Imaging in pleural mesothelioma: A review of the 14th International Conference of the International Mesothelioma Interest Group. (April 2019) - Armato, Samuel G.; Francis, Roslyn J.; Katz, Sharyn I.; Ak, Guntulu; Opitz, Isabelle; Gudmundsson, Eyjolfur; Blyth, Kevin G.; Gupta, Ashish
Highlights: Preclinical mesothelioma models are used to inform clinical therapeutic strategies. Contrast administration time delay impacts enhancement of mesothelioma tumor on CT. Segmented mesothelioma tumor volume from MRI is associated with patient survival. Deep learning shows promise for mesothelioma tumor segmentation in CT. CT-based radiomics is related to 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.