

cancer research cancer similarity metrics patient similarity patient similarity applications precision medicine scoping review protocol

Journal

JMIR research protocols
ISSN: 1929-0748
Titre abrégé: JMIR Res Protoc
Pays: Canada
ID NLM: 101599504

Informations de publication

Date de publication:
04 Sep 2024
Historique:
received: 11 04 2024
accepted: 16 07 2024
revised: 19 06 2024
medline: 4 9 2024
pubmed: 4 9 2024
entrez: 4 9 2024
Statut: epublish

Résumé

Understanding the similarities of patients with cancer is essential to advancing personalized medicine, improving patient outcomes, and developing more effective and individualized treatments. It enables researchers to discover important patterns, biomarkers, and treatment strategies that can have a significant impact on cancer research and oncology. In addition, the identification of previously successfully treated patients supports oncologists in making treatment decisions for a new patient who is clinically or molecularly similar to the previous patient. The planned review aims to systematically summarize, map, and describe existing evidence to understand how patient similarity is defined and used in cancer research and clinical care. To systematically identify relevant studies and to ensure reproducibility and transparency of the review process, a comprehensive literature search will be conducted in several bibliographic databases, including Web of Science, PubMed, LIVIVIVO, and MEDLINE, covering the period from 1998 to February 2024. After the initial duplicate deletion phase, a study selection phase will be applied using Rayyan, which consists of 3 distinct steps: title and abstract screening, disagreement resolution, and full-text screening. To ensure the integrity and quality of the selection process, each of these steps is preceded by a pilot testing phase. This methodological process will culminate in the presentation of the final research results in a structured form according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) flowchart. The protocol has been registered in the Journal of Medical Internet Research. This protocol outlines the methodologies used in conducting the scoping review. A search of the specified electronic databases and after removing duplicates resulted in 1183 unique records. As of March 2024, the review process has moved to the full-text evaluation phase. At this stage, data extraction will be conducted using a pretested chart template. The scoping review protocol, centered on these main concepts, aims to systematically map the available evidence on patient similarity among patients with cancer. By defining the types of data sources, approaches, and methods used in the field, and aligning these with the research questions, the review will provide a foundation for future research and clinical application in personalized cancer care. This protocol will guide the literature search, data extraction, and synthesis of findings to achieve the review's objectives. DERR1-10.2196/58705.

Sections du résumé

BACKGROUND BACKGROUND
Understanding the similarities of patients with cancer is essential to advancing personalized medicine, improving patient outcomes, and developing more effective and individualized treatments. It enables researchers to discover important patterns, biomarkers, and treatment strategies that can have a significant impact on cancer research and oncology. In addition, the identification of previously successfully treated patients supports oncologists in making treatment decisions for a new patient who is clinically or molecularly similar to the previous patient.
OBJECTIVE OBJECTIVE
The planned review aims to systematically summarize, map, and describe existing evidence to understand how patient similarity is defined and used in cancer research and clinical care.
METHODS METHODS
To systematically identify relevant studies and to ensure reproducibility and transparency of the review process, a comprehensive literature search will be conducted in several bibliographic databases, including Web of Science, PubMed, LIVIVIVO, and MEDLINE, covering the period from 1998 to February 2024. After the initial duplicate deletion phase, a study selection phase will be applied using Rayyan, which consists of 3 distinct steps: title and abstract screening, disagreement resolution, and full-text screening. To ensure the integrity and quality of the selection process, each of these steps is preceded by a pilot testing phase. This methodological process will culminate in the presentation of the final research results in a structured form according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) flowchart. The protocol has been registered in the Journal of Medical Internet Research.
RESULTS RESULTS
This protocol outlines the methodologies used in conducting the scoping review. A search of the specified electronic databases and after removing duplicates resulted in 1183 unique records. As of March 2024, the review process has moved to the full-text evaluation phase. At this stage, data extraction will be conducted using a pretested chart template.
CONCLUSIONS CONCLUSIONS
The scoping review protocol, centered on these main concepts, aims to systematically map the available evidence on patient similarity among patients with cancer. By defining the types of data sources, approaches, and methods used in the field, and aligning these with the research questions, the review will provide a foundation for future research and clinical application in personalized cancer care. This protocol will guide the literature search, data extraction, and synthesis of findings to achieve the review's objectives.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) UNASSIGNED
DERR1-10.2196/58705.

Identifiants

pubmed: 39230952
pii: v13i1e58705
doi: 10.2196/58705
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

e58705

Informations de copyright

©Iryna Manuilova, Jan Bossenz, Annemarie Bianka Weise, Dominik Boehm, Cosima Strantz, Philipp Unberath, Niklas Reimer, Patrick Metzger, Thomas Pauli, Silke D Werle, Susann Schulze, Sonja Hiemer, Arsenij Ustjanzew, Hans A Kestler, Hauke Busch, Benedikt Brors, Jan Christoph. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 04.09.2024.

Auteurs

Iryna Manuilova (I)

Junior Research Group (Bio-) Medical Data Science, Faculty of Medicine, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany.
Data Integration Centre, University Hospital Halle (Saale), Halle (Saale), Germany.

Jan Bossenz (J)

Junior Research Group (Bio-) Medical Data Science, Faculty of Medicine, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany.

Annemarie Bianka Weise (AB)

Junior Research Group (Bio-) Medical Data Science, Faculty of Medicine, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany.

Dominik Boehm (D)

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 (C)

Medical Informatics, Institute for Medical Informatics, Biometrics and Epidemiology, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.

Philipp Unberath (P)

Medical Center for Information and Communication Technology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
SRH Fürth University of Applied Sciences, Fürth, Germany.

Niklas Reimer (N)

Medical Systems Biology Group, Lübeck Institute of Experimental Dermatology, University of Lübeck, Lübeck, Germany.
University Cancer Center Schleswig-Holstein, University Hospital Schleswig-Holstein, Lübeck, Germany.
Medical Data Integration Center, University Hospital Schleswig-Holstein, Lübeck, Germany.

Patrick Metzger (P)

Institute of Medical Bioinformatics and Systems Medicine, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Clinical Trial Office, German Cancer Research Center (DKFZ) Heidelberg, Heidelberg, Germany.

Thomas Pauli (T)

Institute of Medical Bioinformatics and Systems Medicine, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany.

Silke D Werle (SD)

Institute of Medical Systems Biology, Ulm University, Ulm, Germany.

Susann Schulze (S)

Krukenberg Cancer Center Halle (Saale), Halle (Saale), Germany.

Sonja Hiemer (S)

Krukenberg Cancer Center Halle (Saale), Halle (Saale), Germany.

Arsenij Ustjanzew (A)

Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany.

Hans A Kestler (HA)

Institute of Medical Systems Biology, Ulm University, Ulm, Germany.

Hauke Busch (H)

Medical Systems Biology Group, Lübeck Institute of Experimental Dermatology, University of Lübeck, Lübeck, Germany.
University Cancer Center Schleswig-Holstein, University Hospital Schleswig-Holstein, Lübeck, Germany.

Benedikt Brors (B)

Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
German Cancer Consortium, Heidelberg, Germany.
National Center for Tumor Diseases (NCT), Heidelberg, Germany.
Medical Faculty Heidelberg and Faculty of Biosciences, Heidelberg University, Heidelberg, Germany.

Jan Christoph (J)

Junior Research Group (Bio-) Medical Data Science, Faculty of Medicine, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany.
Data Integration Centre, University Hospital Halle (Saale), Halle (Saale), Germany.
Medical Informatics, Institute for Medical Informatics, Biometrics and Epidemiology, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.

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