Variability of tumor area measurements for response assessment in malignant pleural mesothelioma

Imaging in pleural mesothelioma: A review of the 15th International Conference of the International Mesothelioma Interest Group

Imaging of mesothelioma plays a role in all aspects of patient management, including disease detection, staging, evaluation of treatment options, response assessment, pre-surgical evaluation, and surveillance. Imaging in this disease impacts a wide range of disciplines throughout the healthcare enterprise. Researchers and clinician-scientists are developing state-of-the-art techniques to extract more of the information contained within these medical images and to utilize it for more sophisticated tasks; moreover, image-acquisition technology is advancing the inherent capabilities of these images. This paper summarizes the imaging-based topics presented orally at the 2021 International Conference of the International Mesothelioma Interest Group (iMig), which was held virtually from May 7-9, 2021. These topics include an update on the mesothelioma staging system, novel molecular targets to guide therapy in mesothelioma, special considerations and potential pitfalls in imaging mesothelioma in the immunotherapy setting, tumor measurement strategies and their correlation with patient survival, tumor volume measurement in MRI and CT, CT-based texture analysis for differentiation of histologic subtype, diffusion-weighted MRI for the assessment of biphasic mesothelioma, and the prognostic significance of skeletal muscle loss with chemotherapy.

New Updates of the Imaging Role in Diagnosis, Staging, and Response Treatment of Malignant Pleural Mesothelioma

Malignant pleural mesothelioma is a rare neoplasm with poor prognosis. CT is the first imaging technique used for diagnosis, staging, and assessment of therapy response. Although, CT has intrinsic limitations due to low soft tissue contrast and the current staging system as well as criteria for evaluating response, it does not consider the complex growth pattern of this tumor. Computer-based methods have proven their potentiality in diagnosis, staging, prognosis, and assessment of therapy response; moreover, computer-based methods can make feasible tasks like segmentation that would otherwise be impracticable. MRI, thanks to its high soft tissue contrast evaluation of contrast enhancement and through diffusion-weighted-images, could replace CT in many clinical settings.

A comparison between MRI and CT in the assessment of primary tumour volume in mesothelioma

INTRODUCTION

Primary tumour staging in Malignant Pleural Mesothelioma (MPM) using Computed Tomography (CT) imaging is confounded by perception errors reflecting low spatial resolution between tumour and adjacent structures. Augmentation using perfusion CT is constrained by radiation dosage. In this study, we evaluated an alternative tumour staging method using perfusion-tuned Magnetic Resonance Imaging (MRI).

METHODS

Consecutive patients with suspected MPM were recruited to a prospective observational study. All had MRI (T1-weighted, isotropic, contrast-enhanced 3-Tesla perfusion imaging) and CT (contrast-enhanced) pre-biopsy. Patients diagnosed with MPM underwent MRI and CT volumetry, with readers blinded to clinical data. MRI volumetry was semi-automated, using signal intensity limits from perfusion studies to grow tumour regions within a pleural volume. A similar CT method was not possible, therefore all visible tumour was manually segmented. MRI and CT volumes were compared (agreement, correlation, analysis time, reproducibility) and associations with survival examined using Cox regression.

RESULTS

58 patients were recruited and had MRI before biopsy. 31/58 were diagnosed with MPM and these scans were used for volumetry. Mean (SD) MRI and CT volumes were 370 cm3 and 302 cm3, respectively. MRI volumes were larger (average bias 61.9 cm3 (SD 116), 95 % limits (-165.5 - 289 cm3), moderately correlated with CT (r = 0.56, p = 0.002) and independently associated with survival (HR 4.03 (95 % CI 1.5-11.55), p = 0.006). CT volumes were not associated with survival, took longer to compute than MRI volumes (mean (SD) 151 (19) v 14 (2) minutes, p=<0.0001) and were less reproducible (inter-observer ICC 0.72 for CT, 0.96 for MRI).

CONCLUSIONS

MRI and CT generate different tumour volumes in MPM. In this study, MRI volumes were larger and were independently associated with survival. MRI volumetry was quicker and more reproducible than CT.

Radiomics in lung cancer for oncologists

Radiomics has revolutionized the world of medical imaging. The aim of this review is to guide oncologists in radiomics and its applications in diagnosis, prediction of response and damage, prediction of survival, and prognosis in lung cancer. In this review, we analyzed published literature on PubMed and MEDLINE with papers published in the last 10 years. We included papers in English language with information about radiomics features and diagnostic, predictive, and prognosis of radiomics in lung cancer. All citations were evaluated for relevant content and validation.

RELEVANCE FOR PATIENTS

The evolution of technology allows the development of computer algorithms that facilitate the diagnosis and evaluation of response after different oncological treatments and their non-invasive follow-up.

Deep learning-based segmentation of malignant pleural mesothelioma tumor on computed tomography scans: application to scans demonstrating pleural effusion

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.

Computed tomography features of local pleural recurrence in patients with malignant pleural mesothelioma treated with intensity-modulated pleural radiation therapy

OBJECTIVE

This study was conducted in order to describe the computed tomography (CT) features of local pleural recurrence in patients with malignant pleural mesothelioma undergoing intensity-modulated pleural radiation therapy (IMPRINT) as part of multimodality treatment.

METHODS

In this observational study, 58 patients treated with IMPRINT between September 21, 2004, and December 1, 2014 were included. Baseline and follow-up CT scans were qualitatively assessed. On follow-up scans, pleural thickening was categorized as unchanged, decreased, or new/increased. New/increased pleural abnormality was subcategorized as diffuse smooth pleural thickening, diffuse nodular pleural thickening, focal pleural nodule, or multiple pleural nodules. To identify features more frequently present at the time of local recurrence, follow-up scans with local recurrence were matched to four control scans; exact conditional logistic regression was performed.

RESULTS

Twenty-one (36%) patients had local pleural recurrence and 20 (34%) patients had nonpleural recurrence; 3 patients had both types of recurrence. The 1-year cumulative incidence rate of local recurrence was 27% (95% confidence interval 15, 39). On follow-up scans, three patterns of pleural abnormality were significantly associated with local recurrence: new/increased multiple pleural nodules (10 (48%) positive scans vs 0 control scans), new/increased diffuse nodular pleural thickening (7 (33%) positive scans vs 1 (1%) control scans), and new/increased focal pleural nodule (3 (14%) positive scans vs 1 (1%) control scan) (p < 0.001 for all).

CONCLUSIONS

Multiple new/increased pleural nodules are the feature most commonly present at local recurrence following IMPRINT; however, any pattern of increased nodular pleural thickening is suspicious.

KEY POINTS

• In patients with mesothelioma receiving intensity-modulated pleural radiation as part of multimodality therapy, increasing multiple pleural nodules is the computed tomography feature most commonly present at local recurrence. • In these patients, any CT pattern of increased nodular pleural thickening should be considered suspicious for local recurrence. • The most common sites of nonpleural recurrence were lung parenchyma, thoracic lymph nodes, and peritoneum.

Deep convolutional neural networks for the automated segmentation of malignant pleural mesothelioma on computed tomography scans

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.

Novel Quantitative Imaging for Predicting Response to Therapy: Techniques and Clinical Applications

The current standard of Response Evaluation Criteria in Solid Tumors (RECIST)-based tumor response evaluation is limited in its ability to accurately monitor treatment response. Radiomics, an approach involving computerized extraction of several quantitative imaging features, has shown promise in predicting as well as monitoring response to therapy. In this article, we provide a brief overview of radiomic approaches and the various analytical methods and techniques, specifically in the context of predicting and monitoring treatment response for non-small cell lung cancer (NSCLC). We briefly summarize some of the various types of radiomic features, including tumor shape and textural patterns, both within the tumor and within the adjacent tumor microenvironment. Additionally, we also discuss work in delta-radiomics or change in radiomic features (e.g., texture within the nodule) across longitudinally interspersed images in time for monitoring changes in therapy. We discuss the utility of these approaches for NSCLC, specifically the role of radiomics as a prognostic marker for treatment effectiveness and early therapy response, including chemoradiation, immunotherapy, and trimodality therapy.

Radiomics and its emerging role in lung cancer research, imaging biomarkers and clinical management: State of the art

With the development of functional imaging modalities we now have the ability to study the microenvironment of lung cancer and its genomic instability. Radiomics is defined as the use of automated or semi-automated post-processing and analysis of large amounts of quantitative imaging features that can be derived from medical images. The automated generation of these analytical features helps to quantify a number of variables in the imaging assessment of lung malignancy. These imaging features include: tumor spatial complexity, elucidation of the tumor genomic heterogeneity and composition, subregional identification in terms of tumor viability or aggressiveness, and response to chemotherapy and/or radiation. Therefore, a radiomic approach can help to reveal unique information about tumor behavior. Currently available radiomic features can be divided into four major classes: (a) morphological, (b) statistical, (c) regional, and (d) model-based. Each category yields quantitative parameters that reflect specific aspects of a tumor. The major challenge is to integrate radiomic data with clinical, pathological, and genomic information to decode the different types of tissue biology. There are many currently available radiomic studies on lung cancer for which there is a need to summarize the current state of the art.

Response evaluation in mesothelioma: Beyond RECIST

Malignant pleural mesothelioma (MPM) typically demonstrates a non-spherical growth pattern, so it is often difficult to accurately categorize change in tumour burden using size-based tumour response criteria (e.g., WHO (World Health Organisation), RECIST (Response Evaluation Criteria in Solid Tumours) and modified RECIST). Functional imaging techniques are applied to derive quantitative measurements of tumours, which reflect particular aspects of the tumour pathophysiology. By quantifying how these measurements change with treatment, it is possible to observe treatment effects. In this review, we survey the existing roles of CT and MRI for the management of MPM, including the currently applied size measurement criteria for the assessment of treatment response. New functional imaging techniques, such as positron emission tomography (PET), diffusion-weighted MRI (DWI) and dynamic contrast-enhanced MRI (DCE-MRI) that may potentially improve the assessment of treatment response will be highlighted and discussed.

Radiologic-pathologic correlation of mesothelioma tumor volume

OBJECTIVES

Tumor volume promises to become a more important factor in patient management. Mesothelioma, with its unique morphology and complex growth pattern, presents a challenging target for tumor volumetrics derived from computed tomography (CT) scans. This study evaluated the validity of image-based mesothelioma tumor volume against the physical volume of the tumor bulk captured by the images.

MATERIALS AND METHODS

Twenty-eight patients underwent CT scanning prior to pleurectomy/decortication with an intent to achieve a macroscopic complete resection. A radiologist manually outlined the tumor border in all CT sections in which tumor appeared in the pre-surgery scan. CT-based tumor volume was computed as the number of image pixels enclosed by all tumor outlines across all sections in the scan multiplied by the physical dimensions of the voxel of tissue captured by each image pixel. The gross tumor specimen volume was measured ex vivo through a water-displacement technique. Correlation between CT volume and pathology volume was calculated.

RESULTS

A correlation coefficient r-squared value of 0.66 was found between CT-based tumor volume and gross tumor specimen volume. Differences between the mean volume (either CT volume or pathology volume) across tumors of different T stages did not achieve statistical significance.

CONCLUSION

Despite a modest correlation between CT-based tumor volume and gross tumor specimen volume, image-based volumetry for mesothelioma is not straightforward-perhaps, in part, due to the challenges of distinguishing tumor borders from adjacent structures and perhaps, in part, due to a complex pathologic reference standard.