Comprehensive Brain Tumour Characterisation with VERDICT-MRI: Evaluation of Cellular and Vascular Measures Validated by Histology

Author:

Figini Matteo1ORCID,Castellano Antonella2ORCID,Bailo Michele3ORCID,Callea Marcella4,Cadioli Marcello5,Bouyagoub Samira6,Palombo Marco17,Pieri Valentina2,Mortini Pietro3,Falini Andrea2,Alexander Daniel C.1,Cercignani Mara67,Panagiotaki Eleftheria1ORCID

Affiliation:

1. Centre for Medical Image Computing and Department of Computer Science, University College London, London WC1V 6LJ, UK

2. Neuroradiology Unit and CERMAC, IRCCS Ospedale San Raffaele, Vita-Salute San Raffaele University, 20132 Milan, Italy

3. Department of Neurosurgery and Gamma Knife Radiosurgery, IRCCS Ospedale San Raffaele, Vita-Salute San Raffaele University, 20132 Milan, Italy

4. Pathology Unit, IRCCS Ospedale San Raffaele, 20132 Milan, Italy

5. Philips Healthcare, 20126 Milan, Italy

6. Clinical Imaging Sciences Centre, Brighton and Sussex Medical School, Brighton BN1 9RR, UK

7. Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff CF24 4HQ, UK

Abstract

The aim of this work was to extend the VERDICT-MRI framework for modelling brain tumours, enabling comprehensive characterisation of both intra- and peritumoural areas with a particular focus on cellular and vascular features. Diffusion MRI data were acquired with multiple b-values (ranging from 50 to 3500 s/mm2), diffusion times, and echo times in 21 patients with brain tumours of different types and with a wide range of cellular and vascular features. We fitted a selection of diffusion models that resulted from the combination of different types of intracellular, extracellular, and vascular compartments to the signal. We compared the models using criteria for parsimony while aiming at good characterisation of all of the key histological brain tumour components. Finally, we evaluated the parameters of the best-performing model in the differentiation of tumour histotypes, using ADC (Apparent Diffusion Coefficient) as a clinical standard reference, and compared them to histopathology and relevant perfusion MRI metrics. The best-performing model for VERDICT in brain tumours was a three-compartment model accounting for anisotropically hindered and isotropically restricted diffusion and isotropic pseudo-diffusion. VERDICT metrics were compatible with the histological appearance of low-grade gliomas and metastases and reflected differences found by histopathology between multiple biopsy samples within tumours. The comparison between histotypes showed that both the intracellular and vascular fractions tended to be higher in tumours with high cellularity (glioblastoma and metastasis), and quantitative analysis showed a trend toward higher values of the intracellular fraction (fic) within the tumour core with increasing glioma grade. We also observed a trend towards a higher free water fraction in vasogenic oedemas around metastases compared to infiltrative oedemas around glioblastomas and WHO 3 gliomas as well as the periphery of low-grade gliomas. In conclusion, we developed and evaluated a multi-compartment diffusion MRI model for brain tumours based on the VERDICT framework, which showed agreement between non-invasive microstructural estimates and histology and encouraging trends for the differentiation of tumour types and sub-regions.

Funder

Engineering and Physical Sciences Research Council

UK Research and Innovation, Future Leaders Fellowship

Publisher

MDPI AG

Subject

Cancer Research,Oncology

Reference55 articles.

1. Pros and cons of current brain tumor imaging;Ellingson;Neuro Oncol.,2014

2. Sanvito, F., Castellano, A., and Falini, A. (2021). Advancements in Neuroimaging to Unravel Biological and Molecular Features of Brain Tumors. Cancers, 13.

3. Brain Cancer: Implication to Disease, Therapeutic Strategies and Tumor Targeted Drug Delivery Approaches;Shah;Recent Pat. Anticancer Drug Discov.,2018

4. Consensus recommendations for a standardized Brain Tumor Imaging Protocol in clinical trials;Ellingson;Neuro Oncol.,2015

5. Radiomics: From qualitative to quantitative imaging;Rogers;Br. J. Radiol.,2020

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3