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Machine Learning-Based Radiomics in Neuro-Oncology

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Machine Learning in Clinical Neuroscience

Part of the book series: Acta Neurochirurgica Supplement ((NEUROCHIRURGICA,volume 134))

Abstract

In the last decades, modern medicine has evolved into a data-centered discipline, generating massive amounts of granular high-dimensional data exceeding human comprehension. With improved computational methods, machine learning and artificial intelligence (AI) as tools for data processing and analysis are becoming more and more important. At the forefront of neuro-oncology and AI-research, the field of radiomics has emerged. Non-invasive assessments of quantitative radiological biomarkers mined from complex imaging characteristics across various applications are used to predict survival, discriminate between primary and secondary tumors, as well as between progression and pseudo-progression. In particular, the application of molecular phenotyping, envisioned in the field of radiogenomics, has gained popularity for both primary and secondary brain tumors. Although promising results have been obtained thus far, the lack of workflow standardization and availability of multicenter data remains challenging. The objective of this review is to provide an overview of novel applications of machine learning- and deep learning-based radiomics in primary and secondary brain tumors and their implications for future research in the field.

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Acknowledgements

FE is participant in the BIH Charité Junior Clinician Scientist Program funded by the Charité – Universitätsmedizin Berlin and Berlin Institute of Health at Charité (BIH).

Funding

JMK and DD are supported by the Bundesministerium für Bildung und Forschung (BMBF COMPLS3-022).

Conflicts of Interest/Competing Interests

None of the authors has any conflict of interest to disclose.

DK received travel grants from Accuray and has served as an advisory board member for Novocure, no conflicts of interest with regard to the current work exist.

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Authors and Affiliations

  1. Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany

    Felix Ehret

  2. Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiation Oncology, Berlin, Germany

    Felix Ehret & David Kaul

  3. European Cyberknife Center Munich, Munich, Germany

    Felix Ehret

  4. German Cancer Consortium (DKTK), Partner Site Berlin, German Cancer Research Center (DKFZ), Heidelberg, Germany

    David Kaul

  5. Department of Neurosurgery, Faculty of Medicine, RWTH Aachen University, Aachen, Germany

    Hans Clusmann, Daniel Delev & Julius M. Kernbach

  6. Neurosurgical Artificial Intelligence Laboratory Aachen (NAILA), RWTH Aachen University Hospital, Aachen, Germany

    Daniel Delev & Julius M. Kernbach

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  1. Felix Ehret
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  2. David Kaul
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  3. Hans Clusmann
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  4. Daniel Delev
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  5. Julius M. Kernbach
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Correspondence to Julius M. Kernbach .

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Editors and Affiliations

  1. Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland

    Victor E. Staartjes

  2. Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland

    Luca Regli

  3. Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland

    Carlo Serra

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Ehret, F., Kaul, D., Clusmann, H., Delev, D., Kernbach, J.M. (2022). Machine Learning-Based Radiomics in Neuro-Oncology. In: Staartjes, V.E., Regli, L., Serra, C. (eds) Machine Learning in Clinical Neuroscience. Acta Neurochirurgica Supplement, vol 134. Springer, Cham. https://doi.org/10.1007/978-3-030-85292-4_18

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