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Künstliche Intelligenz und maschinelles Lernen in der onkologischen Bildgebung

Artificial intelligence and machine learning in oncologic imaging

  • Leitthema
  • Published:
Der Onkologe Aims and scope

Zusammenfassung

Hintergrund

Maschinelles Lernen (ML) hält gegenwärtig Einzug in vielen Bereichen der Gesellschaft, so auch in der Medizin. Diese Transformation birgt das Potenzial, das Berufsbild und den Berufsalltag drastisch zu verändern, auch wenn diese Neuerungen bis jetzt nur vereinzelt die klinische Praxis beeinflussen und mit Risiken verbunden sein können. In den Stadien und der Interaktion zwischen den Disziplinen und Modalitäten der onkologischen Patientenversorgung wird dies besonders deutlich. Computer erbringen in mehreren Forschungsarbeiten in Kollaboration mit Menschen oder allein bereits bessere Ergebnisse als Menschen in der Tumoridentifikation, ihrer Klassifikation sowie beim Erstellen von Prognosen und der Evaluation von Therapien. Zudem können Algorithmen – z. B. künstliche neuronale Netze (KNN), welche für viele der gegenwärtigen Errungenschaften im ML-Feld verantwortlich sind – dies reproduzierbar, schnell und kostengünstig erbringen.

Ziel der Arbeit

In dieser Übersichtsarbeit wird der gegenwärtige Forschungsstand beispielhaft anhand von ausgewählten Tumorentitäten beleuchtet und in die Entwicklung des Forschungsgebiets und der Medizin eingeordnet.

Material und Methoden

Diese Arbeit basiert auf einer selektiven Literaturrecherche in den Datenbanken PubMed und arXiv.

Schlussfolgerungen

Zukünftig werden KI-Anwendungen sich zu einem integralen Bestandteil des ärztlichen Handels entwickeln und Vorteile für die onkologische Diagnostik und Therapie bieten.

Abstract

Background

Machine learning (ML) is finding entry into many areas of society, including medicine. This transformation has the potential to drastically change the perception of medicine and medical practice. While these advances currently only influence clinical routine in isolated cases, they also come with risks. These aspects become particularly clear when considering the different stages of oncologic patient care and the involved interdisciplinary and intermodality interactions. In recent publications, computers—in collaboration with humans or alone—have been outperforming humans. This pertains to tumor identification, tumor classification, creation of prognoses, and evaluation of treatments. Additionally, ML algorithms, e.g., artificial neural networks (ANNs), which constitute the drivers behind many of the latest achievements in ML, can deliver this level of performance in a reproducible, fast, and cheap manner.

Objective

This review elucidates the current state of research on ML in oncology by focusing on selected tumor entities, and relates this to the development of research and medicine as a whole.

Materials and methods

This work is based on a selective literature search in the databases PubMed and arXiv.

Conclusion

In the future, AI applications will develop into an integral part of the medical profession and offer advantages for oncologic diagnostics and treatment.

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Authors and Affiliations

  1. AG Computational Radiology, Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Deutschland

    Jens Kleesiek & Jacob M. Murray

  2. German Cancer Consortium (DKTK), Heidelberg, Deutschland

    Jens Kleesiek &  Rickmer Braren

  3. Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, München, Deutschland

    Georgios Kaissis &  Rickmer Braren

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  1. Jens Kleesiek
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  3. Georgios Kaissis
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  4. Rickmer Braren
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Correspondence to Jens Kleesiek.

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Interessenkonflikt

J. Kleesiek, J.M. Murray, G. Kaissis und R. Braren geben an, dass kein Interessenkonflikt besteht.

Für diesen Beitrag wurden von den Autoren keine Studien an Menschen oder Tieren durchgeführt. Für die aufgeführten Studien gelten die jeweils dort angegebenen ethischen Richtlinien.

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Kleesiek, J., Murray, J.M., Kaissis, G. et al. Künstliche Intelligenz und maschinelles Lernen in der onkologischen Bildgebung. Onkologe 26, 60–65 (2020). https://doi.org/10.1007/s00761-019-00679-4

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