Radiomics in neuro-oncological clinical trials

Philipp Lohmann*, Enrico Franceschi, Philipp Vollmuth, Frédéric Dhermain, Michael Weller, Matthias Preusser, Marion Smits, Norbert Galldiks

*Corresponding author for this work

Research output: Contribution to journalReview articleAcademicpeer-review

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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.

Original languageEnglish
Pages (from-to)e841-e849
JournalThe Lancet Digital Health
Volume4
Issue number11
DOIs
Publication statusPublished - 1 Nov 2022

Bibliographical note

Funding Information:
This work was supported by the Deutsche Forschungsgemeinschaft (German Research Foundation; project number 428090865/SPP 2177 [PL and NG], and project number 491111487).

Publisher Copyright: © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license

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