Universität zu Köln

Lohmann, Philipp, Franceschi, Enrico, Vollmuth, Philipp, Dhermain, Frederic, Weller, Michael ORCID: 0000-0002-1748-174X, Preusser, Matthias, Smits, Marion ORCID: 0000-0001-5563-2871 and Galldiks, Norbert (2022). Radiomics in neuro-oncological clinical trials. Lancet Digit. Health, 4 (11). S. e841 - 9. AMSTERDAM: ELSEVIER. ISSN 2589-7500

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Abstract

The development of clinical trials has led to substantial improvements in the prevention and treatment of many diseases, including brain cancer. Advances in medicine, such as improved surgical techniques, the development of new drugs and devices, the use of statistical methods in research, and the development of codes of ethics, have considerably influenced the way clinical trials are conducted today. In addition, methods from the broad field of artificial intelligence, such as radiomics, have the potential to considerably affect clinical trials and clinical practice in the future. Radiomics is a method to extract undiscovered features from routinely acquired imaging data that can neither be captured by means of human perception nor conventional image analysis. In patients with brain cancer, radiomics has shown its potential for the non-invasive identification of prognostic biomarkers, automated response assessment, and differentiation between treatment-related changes from tumour progression. Despite promising results, radiomics is not yet established in routine clinical practice nor in clinical trials. In this Viewpoint, the European Organization for Research and Treatment of Cancer Brain Tumour Group summarises the current status of radiomics, discusses its potential and limitations, envisions its future role in clinical trials in neuro-oncology, and provides guidance on how to address the challenges in radiomics.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Lohmann, Philipp UNSPECIFIED UNSPECIFIED UNSPECIFIED Franceschi, Enrico UNSPECIFIED UNSPECIFIED UNSPECIFIED Vollmuth, Philipp UNSPECIFIED UNSPECIFIED UNSPECIFIED Dhermain, Frederic UNSPECIFIED UNSPECIFIED UNSPECIFIED Weller, Michael UNSPECIFIED orcid.org/0000-0002-1748-174X UNSPECIFIED Preusser, Matthias UNSPECIFIED UNSPECIFIED UNSPECIFIED Smits, Marion UNSPECIFIED orcid.org/0000-0001-5563-2871 UNSPECIFIED Galldiks, Norbert UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-693626
DOI:	10.1016/S2589-7500(22)00144-3
Journal or Publication Title:	Lancet Digit. Health
Volume:	4
Number:	11
Page Range:	S. e841 - 9
Date:	2022
Publisher:	ELSEVIER
Place of Publication:	AMSTERDAM
ISSN:	2589-7500
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language ARTIFICIAL-INTELLIGENCE; IMAGING PREDICTOR; BRAIN METASTASES; RADIOSURGERY; PROGRESSION; FEATURES; IMAGES Multiple languages Medical Informatics; Medicine, General & Internal Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/69362