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


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Armato S. G. , Francis R. J. , Katz S. I. , AK G., Opitz I., Gudmundsson E., ...More

LUNG CANCER, vol.130, pp.108-114, 2019 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Review
  • Volume: 130
  • Publication Date: 2019
  • Doi Number: 10.1016/j.lungcan.2018.11.033
  • Journal Name: LUNG CANCER
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.108-114
  • Keywords: Preclinical imaging, Dynamic contrast-enhanced CT, Clinical staging, Tumor volume, Tumor segmentation, Deep learning, Radiomics, Patient outcomes, STAGING PROJECT PROPOSALS, FORTHCOMING 8TH EDITION, COMPUTED-TOMOGRAPHY, IASLC MESOTHELIOMA, TNM CLASSIFICATION, CT, CANCER, DESCRIPTORS, DISEASE, SEGMENTATION
  • Eskisehir Osmangazi University Affiliated: Yes

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.