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

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.

Keywords: Clinical staging; Deep learning; Dynamic contrast-enhanced CT; Patient outcomes; Preclinical imaging; Radiomics; Tumor segmentation; Tumor volume.

Figure 1.

FDG-PET maximum intensity projection image of a MexTAg genetically modified mouse. The multiple small foci of FDG activity in the abdomen represent peritoneal mesothelioma. (Image courtesy of C. Robinson; acknowledgement to ACRF Cancer Imaging Facility, Perth, Australia.)

Figure 2.

Best-fit analysis of time-enhancement curves for MPM. At a time delay range of 230-300 s (double arrow), >95% of maximal tumor enhancement is achieved for all 10 patients.

Figure 3.

(a) Kaplan-Meier survival curves for types of pleural thickening. (b) Kaplan-Meier survival curves for lymph node metastasis.

Figure 4.

(a) Overall survival curves and (b) progression-free survival curves for patientsseparated based on the features with the largest concordance index in univariate Cox regression.

Figure 5.

DSC values for the deep CNN-based method and the 2011 Method when compared with observer reference segmentations (across all observers) on the test set of 63 axial CT sections. The line of equality is shown as a dashed line.

Figure 6.

Semi-automated, perfusion-tuned mesothelioma tumor segmentation at 3T contrast-enhanced MRI. A “contour mask” is generated throughout the image series (left panels). Tumor regions are grown within this mask volume (in red, middle panels) based on pre-defined signal intensity limits. Patient survival is shown dichotomized by MRI tumor volume at 300 cm³ and by tertile (right panels).