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

Convolutional Neural Networks for Segmentation of Pleural Mesothelioma: Analysis of Probability Map Thresholds (CALGB 30901, Alliance)

The purpose of this study was to evaluate the impact of probability map threshold on pleural mesothelioma (PM) tumor delineations generated using a convolutional neural network (CNN). One hundred eighty-six CT scans from 48 PM patients were segmented by a VGG16/U-Net CNN. A radiologist modified the contours generated at a 0.5 probability threshold. Percent difference of tumor volume and overlap using the Dice Similarity Coefficient (DSC) were compared between the reference standard provided by the radiologist and CNN outputs for thresholds ranging from 0.001 to 0.9. CNN-derived contours consistently yielded smaller tumor volumes than radiologist contours. Reducing the probability threshold from 0.5 to 0.01 decreased the absolute percent volume difference, on average, from 42.93% to 26.60%. Median and mean DSC ranged from 0.57 to 0.59, with a peak at a threshold of 0.2; no distinct threshold was found for percent volume difference. The CNN exhibited deficiencies with specific disease presentations, such as severe pleural effusion or disease in the pleural fissure. No single output threshold in the CNN probability maps was optimal for both tumor volume and DSC. This study emphasized the importance of considering both figures of merit when evaluating deep learning-based tumor segmentations across probability thresholds. This work underscores the need to simultaneously assess tumor volume and spatial overlap when evaluating CNN performance. While automated segmentations may yield comparable tumor volumes to that of the reference standard, the spatial region delineated by the CNN at a specific threshold is equally important.

Prognostic Value of Chest CT Volumetric Analysis in Patients with Malignant Pleural Mesothelioma

Background/Objectives: Malignant pleural mesothelioma (MPM) is a rare, aggressive cancer linked to asbestos exposure and with poor overall survival. In recent years, CT volumetric analysis has gained increasing interest as a more accurate method for assessing tumor burden. This study aims to evaluate the prognostic value of chest CT volumetric analysis in MPM, comparing tumor volume with tumor thickness measurements and survival outcomes. Methods: This is a retrospective, observational analysis of all patients undergoing diagnostic thoracoscopy between 2014 and 2021 at the University Hospital of Cattinara (Trieste, Italy). Inclusion criteria were as follows: age ≥ 18 years, histological diagnosis of MPM, and the availability of at least one contrast-enhanced chest CT scan at the time of diagnosis. For each patient, the tumor thickness was measured on the axial plane at three levels (upper, middle, and lower hemithorax). Tumor and effusion volumes were calculated with the RayStation® software version 11.7.174 (HealthMyne®, Madison, WI, USA). Results: A total of 81 patients were eligible for analysis. Maximum and mean tumor thickness were strongly associated with survival, with higher thicknesses correlating with an increased risk of death (adjusted hazard ratio per doubling (aHR) of 1.97 (95%CI: 1.40-2.77) and of 2.23 (95%CI: 1.56-3.20), p < 0.001)), respectively, while the effect of the tumor volume on survival was nevertheless significant but less impactful (aHR = 1.26 (1.10-1.45, p < 0.001)). The presence and volume of effusion did not correlate with survival (p = 0.48 and p = 0.64, respectively). Conclusions: This study supports the role of quantitative parameters for staging MPM, particularly given the frequent discrepancies between clinical and pathological staging when relying solely on qualitative measures.

An overview on multimodal imaging for the diagnostic workup of pleural mesothelioma

Pleural mesothelioma (PM) is an aggressive disease that has a strong causal relationship with asbestos exposure and represents a major challenge from both a diagnostic and therapeutic viewpoint. Despite recent improvements in patient care, PM typically carries a poor outcome, especially in advanced stages. Therefore, a timely and effective diagnosis taking advantage of currently available imaging techniques is essential to perform an accurate staging and dictate the most appropriate treatment strategy. Our aim is to provide a brief, but exhaustive and up-to-date overview of the role of multimodal medical imaging in the management of PM.

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.

Considerations for Imaging of Malignant Pleural Mesothelioma: A Consensus Statement from the International Mesothelioma Interest Group

Malignant pleural mesothelioma (MPM) is an aggressive primary malignancy of the pleura that presents unique radiologic challenges with regard to accurate and reproducible assessment of disease extent at staging and follow-up imaging. By optimizing and harmonizing technical approaches to imaging MPM, the best quality imaging can be achieved for individual patient care, clinical trials, and imaging research. This consensus statement represents agreement on harmonized, standard practices for routine multimodality imaging of MPM, including radiography, computed tomography, ¹⁸F-2-deoxy-D-glucose positron emission tomography, and magnetic resonance imaging, by an international panel of experts in the field of pleural imaging assembled by the International Mesothelioma Interest Group. In addition, modality-specific technical considerations and future directions are discussed. A bulleted summary of all technical recommendations is provided.

Feature selection methods and predictive models in CT lung cancer radiomics

Radiomics is a technique that extracts quantitative features from medical images using data-characterization algorithms. Radiomic features can be used to identify tissue characteristics and radiologic phenotyping that is not observable by clinicians. A typical workflow for a radiomics study includes cohort selection, radiomic feature extraction, feature and predictive model selection, and model training and validation. While there has been increasing attention given to radiomic feature extraction, standardization, and reproducibility, currently, there is a lack of rigorous evaluation of feature selection methods and predictive models. Herein, we review the published radiomics investigations in CT lung cancer and provide an overview of the commonly used radiomic feature selection methods and predictive models. We also compare limitations of various methods in clinical applications and present sources of uncertainty associated with those methods. This review is expected to help raise awareness of the impact of radiomic feature and model selection methods on the integrity of radiomics studies.

New Era for Malignant Pleural Mesothelioma: Updates on Therapeutic Options

Malignant pleural mesothelioma (MPM) is a rare malignancy with few treatment options. Recent advances have led to US Food and Drug Administration approvals and changes in the standard of care with a novel biomedical device approved for use with platinum-pemetrexed, and also for immunotherapy agents to be included as a frontline treatment option in unresectable disease. Although predictive biomarkers for systemic therapy are not currently in use in clinical practice, it is essential to correctly identify the MPM histology to determine an optimal treatment plan. Patients with nonepithelioid MPM may have a greater magnitude of benefit to dual immunotherapy checkpoint inhibitors and this regimen should be preferred in the frontline setting for these patients. However, all patients with MPM can derive benefit from immunotherapy treatments, and these agents should ultimately be used at some point during their treatment journey. There are ongoing studies in the frontline unresectable setting that may further define the frontline therapy space, but a critical area of research will need to focus on the immunotherapy refractory population. This review article will describe the new developments in the areas of biology with genomics and chromothripsis, and also focus on updates in treatment strategies in radiology, surgery, radiation, and medical oncology with cellular therapies. These recent innovations are generating momentum to find better therapies for this disease.

Development of unenhanced CT-based imaging signature for BAP1 mutation status prediction in malignant pleural mesothelioma: Consideration of 2D and 3D segmentation

OBJECTIVES

We aimed to explore the feasibility of 2D and 3D radiomics signature based on the unenhanced computed tomography (CT) images to predict BRCA1-associated protein 1 (BAP1) gene mutation status for malignant pleural mesothelioma (MPM) patients.

MATERIALS AND METHODS

74 patients with MPM were retrospectively enrolled (22 mutant BAP1, 52 wild-type BAP1 demonstrated by Sanger sequencing). The radiomic features were extracted respectively from the 2D and 3D segmentation of unenhanced pre-treatment CT images, and the dataset was randomly divided into training (n = 51) and test (n = 23) sets for radiomics model development and internal validation. The synthetic minority over-sampling technique (SMOTE) was used for data balancing in the training set. 2D or 3D features were sequentially selected by ICC > 0.8, correlation analysis (cut-value 0.7), univariate analysis or univariate logistic regression (LR), which were involved into multivariate LR for LR model construction. Following the comparison of the 2D and 3D models by the ROC analysis and Delong test for AUC, the calibration and clinical utility of 2D and 3D models were evaluated.

RESULTS

3D radiomic features showed better ICCs compared with 2D in both intra- (P < 0.001) and inter-observer (P <  0.001) analysis. 3D radiomic model based on selected features developed from a balanced training dataset presented a favorable predictive performance with AUC of 0.786 and 0.768 in the training and test sets, respectively. The predictive performance of 3D model was superior to 2D model (1 feature) both in the training (AUC 0.786 vs. 0.683, P = 0.036) and the test (AUC 0.768 vs.0.652, P = 0.441) set. The calibration curve and decision curves also indicate a better BAP1 prediction performance and clinical benefit for 3D model than that of 2D model.

CONCLUSION

The developed unenhanced CT-based 3D radiomics signature is potential as a noninvasive marker for predicting BAP1 mutation status.

Preoperative Identification of Benefit from Surgery for Malignant Pleural Mesothelioma

In the absence of standardized treatment algorithms for patients with malignant pleural mesothelioma, one of the main difficulties remains patient allocation to therapies with potential benefit. This article discusses clinical, radiologic, pathologic, and molecular prognostic factors as well as genetic background leading to preoperative identification of benefit from surgery, which have been investigated over the past years to simplify and at the same time specify patient selection for surgical treatment.

Impact of CT convolution kernel on robustness of radiomic features for different lung diseases and tissue types

OBJECTIVES

In this study, we aimed to assess the impact of different CT reconstruction kernels on the stability of radiomic features and the transferability between different diseases and tissue types. Three lung diseases were evaluated, i.e. non-small cell lung cancer (NSCLC), malignant pleural mesothelioma (MPM) and interstitial lung disease related to systemic sclerosis (SSc-ILD) as well as four different tissue types, i.e. primary tumor, largest involved lymph node ipsilateral and contralateral lung.

METHODS

Pre-treatment non-contrast enhanced CT scans from 23 NSCLC, 10 MPM and 12 SSc-ILD patients were collected retrospectively. For each patient, CT scans were reconstructed using smooth and sharp kernel in filtered back projection. The regions of interest (ROIs) were contoured on the smooth kernel-based CT and transferred to the sharp kernel-based CT. The voxels were resized to the largest voxel dimension of each cohort. In total, 1386 features were analyzed. Feature stability was assessed using the intraclass correlation coefficient. Features above the stability threshold >0.9 were considered stable.

RESULTS

We observed a strong impact of the reconstruction method on stability of the features (at maximum 26% of the 1386 features were stable). Intensity features were the most stable followed by texture and wavelet features. The wavelet features showed a positive correlation between percentage of stable features and size of the ROI (R2 = 0.79, p = 0.005). Lymph node radiomics showed poorest stability (<10%) and lung radiomics the largest stability (26%). Robustness analysis done on the contralateral lung could to a large extent be transferred to the ipsilateral lung, and the overlap of stable lung features between different lung diseases was more than 50%. However, results of robustness studies cannot be transferred between tissue types, which was investigated in NSCLC and MPM patients; the overlap of stable features for lymph node and lung, as well as for primary tumor and lymph node was very small in both disease types.

CONCLUSION

The robustness of radiomic features is strongly affected by different reconstruction kernels. The effect is largely influenced by the tissue type and less by the disease type.

ADVANCES IN KNOWLEDGE

The study presents to our knowledge the most complete analysis on the impact of convolution kernel on the robustness of CT-based radiomics for four relevant tissue types in three different lung diseases. .

Postoperative survival results of patients with stage I-II malignant pleural mesothelioma in an endemic area

Introduction: The accepted treatment option for malignant pleural mesothelioma (MPM) is multimodality treatment including surgery, chemotherapy and radiotherapy. In this study, we aimed to evaluate the results of patients who underwent surgical resection for multimodality treatment due to MPM at our clinic between July 2015 and October 2019. Method: The results of 13 patients who underwent surgical treatment for MPM in our clinic were evaluated retrospectively. Patients' demographic structure, regions where they live, symptom presentation, disease localization, biopsy diagnosis, type of surgical treatment, choice of neoadjuvant or adjuvant chemotherapy, postoperative complications and survival outcomes were evaluated. Results: The mean survival time was 19.6 months. Six patients were still under follow-up. One patient whose postoperative pathology was reported as mixed type had the worst survival with 13 months and the best survival was 32 months in the patient who underwent postoperative hyperthermic chemotherapy with pleural decortication. Four patients died due to local recurrence and general condition disorder, and two patients died after peritonitis carcinomatosis and ascites. Conclusion: Epitheloid type multimodality treatment and intrapleural hyperthermic chemotherapy may be a good choice in patients with the stage I and II malignant mesothelioma without surgical comorbidity

Diagnosis of Mesothelioma

Mesothelioma is a rare neoplasm that arises from mesothelial cells lining body cavities including the pleura, pericardium, peritoneum, and tunica vaginalis. Most malignant mesotheliomas occur in the chest and are frequently associated with a history of asbestos exposure. The diagnosis of malignant mesothelioma is challenging and fraught with pitfalls, particularly in small biopsies. This article highlights what the pathologist needs to know regarding the clinical and radiographic presentation of mesothelioma, histologic features including subtypes and variants, and recent advances in immunohistochemical markers and molecular testing.