Imaging plays an important role in the detection, diagnosis, staging, response assessment, and surveillance of malignant pleural mesothelioma. The etiology, biology, and growth pattern of mesothelioma present unique challenges for each modality used to capture various aspects of this disease. Clinical implementation of imaging techniques and information derived from images continue to evolve based on active research in this field worldwide. This paper summarizes the imaging-based research presented orally at the 2016 International Conference of the International Mesothelioma Interest Group (iMig) in Birmingham, United Kingdom, held May 1–4, 2016. Presented topics included intraoperative near-infrared imaging of mesothelioma to aid the assessment of resection completeness, an evaluation of tumor enhancement improvement with increased time delay between contrast injection and image acquisition in standard clinical magnetic resonance imaging (MRI) scans, the potential of early contrast enhancement analysis to provide MRI with a role in mesothelioma detection, the differentiation of short- and long-term survivors based on MRI tumor volume and histogram analysis, the response-assessment potential of hemodynamic parameters derived from dynamic contrast-enhanced computed tomography (DCE-CT) scans, the correlation of CT-based tumor volume with the post-surgical tumor specimen weight, and consideration of the need to update the mesothelioma tumor response assessment paradigm.
Please cite this article as: E. Gudmundsson et al., Pleuroparenchymal fibroelastosis in idiopathic pulmonary fibrosis: Survival analysis using visual and computer-based computed tomography assessment, EClinicalMedicine (2021),
Tumor volume has been a topic of interest in the staging, prognostic evaluation, and treatment response assessment of malignant pleural mesothelioma (MPM). Deep convolutional neural networks (CNNs) were trained separately for the left and right hemithoraces on the task of differentiating between pleural thickening and normal thoracic tissue on computed tomography (CT) scans. A total of 4259 and 6192 axial sections containing segmented tumor were used to train the left-hemithorax CNN and the right-hemithorax CNN, respectively. Two distinct test sets of 131 sections from the CT scans of 43 patients were used to evaluate segmentation performance by calculating the Dice similarity coefficient (DSC) between deep CNN-generated tumor segmentations and reference tumor segmentations provided by a total of eight observers. Median DSC values ranged from 0.662 to 0.800 over the two test sets when comparing deep CNN-generated segmentations with observer reference segmentations. The deep CNN-based method achieved significantly higher DSC values for all three observers on the test set that allowed direct comparisons with a previously published automated segmentation method of MPM tumor on CT scans (p < 0.0005). A deep CNN was implemented for the automated segmentation of MPM tumor on CT scans, showing superior performance to a previously published method.
Tumor volume is a topic of interest for the prognostic assessment, treatment response evaluation, and staging of malignant pleural mesothelioma. Many mesothelioma patients present with, or develop, pleural fluid, which may complicate the segmentation of this disease. Deep convolutional neural networks (CNNs) of the two-dimensional U-Net architecture were trained for segmentation of tumor in the left and right hemithoraces, with the networks initialized through layers pretrained on ImageNet. Networks were trained on a dataset of 5230 axial sections from 154 CT scans of 126 mesothelioma patients. A test set of 94 CT sections from 34 patients, who all presented with both tumor and pleural effusion, in addition to a more general test set of 130 CT sections from 43 patients, were used to evaluate segmentation performance of the deep CNNs. The Dice similarity coefficient (DSC), average Hausdorff distance, and bias in predicted tumor area were calculated through comparisons with radiologist-provided tumor segmentations on the test sets. The present method achieved a median DSC of 0.690 on the tumor and effusion test set and achieved significantly higher performance on both test sets when compared with a previous deep learning-based segmentation method for mesothelioma.
Emphysema is one of the most common pulmonary comorbidities of idiopathic pulmonary fibrosis (IPF), presenting in about one-third of IPF patients [1]. The term combined pulmonary fibrosis and emphysema (CPFE) has been used to describe a potential phenotype characterised by the coexistence of upper lobe-predominant emphysema, lower lobe-predominant fibrosis and relative preservation of lung volumes (forced vital capacity; FVC) in the context of a disproportionately reduced gas transfer (diffusing capacity of the lung for carbon monoxide;
D
LCO
) [1–3]. With regard to patient survival, it remains unclear whether mortality in patients with CPFE reflects the cumulative effects of two disease processes (emphysema and fibrosis), or whether CPFE represents a distinct disease phenotype where outcome is worse than the sum of disease parts (emphysema and fibrosis).
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