Imaging of Malignant Pleural Mesothelioma: Pearls and Pitfalls

Diffusion-weighted Imaging of the Chest: A Primer for Radiologists

Diffusion-weighted imaging (DWI) is a fundamental sequence not only in neuroimaging but also in oncologic imaging and has emerging applications for MRI evaluation of the chest. DWI can be used in clinical practice to enhance lesion conspicuity, tissue characterization, and treatment response. While the spatial resolution of DWI is in the order of millimeters, changes in diffusion can be measured on the micrometer scale. As such, DWI sequences can provide important functional information to MRI evaluation of the chest but require careful optimization of acquisition parameters, notably selection of b values, application of parallel imaging, fat saturation, and motion correction techniques. Along with assessment of morphologic and other functional features, evaluation of DWI signal attenuation and apparent diffusion coefficient maps can aid in tissue characterization. DWI is a noninvasive noncontrast acquisition with an inherent quantitative nature and excellent reproducibility. The outstanding contrast-to-noise ratio provided by DWI can be used to improve detection of pulmonary, mediastinal, and pleural lesions, to identify the benign nature of complex cysts, to characterize the solid portions of cystic lesions, and to classify chest lesions as benign or malignant. DWI has several advantages over fluorine 18 (18F)-fluorodeoxyglucose PET/CT in the assessment, TNM staging, and treatment monitoring of lung cancer and other thoracic neoplasms with conventional or more recently developed therapies. © RSNA, 2023 Quiz questions for this article are available in the supplemental material. Supplemental material and the slide presentation from the RSNA Annual Meeting are available for this article.

Pleural Neoplasms-What Could MRI Change?

The primary pleural neoplasms constitute around 10% of the pleural tumors. The currently recommended method for their imaging is CT which has been shown to have certain limitations. Strong development of the MRI within the last two decades has provided us with a number of sequences that could potentially be superior to CT when it comes to the pleural malignancies' detection and characterization. This literature review discusses the possible applications of the MRI as a diagnostic tool in patients with pleural neoplasms. Although selected MRI techniques have been shown to have a number of advantages over CT, further research is required in order to confirm the obtained results, broaden our knowledge on the topic, and pinpoint the sequences most optimal for pleural imaging, as well as the best methods for reading and analysis of the obtained data.

Mesotheliomas and Benign Mesothelial Tumors: Update on Pathologic and Imaging Findings

A diverse spectrum of benign entities and malignant neoplasms originate from the monotonous mesothelium that lines the serosal membranes of the pleural, pericardial, and peritoneal cavities. The mesothelium of myriad sites shows a common origin from the lateral plate mesoderm; primary mesothelial tumors thus demonstrate similar pathogenesis, imaging findings, and treatment options. Significant changes have been made in the 2021 World Health Organization (WHO) classification schemata of the pleural and pericardial tumors on the basis of recent advances in pathology and genetics. While malignant mesotheliomas are biologically aggressive malignancies that occur primarily in patients exposed to asbestos with attendant poor survival rates, well-differentiated papillary mesothelial tumors and adenomatoid tumors charter a benign clinical course with an excellent prognosis. Mesothelioma in situ is a newly characterized entity represented by recurrent unexplained pleural effusions without any identifiable mass at imaging or thoracoscopy. Immunohistochemical markers based on BAP1, MTAP, CDKN2A, and TRAF7 gene mutations help differentiate diffuse mesotheliomas from benign mesothelial proliferations and localized mesotheliomas. Cross-sectional imaging modalities, including US, CT, MRI, and fluorine 18-fluorodeoxyglucose (FDG) PET/CT, permit diagnosis and play a major role in staging and assessing surgical resectability. Imaging studies are invaluable in providing noninvasive and quantitative assessment of tumor response in patients with unresectable disease. Owing to significant overlap in patient characteristics and pathomorphology, accurate diagnosis based on advanced histopathology techniques and genetic abnormalities is imperative for optimal management and prognostication. While patients with nonepithelioid pleural mesotheliomas benefit from immunotherapy, novel targeted therapies for CDKN2A-, NF2-, and BAP1-altered mesotheliomas are under consideration. ^© RSNA, 2023 Quiz questions for this article are available through the Online Learning Center.

Differentiating malignant pleural mesothelioma and metastatic pleural disease based on a machine learning model with primary CT signs: A multicentre study

Rationale and Objectives

It is still a challenge to make confirming diagnosis of malignant pleural mesothelioma (MPM), especially differentiating from metastatic pleural disease (MPD). The aim of this study was to develop a model to distinguish MPM with MPD based on primary CT signs.

Materials and methods

We retrospectively recruited 150 MPM patients and 147 MPD patients from two centers and assigned them to training (115 MPM patients and 113 MPD patients) and testing (35 MPM patients and 34 MPD patients) cohorts. The images were analyzed for pleural thickening, hydrothorax, lymphadenopathy, thoracic volume and calcified pleural plaque (CPP). The selected clinical characteristics and primary CT signs comprised the model by multivariate logistic regression in the training cohort. Then the model was tested on the external testing cohort. ROC curve and F1 score were used to validate the capability of the model in both two cohorts.

Results

There were significant differences between two groups: (1) carcinoembryonic antigen (CEA); (2) nodular and mass pleural thickening; (3) the enhancement of pleura; (4) focal, diffuse and circumferential pleural thickening; (5) the thickest pleura; (6) thickening of diaphragmatic pleura; (7) multiple nodules and effusion of interlobar pleura; (8) hilar LN and ring enhancement of LN; (9) punctate and stipe CPP. The AUC and F1 score of the model were 0.970 and 0.857 in the training cohort, 0.955 and 0.818 in the testing cohort.

Conclusion

The model holds promise for use as a diagnostic tool to distinguish MPM from MPD.