Assessment of brain cancer atlas maps with multimodal imaging features

Journal of Translational Medicine

Review

Marco Dominietto¹  Enrico Capobianco² 

[1] Paul Scherrer Institute (PSI), Forschungsstrasse 111, 5232, Villigen, Switzerland;Gate To Brain SA, Via Livio 7, 6830, Chiasso, Switzerland;The Jackson Laboratory, 10 Discovery Drive, 06032, Farmington, CT, USA;

关键词: GBM;    MRI imaging;    Brain cancer atlas;    Radiomics;    Multimodal integration;   

DOI  :  10.1186/s12967-023-04222-3

 received in 2023-04-18, accepted in 2023-05-22,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundGlioblastoma Multiforme (GBM) is a fast-growing and highly aggressive brain tumor that invades the nearby brain tissue and presents secondary nodular lesions across the whole brain but generally does not spread to distant organs. Without treatment, GBM can result in death in about 6 months. The challenges are known to depend on multiple factors: brain localization, resistance to conventional therapy, disrupted tumor blood supply inhibiting effective drug delivery, complications from peritumoral edema, intracranial hypertension, seizures, and neurotoxicity.Main textImaging techniques are routinely used to obtain accurate detections of lesions that localize brain tumors. Especially magnetic resonance imaging (MRI) delivers multimodal images both before and after the administration of contrast, which results in displaying enhancement and describing physiological features as hemodynamic processes. This review considers one possible extension of the use of radiomics in GBM studies, one that recalibrates the analysis of targeted segmentations to the whole organ scale. After identifying critical areas of research, the focus is on illustrating the potential utility of an integrated approach with multimodal imaging, radiomic data processing and brain atlases as the main components. The templates associated with the outcome of straightforward analyses represent promising inference tools able to spatio-temporally inform on the GBM evolution while being generalizable also to other cancers.ConclusionsThe focus on novel inference strategies applicable to complex cancer systems and based on building radiomic models from multimodal imaging data can be well supported by machine learning and other computational tools potentially able to translate suitably processed information into more accurate patient stratifications and evaluations of treatment efficacy.Graphical Abstract

【 授权许可】

CC BY   
© The Author(s) 2023

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