Unveiling the digital evolution of molecular tumor boards

  • Molecular tumor boards (MTB) are interdisciplinary conferences involving various experts discussing patients with advanced tumors, to derive individualized treatment suggestions based on molecular variants. These discussions involve using heterogeneous internal data, such as patient clinical data, but also external resources such as knowledge databases for annotations and search for relevant clinical studies. This imposes a certain level of complexity that requires huge effort to homogenize the data and use it in a speedy manner to reach the needed treatment. For this purpose, most institutions involving an MTB are heading toward automation and digitalization of the process, hence reducing manual work requiring human intervention and subsequently time in deriving personalized treatment suggestions. The tools are also used to better visualize the patient’s data, which allows a refined overview for the board members. In this paper, we present the results of our thorough literatureMolecular tumor boards (MTB) are interdisciplinary conferences involving various experts discussing patients with advanced tumors, to derive individualized treatment suggestions based on molecular variants. These discussions involve using heterogeneous internal data, such as patient clinical data, but also external resources such as knowledge databases for annotations and search for relevant clinical studies. This imposes a certain level of complexity that requires huge effort to homogenize the data and use it in a speedy manner to reach the needed treatment. For this purpose, most institutions involving an MTB are heading toward automation and digitalization of the process, hence reducing manual work requiring human intervention and subsequently time in deriving personalized treatment suggestions. The tools are also used to better visualize the patient’s data, which allows a refined overview for the board members. In this paper, we present the results of our thorough literature research about MTBs, their process, the most common knowledge bases, and tools used to support this decision-making process.show moreshow less

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Metadaten
Author:Sebastian LutzORCiD, Alicia D'Angelo, Sonja Hammerl, Maximilian Schmutz, Rainer ClausORCiDGND, Nina M. Fischer, Frank KramerORCiDGND, Zaynab HammoudORCiD
URN:urn:nbn:de:bvb:384-opus4-1176716
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/117671
ISSN:1776-2596OPAC
ISSN:1776-260XOPAC
Parent Title (English):Targeted Oncology
Publisher:Springer Science and Business Media LLC
Place of publication:Berlin
Type:Article
Language:English
Year of first Publication:2025
Publishing Institution:Universität Augsburg
Release Date:2024/12/17
Volume:20
First Page:27
Last Page:43
DOI:https://doi.org/10.1007/s11523-024-01109-1
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Medizinische Fakultät
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für IT-Infrastrukturen für die Translationale Medizinische Forschung
Medizinische Fakultät / Universitätsklinikum
Medizinische Fakultät / Lehrstuhl für Innere Medizin mit Schwerpunkt Hämatologie und Onkologie
Medizinische Fakultät / Professur für personalisierte Tumormedizin und molekulare Onkologie
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Licence (German):CC-BY-NC 4.0: Creative Commons: Namensnennung - Nicht kommerziell (mit Print on Demand)