Header

UZH-Logo

Maintenance Infos

Imaging in pleural mesothelioma: A review of the 14th International Conference of the International Mesothelioma Interest Group


Armato, Samuel G; Francis, Roslyn J; Katz, Sharyn I; Ak, Guntulu; Opitz, Isabelle; Gudmundsson, Eyjolfur; Blyth, Kevin G; Gupta, Ashish (2019). Imaging in pleural mesothelioma: A review of the 14th International Conference of the International Mesothelioma Interest Group. Lung Cancer, 130:108-114.

Abstract

Mesothelioma patients rely on the information their clinical team obtains from medical imaging. Whether x-ray-based computed tomography (CT) or magnetic resonance imaging (MRI) based on local magnetic fields within a patient's tissues, different modalities generate images with uniquely different appearances and information content due to the physical differences of the image-acquisition process. Researchers are developing sophisticated ways to extract a greater amount of the information contained within these images. This paper summarizes the imaging-based research presented orally at the 2018 International Conference of the International Mesothelioma Interest Group (iMig) in Ottawa, Ontario, Canada, held May 2-5, 2018. Presented topics included advances in the imaging of preclinical mesothelioma models to inform clinical therapeutic strategies, optimization of the time delay between contrast administration and image acquisition for maximized enhancement of mesothelioma tumor on CT, an investigation of image-based criteria for clinical tumor and nodal staging of mesothelioma by contrast-enhanced CT, an investigation of methods for the extraction of mesothelioma tumor volume from MRI and the association of volume with patient survival, the use of deep learning for mesothelioma tumor segmentation in CT, and an evaluation of CT-based radiomics for the prognosis of mesothelioma patient survival.

Abstract

Mesothelioma patients rely on the information their clinical team obtains from medical imaging. Whether x-ray-based computed tomography (CT) or magnetic resonance imaging (MRI) based on local magnetic fields within a patient's tissues, different modalities generate images with uniquely different appearances and information content due to the physical differences of the image-acquisition process. Researchers are developing sophisticated ways to extract a greater amount of the information contained within these images. This paper summarizes the imaging-based research presented orally at the 2018 International Conference of the International Mesothelioma Interest Group (iMig) in Ottawa, Ontario, Canada, held May 2-5, 2018. Presented topics included advances in the imaging of preclinical mesothelioma models to inform clinical therapeutic strategies, optimization of the time delay between contrast administration and image acquisition for maximized enhancement of mesothelioma tumor on CT, an investigation of image-based criteria for clinical tumor and nodal staging of mesothelioma by contrast-enhanced CT, an investigation of methods for the extraction of mesothelioma tumor volume from MRI and the association of volume with patient survival, the use of deep learning for mesothelioma tumor segmentation in CT, and an evaluation of CT-based radiomics for the prognosis of mesothelioma patient survival.

Statistics

Citations

Dimensions.ai Metrics
1 citation in Web of Science®
3 citations in Scopus®
Google Scholar™

Altmetrics

Downloads

0 downloads since deposited on 07 Feb 2020
0 downloads since 12 months

Additional indexing

Item Type:Journal Article, refereed, further contribution
Communities & Collections:04 Faculty of Medicine > University Hospital Zurich > Clinic for Thoracic Surgery
Dewey Decimal Classification:610 Medicine & health
Scopus Subject Areas:Health Sciences > Oncology
Health Sciences > Pulmonary and Respiratory Medicine
Life Sciences > Cancer Research
Language:English
Date:April 2019
Deposited On:07 Feb 2020 16:55
Last Modified:29 Jul 2020 13:59
Publisher:Elsevier
ISSN:0169-5002
OA Status:Closed
Publisher DOI:https://doi.org/10.1016/j.lungcan.2018.11.033
PubMed ID:30885330

Download

Closed Access: Download allowed only for UZH members