Computerized segmentation and measurement of malignant pleural mesothelioma

The Convergence of Radiology and Genomics: Advancing Breast Cancer Diagnosis with Radiogenomics

Despite significant progress in the prevention, screening, diagnosis, prognosis, and therapy of breast cancer (BC), it remains a highly prevalent and life-threatening disease affecting millions worldwide. Molecular subtyping of BC is crucial for predictive and prognostic purposes due to the diverse clinical behaviors observed across various types. The molecular heterogeneity of BC poses uncertainties in its impact on diagnosis, prognosis, and treatment. Numerous studies have highlighted genetic and environmental differences between patients from different geographic regions, emphasizing the need for localized research. International studies have revealed that patients with African heritage are often diagnosed at a more advanced stage and exhibit poorer responses to treatment and lower survival rates. Despite these global findings, there is a dearth of in-depth studies focusing on communities in the African region. Early diagnosis and timely treatment are paramount to improving survival rates. In this context, radiogenomics emerges as a promising field within precision medicine. By associating genetic patterns with image attributes or features, radiogenomics has the potential to significantly improve early detection, prognosis, and diagnosis. It can provide valuable insights into potential treatment options and predict the likelihood of survival, progression, and relapse. Radiogenomics allows for visual features and genetic marker linkage that promises to eliminate the need for biopsy and sequencing. The application of radiogenomics not only contributes to advancing precision oncology and individualized patient treatment but also streamlines clinical workflows. This review aims to delve into the theoretical underpinnings of radiogenomics and explore its practical applications in the diagnosis, management, and treatment of BC and to put radiogenomics on a path towards fully integrated diagnostics.

Deep learning for diagnosis of malign pleural effusion on computed tomography images

BACKGROUND

The pleura is a serous membrane that surrounds the lungs. The visceral surface secretes fluid into the serous cavity and the parietal surface ensures a regular absorption of this fluid. If this balance is disturbed, fluid accumulation occurs in the pleural space called "Pleural Effusion". Today, accurate diagnosis of pleural diseases is becoming more critical, as advances in treatment protocols have contributed positively to prognosis. Our aim is to perform computer-aided numerical analysis of Computed Tomography (CT) images from patients showing pleural effusion images on CT and to examine the prediction of malignant/benign distinction using deep learning by comparing with the cytology results.

METHODS

The authors classified 408 CT images from 64 patients whose etiology of pleural effusion was investigated using the deep learning method. 378 of the images were used for the training of the system; 15 malignant and 15 benign CT images, which were not included in the training group, were used as the test.

RESULTS

Among the 30 test images evaluated in the system; 14 of 15 malignant patients and 13 of 15 benign patients were estimated with correct diagnosis (PPD: 93.3%, NPD: 86.67%, Sensitivity: 87.5%, Specificity: 92.86%).

CONCLUSION

Advances in computer-aided diagnostic analysis of CT images and obtaining a pre-diagnosis of pleural fluid may reduce the need for interventional procedures by guiding physicians about which patients may have malignancies. Thus, it is cost and time-saving in patient management, allowing earlier diagnosis and treatment.

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.

Fully automated volumetric measurement of malignant pleural mesothelioma by deep learning AI: validation and comparison with modified RECIST response criteria

BACKGROUND

In malignant pleural mesothelioma (MPM), complex tumour morphology results in inconsistent radiological response assessment. Promising volumetric methods require automation to be practical. We developed a fully automated Convolutional Neural Network (CNN) for this purpose, performed blinded validation and compared CNN and human response classification and survival prediction in patients treated with chemotherapy.

METHODS

In a multicentre retrospective cohort study; 183 CT datasets were split into training and internal validation (123 datasets (80 fully annotated); 108 patients; 1 centre) and external validation (60 datasets (all fully annotated); 30 patients; 3 centres). Detailed manual annotations were used to train the CNN, which used two-dimensional U-Net architecture. CNN performance was evaluated using correlation, Bland-Altman and Dice agreement. Volumetric response/progression were defined as ≤30%/≥20% change and compared with modified Response Evaluation Criteria In Solid Tumours (mRECIST) by Cohen's kappa. Survival was assessed using Kaplan-Meier methodology.

RESULTS

Human and artificial intelligence (AI) volumes were strongly correlated (validation set r=0.851, p<0.0001). Agreement was strong (validation set mean bias +31 cm3 (p=0.182), 95% limits 345 to +407 cm3). Infrequent AI segmentation errors (4/60 validation cases) were associated with fissural tumour, contralateral pleural thickening and adjacent atelectasis. Human and AI volumetric responses agreed in 20/30 (67%) validation cases κ=0.439 (0.178 to 0.700). AI and mRECIST agreed in 16/30 (55%) validation cases κ=0.284 (0.026 to 0.543). Higher baseline tumour volume was associated with shorter survival.

CONCLUSION

We have developed and validated the first fully automated CNN for volumetric MPM segmentation. CNN performance may be further improved by enriching future training sets with morphologically challenging features. Volumetric response thresholds require further calibration in future studies.

Computed Tomography and Spirometry Can Predict Unresectability in Malignant Pleural Mesothelioma

Preoperative identification of unresectable pleural mesothelioma could spare unnecessary surgical intervention and accelerate the initiation of medical treatments. The aim of this study is to determine predictors of unresectability, testing our impression that the contraction of the ipsilateral hemithorax is often associated with exploratory thoracotomy. Between 1994 and 2020, 291 patients undergoing intended macroscopic complete resection for mesothelioma after chemotherapy were retrospectively investigated. Eligible patients (n = 58) presented a preoperative 3 mm slice-thickness chest computed tomography without pleural effusion or hydropneumothorax. Lung volumes (segmented using a semi-automated method), modified-Response Evaluation Criteria in Solid Tumors (RECIST) measurements, and spirometries were collected after chemotherapy. Multivariable analysis was performed to determine the predictors of unresectability. An unresectable disease was found at the time of operation in 25.9% cases. By multivariable analysis, the total lung capacity (p = 0.03) and the disease burden (p = 0.02) were found to be predictors of unresectability; cut-off values were <77.5% and >120.5 mm, respectively. Lung volumes were not confirmed to be associated with unresectability at multivariable analysis, probably due to the correlation with the disease burden (p < 0.001; r = −0.4). Our study suggests that disease burden and total lung capacity could predict MPM unresectability, helping surgeons in recommending surgery or not in a multimodality setting.

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.

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.

The Era of Radiogenomics in Precision Medicine: An Emerging Approach to Support Diagnosis, Treatment Decisions, and Prognostication in Oncology

With the rapid development of new technologies, including artificial intelligence and genome sequencing, radiogenomics has emerged as a state-of-the-art science in the field of individualized medicine. Radiogenomics combines a large volume of quantitative data extracted from medical images with individual genomic phenotypes and constructs a prediction model through deep learning to stratify patients, guide therapeutic strategies, and evaluate clinical outcomes. Recent studies of various types of tumors demonstrate the predictive value of radiogenomics. And some of the issues in the radiogenomic analysis and the solutions from prior works are presented. Although the workflow criteria and international agreed guidelines for statistical methods need to be confirmed, radiogenomics represents a repeatable and cost-effective approach for the detection of continuous changes and is a promising surrogate for invasive interventions. Therefore, radiogenomics could facilitate computer-aided diagnosis, treatment, and prediction of the prognosis in patients with tumors in the routine clinical setting. Here, we summarize the integrated process of radiogenomics and introduce the crucial strategies and statistical algorithms involved in current studies.

Delayed-Phase Enhancement for Evaluation of Malignant Pleural Mesothelioma on Computed Tomography: A Prospective Cohort Study

BACKGROUND

Radiologic assessment of malignant pleural mesothelioma (MPM) on computed tomography (CT) imaging can be limited by similar attenuations of MPM and adjacent tissues. This can result in inaccuracies in defining the presence and extent of pleural tumor burden. We hypothesized that increasing the time delay for pleural enhancement will optimize discrimination between MPM and noncancerous tissues on CT. Here we conduct a prospective observational study to determine the optimal time delay for imaging MPM on CT.

PATIENTS AND METHODS

Adult MPM patients (n = 15) were enrolled in this prospective exploratory imaging trial. Patients with < 1 cm MPM thickness, prior pleurectomy, pleurodesis, pleural radiotherapy, or antiangiogenic therapy were excluded. All patients underwent a dynamically-enhanced CT with multiple time delays (0 - 10 minutes) after intravenous contrast administration. Tumor tissue attenuation was measured at each phase of enhancement. A qualitative assessment of tumor enhancement kinetics was also performed. The optimal phase of enhancement based on qualitative lesion conspicuity and quantitative tumor enhancement was then compared.

RESULTS

MPM tumor enhancement was quantitatively and qualitatively increased at time delays beyond the conventional time delay for thoracic CT imaging (40-60 seconds). Patient tumor enhancement kinetics, displayed as the fraction of maximal tumor tissue attenuation as a function of time, revealed an optimal time delay of 230 to 300 seconds after intravenous contrast administration. There was an association between degree of tumor enhancement and subjective lesion conspicuity.

CONCLUSION

Optimal MPM contrast enhancement occurs at a later phase than typically acquired with conventional thoracic CT imaging.

A Phase II Study of Pazopanib in Patients with Malignant Pleural Mesothelioma: NCCTG N0623 (Alliance)

PURPOSE

Preclinical and clinical data have shown promise in using antiangiogenic agents to treat malignant pleural mesothelioma (MPM). We conducted this phase II study to evaluate the efficacy and toxicity of single-agent pazopanib in patients with MPM.

MATERIALS AND METHODS

Patients with MPM who had received 0-1 prior chemotherapy regimens were eligible to receive pazopanib at a dose of 800 mg daily. The primary endpoint was progression-free survival rate at 6 months (PFS6), with a preplanned interim analysis for futility. Secondary endpoints included overall survival (OS), PFS, adverse events assessment and clinical benefit (complete response, partial response [PR], and stable disease [SD]).

RESULTS

Thirty-four evaluable patients were enrolled, with a median age of 73 years (49-84). The trial was closed early because of lack of efficacy at the preplanned interim analysis. Only 8 patients (28.6%; 95% confidence interval [CI], 13.2-48.7%) in the first 28 evaluable were progression-free at 6 months. PFS6 was 32.4% (95% CI, 17.4-50.5). There were 2 PR (5.9%) and 16 SD (47.1%). The overall median PFS and OS were 4.2 months (95% CI, 2.0-6.0) and 11.5 months (95% CI: 5.3-18.2), respectively. The median PFS and OS for the previously untreated patients was 5.4 months (95% CI, 2.7-8.5) and 16.6 months (95% CI, 6.6-30.6), respectively; and 2.0 months (95% CI, 1.3-4.2) and 5.0 months (95% CI: 3.0-11.9), respectively, for the previously treated patients. Grade 3 or higher adverse events were observed in 23 patients (67.6%).

CONCLUSION

Single-agent pazopanib was poorly tolerated in patients with MPM. The primary endpoint of PFS6 was not achieved in the current study. ClinicalTrials.gov identification number. NCT00459862.

IMPLICATIONS FOR PRACTICE

Single-agent pazopanib did not meet its endpoint in this phase II trial in malignant mesothelioma. Pazopanib is well tolerated in mesothelioma patients with a manageable toxicity profile. There is a need to better identify signals of angiogenesis that can be targeted in mesothelioma. Encouraging findings in frontline treatment warrant further investigations in combination with chemotherapy or immunotherapy.

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.

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

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.

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.

Progress in the Management of Malignant Pleural Mesothelioma in 2017

Malignant pleural mesothelioma (MPM) is an uncommon, almost universally fatal, asbestos-induced malignancy. New and effective strategies for diagnosis, prognostication, and treatment are urgently needed. Herein we review the advances in MPM achieved in 2017. Whereas recent epidemiological data demonstrated that the incidence of MPM-related death continued to increase in United States between 2009 and 2015, new insight into the molecular pathogenesis and the immunological tumor microenvironment of MPM, for example, regarding the role of BRCA1 associated protein 1 and the expression programmed death receptor ligand 1, are highlighting new potential therapeutic strategies. Furthermore, there continues to be an ever-expanding number of clinical studies investigating systemic therapies for MPM. These trials are primarily focused on immunotherapy using immune checkpoint inhibitors alone or in combination with other immunotherapies and nonimmunotherapies. In addition, other promising targeted therapies, including pegylated adenosine deiminase (ADI-PEG20), which focuses on argininosuccinate synthase 1-deficient tumors, and tazemetostat, an enhancer of zeste 2 polycomb repressive complex 2 subunit inhibitor of BRCA1 associated protein 1 gene (BAP1)-deficient tumors, are currently being explored.

Treatment of Malignant Pleural Mesothelioma: American Society of Clinical Oncology Clinical Practice Guideline

Purpose To provide evidence-based recommendations to practicing physicians and others on the management of malignant pleural mesothelioma. Methods ASCO convened an Expert Panel of medical oncology, thoracic surgery, radiation oncology, pulmonary, pathology, imaging, and advocacy experts to conduct a literature search, which included systematic reviews, meta-analyses, randomized controlled trials, and prospective and retrospective comparative observational studies published from 1990 through 2017. Outcomes of interest included survival, disease-free or recurrence-free survival, and quality of life. Expert Panel members used available evidence and informal consensus to develop evidence-based guideline recommendations. Results The literature search identified 222 relevant studies to inform the evidence base for this guideline. Recommendations Evidence-based recommendations were developed for diagnosis, staging, chemotherapy, surgical cytoreduction, radiation therapy, and multimodality therapy in patients with malignant pleural mesothelioma. Additional information is available at www.asco.org/thoracic-cancer-guidelines and www.asco.org/guidelineswiki .

Radiomics for Response and Outcome Assessment for Non-Small Cell Lung Cancer

Routine follow-up visits and radiographic imaging are required for outcome evaluation and tumor recurrence monitoring. Yet more personalized surveillance is required in order to sufficiently address the nature of heterogeneity in nonsmall cell lung cancer and possible recurrences upon completion of treatment. Radiomics, an emerging noninvasive technology using medical imaging analysis and data mining methodology, has been adopted to the area of cancer diagnostics in recent years. Its potential application in response assessment for cancer treatment has also drawn considerable attention. Radiomics seeks to extract a large amount of valuable information from patients' medical images (both pretreatment and follow-up images) and quantitatively correlate image features with diagnostic and therapeutic outcomes. Radiomics relies on computers to identify and analyze vast amounts of quantitative image features that were previously overlooked, unmanageable, or failed to be identified (and recorded) by human eyes. The research area has been focusing on the predictive accuracy of pretreatment features for outcome and response and the early discovery of signs of tumor response, recurrence, distant metastasis, radiation-induced lung injury, death, and other outcomes, respectively. This review summarized the application of radiomics in response assessments in radiotherapy and chemotherapy for non-small cell lung cancer, including image acquisition/reconstruction, region of interest definition/segmentation, feature extraction, and feature selection and classification. The literature search for references of this article includes PubMed peer-reviewed publications over the last 10 years on the topics of radiomics, textural features, radiotherapy, chemotherapy, lung cancer, and response assessment. Summary tables of radiomics in response assessment and treatment outcome prediction in radiation oncology have been developed based on the comprehensive review of the literature.

Volumetric assessment in malignant pleural mesothelioma

Malignant pleural mesothelioma (MPM) is a highly malignant pleural neoplasm with a dismal prognosis. Multimodality approach including surgery and chemotherapy are utilized to treat patients with resectable disease. Clinical staging allows for selection of patients for treatment strategies, but has not been found to be prognostic and is plagued by high interobserver variability. Tumor volume measurement on cross-sectional imaging has emerged as a potential quantitative tool with prognostic significance. This review focuses on volumetric assessment from cross-sectional imaging (CT, MRI, ¹⁸F-FDG PET/CT) and the potential prognostic benefit and applications.

Computer-aided volumetric assessment of malignant pleural mesothelioma on CT using a random walk-based method

OBJECTIVE

The aim of this study is to assess the performance of a computer-aided semi-automated algorithm we have adapted for the purpose of segmenting malignant pleural mesothelioma (MPM) on CT.

METHODS

Forty-five CT scans were collected from 15 patients (M:F [Formula: see text] 10:5, mean age 62.8 years) in a multi-centre clinical drug trial. A computer-aided random walk-based algorithm was applied to segment the tumour; the results were then compared to radiologist-drawn contours and correlated with measurements made using the MPM-adapted Response Evaluation Criteria in Solid Tumour (modified RECIST).

RESULTS

A mean accuracy (Sørensen-Dice index) of 0.825 (95% CI [0.758, 0.892]) was achieved. Compared to a median measurement time of 68.1 min (range [40.2, 102.4]) for manual delineation, the median running time of our algorithm was 23.1 min (range [10.9, 37.0]). A linear correlation (Pearson's correlation coefficient: 0.6392, [Formula: see text]) was established between the changes in modified RECIST and computed tumour volume.

CONCLUSION

Volumetric tumour segmentation offers a potential solution to the challenges in quantifying MPM. Computer-assisted methods such as the one presented in this study facilitate this in an accurate and time-efficient manner and provide additional morphological information about the tumour's evolution over time.

Imaging in pleural mesothelioma: A review of the 13th International Conference of the International Mesothelioma Interest Group

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 post-surgical tumor specimen weight, and consideration of the need to update the mesothelioma tumor response assessment paradigm.

A Multicenter Study of Volumetric Computed Tomography for Staging Malignant Pleural Mesothelioma

BACKGROUND

Standard imaging modalities are inaccurate in staging malignant pleural mesothelioma (MPM). Single-institution studies suggest that volumetric computed tomography (CT) is more accurate but labor intensive. We established a multicenter network to test interobserver variability, accuracy (relative to pathologic stage), and the prognostic significance of semiautomated volumetric CT.

METHODS

Six institutions electronically submitted to an established multicenter database clinical and pathologic data for patients with MPM who had operations. Institutional radiologists reviewed preoperative CT scans for quality and then submitted by electronic network (AG Mednet, www.agmednet.com) to the biostatistical center. Two reference radiologists blinded to clinical data performed semiautomated tumor volume calculations using Vitrea Enterprise 6.0 software (Vital Images, Minnetonka, MN) and then submitted readings to the biostatistical center. Study end points included feasibility of the network, interobserver variability for volumetric CT, correlation of tumor volume to pTN stages, and overall survival (OS).

RESULTS

Of 164 patients, the CT scans for 129 were analyzable and read by reference radiologists. Most tumors were less than 500 cm(3). A small bias was observed between readers because one provided consistently larger measurements than the other (mean difference, 47.9; p = .0027), but for 80%, the absolute difference was 200 cm(3) or less. Spearman correlation between readers was 0.822. Volume correlated with pTN stages and OS, best defined by three groups with average volumes of 91.2, 245.3, and 511.3 cm(3) associated with median OS of 37, 18, and 8 months, respectively.

CONCLUSIONS

For the first time, a multicenter network was established and initial correlations of tumor volume with pTN stages and OS are shown. A larger multicenter international study is planned to confirm the results and refine correlations.

North American Multicenter Volumetric CT Study for Clinical Staging of Malignant Pleural Mesothelioma: Feasibility and Logistics of Setting Up a Quantitative Imaging Study

BACKGROUND

Clinical tumor (T), node, and metastasis staging is based on a qualitative assessment of features defining T descriptors and has been found to be suboptimal for predicting the prognosis of patients with malignant pleural mesothelioma (MPM). Previous work suggests that volumetric computed tomography (VolCT) is prognostic and, if found practical and reproducible, could improve clinical MPM classification.

METHODS

Six North American institutions electronically submitted clinical, pathologic, and imaging data on patients with stages I to IV MPM to an established multicenter database and biostatistical center. Two reference radiologists blinded to clinical data independently reviewed the scans; calculated clinical T, node, and metastasis stage by standard criteria; performed semiautomated tumor volume calculations using commercially available software; and submitted the findings to the biostatistical center. Study end points included the feasibility of a multi-institutional VolCT network, concordance of independent VolCT assessments, and association of VolCT with pathological T classification.

RESULTS

Of 164 submitted cases, 129 were evaluated by both reference radiologists. Discordant clinical staging of most cases confirmed the inadequacy of current criteria. The overall correlation between VolCT estimates was good (Spearman correlation 0.822), but some were significantly discordant. Root cause analysis of the most discordant estimates identified four common sources of variability. Despite these limitations, median tumor volume estimates were similar within subgroups of cases representing each pathological T descriptor and increased monotonically for each reference radiologist with increasing pathological T status.

CONCLUSIONS

The good correlation between VolCT estimates obtained for most cases reviewed by two independent radiologists and qualitative association of VolCT with pathological T status combine to encourage further study. The identified sources of user error will inform design of a follow-up prospective trial to more formally assess interobserver variability of VolCT and its potential contribution to clinical MPM staging.

Lung texture in serial thoracic computed tomography scans: correlation of radiomics-based features with radiation therapy dose and radiation pneumonitis development

PURPOSE

To assess the relationship between radiation dose and change in a set of mathematical intensity- and texture-based features and to determine the ability of texture analysis to identify patients who develop radiation pneumonitis (RP).

METHODS AND MATERIALS

A total of 106 patients who received radiation therapy (RT) for esophageal cancer were retrospectively identified under institutional review board approval. For each patient, diagnostic computed tomography (CT) scans were acquired before (0-168 days) and after (5-120 days) RT, and a treatment planning CT scan with an associated dose map was obtained. 32- × 32-pixel regions of interest (ROIs) were randomly identified in the lungs of each pre-RT scan. ROIs were subsequently mapped to the post-RT scan and the planning scan dose map by using deformable image registration. The changes in 20 feature values (ΔFV) between pre- and post-RT scan ROIs were calculated. Regression modeling and analysis of variance were used to test the relationships between ΔFV, mean ROI dose, and development of grade ≥2 RP. Area under the receiver operating characteristic curve (AUC) was calculated to determine each feature's ability to distinguish between patients with and those without RP. A classifier was constructed to determine whether 2- or 3-feature combinations could improve RP distinction.

RESULTS

For all 20 features, a significant ΔFV was observed with increasing radiation dose. Twelve features changed significantly for patients with RP. Individual texture features could discriminate between patients with and those without RP with moderate performance (AUCs from 0.49 to 0.78). Using multiple features in a classifier, AUC increased significantly (0.59-0.84).

CONCLUSIONS

A relationship between dose and change in a set of image-based features was observed. For 12 features, ΔFV was significantly related to RP development. This study demonstrated the ability of radiomics to provide a quantitative, individualized measurement of patient lung tissue reaction to RT and assess RP development.

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.

Three-dimensional stereoscopic volume rendering of malignant pleural mesothelioma

Our objective was to investigate the application of three-dimensional (3D) stereoscopic volume rendering with perceptual colorization on preoperative imaging for malignant pleural mesothelioma. At present, we have prospectively enrolled 6 patients being considered for resection of malignant pleural mesothelioma that have undergone a multidetector-row computed tomography (CT) scan of the chest. The CT data sets were volume rendered without preprocessing. The resultant 3D rendering was displayed stereoscopically and used to provide information regarding tumor extent, morphology, and anatomic involvement. To demonstrate this technique, this information was compared with the corresponding two-dimensional CT grayscale axial images from two of these patients. Three-dimensional stereoscopic reconstructions of the CT data sets provided detailed information regarding the local extent of tumor that could be used for preoperative surgical planning. Three-dimensional stereoscopic volume rendering for malignant pleural mesothelioma is a novel approach. Combined with our innovative perceptual colorization algorithm, stereoscopic volumetric analysis potentially allows for the accurate determination of the extent of pleural mesothelioma with results difficult to duplicate using grayscale, multiplanar CT images.

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

PURPOSE

The measurement of malignant pleural mesothelioma is critical to the assessment of tumor response to therapy. Current response assessment standards utilize summed linear measurements acquired on three computed tomography (CT) sections. The purpose of this study was to evaluate manual area measurements as an alternate response assessment metric, specifically through the study of measurement interobserver variability.

METHODS

Two CT scans from each of 31 patients were collected. Using a computer interface, five observers contoured tumor on three selected CT sections from each baseline scan. Four observers also constructed matched follow-up scan tumor contours for the same 31 patients. Area measurements extracted from these contours were compared using a random effects analysis of variance model to assess relative interobserver variability. The sums of section area measurements were also analyzed, since these area sums are more clinically relevant for response assessment.

RESULTS

When each observer's measurements were compared with those of the other four observers, strong correlation was observed. The 95% confidence interval for relative interobserver variability of baseline scan summed area measurements was [-71%, +240%], spanning 311%. For the follow-up scan summed area measurements, the 95% confidence interval for relative interobserver variability was [-41%, +70%], spanning 111%. At both baseline and follow-up, the variability among observers was a significant component of the total variability in both per-section and summed area measurements (p<0.0001).

CONCLUSIONS

Despite the ability of tumor area measurements to capture tumor burden with greater fidelity than linear tumor thickness measurements, manual area measurements may not be a robust means of response assessment in mesothelioma patients.

Lung volume measurements as a surrogate marker for patient response in malignant pleural mesothelioma

INTRODUCTION

The purpose of this study was to investigate the continuous changes in three distinct response assessment methods during treatment as a marker of response for patients with mesothelioma. Linear tumor thickness measurements, disease volume measurements, and lung volume measurements (a physiological correlate of disease volumes) were investigated in this study.

METHODS

Serial computed tomography scans were obtained during the course of clinically standard chemotherapy for 61 patients. For each of the 216 computed tomography scans, the aerated lung volumes were segmented using a fully automated method, and the pleural disease volume was segmented using a semiautomated method. Modified Response Evaluation Criteria in Solid Tumors linear-thickness measurements were acquired clinically. Diseased (ipsilateral) lung volumes were normalized by the respective contralateral lung volumes to account for the differences in inspiration between scans for each patient. Relative changes in each metric from baseline were tracked over the course of follow-up imaging. Survival modeling was performed using Cox proportional hazards models with time-varying covariates.

RESULTS

Median survival from pretreatment baseline imaging was 12.7 months. A negative correlation was observed between measurements of lung volume and disease volume, and a positive correlation was observed between linear-thickness measurements and disease volume. As continuous numerical parameters, all three response assessment methods were significant imaging biomarkers of patient prognosis in independent survival models.

CONCLUSIONS

Analysis of trajectories of linear-thickness measurements, disease volume measurements, and lung volume measurements during chemotherapy for patients with mesothelioma indicates that increasing linear thickness, increasing disease volume, and decreasing lung volume are all significantly and independently associated with poor patient prognosis.

Do we need a revised staging system for malignant pleural mesothelioma? Analysis of the IASLC database

INTRODUCTION

A number of staging systems have been proposed for malignant pleural mesothelioma (MPM) in the past, but few have utilized a TNM (tumor, node, metastasis) system. The International Association for the Study of Lung Cancer (IASLC) and the International Mesothelioma Interest Group (IMIG) previously developed a TNM-staging system which has been accepted by the International Union Against Cancer (UICC) and the American Joint Commission on Cancer (AJCC). The present study examines this staging system by analysing the updated IASLC database for patients with MPM.

METHODS

De-identified data from participating centres dated from 1995 to 2009 were submitted to the IASLC Statistical Center. Surgical procedures included those with a curative or palliative intent. Survival was measured from the date of pathologic diagnosis to the most recent contact or death. Endpoints included overall survival and analysis of potential prognostic factors.

RESULTS

Data was available for 3,101 patients from 15 centers, mostly from North America and Europe. After a median follow-up of 15 months, a number of clinicopathological and treatment-related prognostic factors were found to significantly influence overall survival. These included overall tumor stage based on the proposed TNM staging system, T category, N category, tumor histology, gender, age, and type of operation.

CONCLUSIONS

The IASLC database represents the largest, multicenter and international database on MPM to date. Analyses demonstrate that the proposed TNM staging system effectively distinguishes the T and N categories, but also highlight areas for potential revision in the future.

Validation study of a fast, accurate, and precise brain tumor volume measurement

Precision and accuracy are sometimes sacrificed to ensure that medical image processing is rapid. To address this, our lab had developed a novel level set segmentation algorithm that is 16× faster and >96% accurate on realistic brain phantoms.

METHODS

This study reports speed, precision and estimated accuracy of our algorithm when measuring MRIs of meningioma brain tumors and compares it to manual tracing and modified MacDonald (MM) ellipsoid criteria. A repeated-measures study allowed us to determine measurement precisions (MPs) - clinically relevant thresholds for statistically significant change.

RESULTS

Speed: the level set, MM, and trace methods required 1:20, 1:35, and 9:35 (mm:ss) respectively on average to complete a volume measurement (p<0.05). Accuracy: the level set was not statistically different to the estimated true lesion volumes (p>0.05). Precision: the MM's within-operator and between-operator MPs were significantly higher (worse) than the other methods (p<0.05). The observed difference in MP between the level set and trace methods did not reach statistical significance (p>0.05).

CONCLUSION

Our level set is faster on average than MM, yet has accuracy and precision comparable to manual tracing.

Automated iterative neutrosophic lung segmentation for image analysis in thoracic computed tomography

PURPOSE

Lung segmentation is a fundamental step in many image analysis applications for lung diseases and abnormalities in thoracic computed tomography (CT). The authors have previously developed a lung segmentation method based on expectation-maximization (EM) analysis and morphological operations (EMM) for our computer-aided detection (CAD) system for pulmonary embolism (PE) in CT pulmonary angiography (CTPA). However, due to the large variations in pathology that may be present in thoracic CT images, it is difficult to extract the lung regions accurately, especially when the lung parenchyma contains extensive lung diseases. The purpose of this study is to develop a new method that can provide accurate lung segmentation, including those affected by lung diseases.

METHODS

An iterative neutrosophic lung segmentation (INLS) method was developed to improve the EMM segmentation utilizing the anatomic features of the ribs and lungs. The initial lung regions (ILRs) were extracted using our previously developed EMM method, in which the ribs were extracted using 3D hierarchical EM segmentation and the ribcage was constructed using morphological operations. Based on the anatomic features of ribs and lungs, the initial EMM segmentation was refined using INLS to obtain the final lung regions. In the INLS method, the anatomic features were mapped into a neutrosophic domain, and the neutrosophic operation was performed iteratively to refine the ILRs. With IRB approval, 5 and 58 CTPA scans were collected retrospectively and used as training and test sets, of which 2 and 34 cases had lung diseases, respectively. The lung regions manually outlined by an experienced thoracic radiologist were used as reference standard for performance evaluation of the automated lung segmentation. The percentage overlap area (POA), the Hausdorff distance (Hdist), and the average distance (AvgDist) of the lung boundaries relative to the reference standard were used as performance metrics.

RESULTS

The proposed method achieved larger POAs and smaller distance errors than the EMM method. For the 58 test cases, the average POA, Hdist, and AvgDist were improved from 85.4±18.4%, 22.6±29.4 mm, and 3.5±5.4 mm using EMM to 91.2±6.7%, 16.0±11.3 mm, and 2.5±1.0 mm using INLS, respectively. The improvements were statistically significant (p<0.05). To evaluate the accuracy of the INLS method in the identification of the lung boundaries affected by lung diseases, the authors separately analyzed the performance of the proposed method on the cases with versus without the lung diseases. The results showed that the cases without lung diseases were segmented more accurately than the cases with lung diseases by both the EMM and the INLS methods, but the INLS method achieved better performance than the EMM method in both cases.

CONCLUSIONS

The new INLS method utilizing the anatomic features of the rib and lung significantly improved the accuracy of lung segmentation, especially for the cases affected by lung diseases. Improvement in lung segmentation will facilitate many image analysis tasks and CAD applications for lung diseases and abnormalities in thoracic CT, including automated PE detection.

Disease volumes as a marker for patient response in malignant pleural mesothelioma

BACKGROUND

The goal of this study was to create a comprehensive model for malignant pleural mesothelioma patient survival utilizing continuous, time-varying estimates of disease volume from computed tomography (CT) imaging in conjunction with clinical covariates.

PATIENTS AND METHODS

Serial CT scans were obtained during the course of clinically standard chemotherapy for 81 patients. The pleural disease volume was segmented for each of the 281 CT scans, and relative changes in disease volume from the baseline scan were tracked over the course of serial follow-up imaging. A prognostic model was built using time-varying disease volume measurements in conjunction with clinical covariates.

RESULTS

Over the course of treatment, disease volume decreased by an average of 19%, and median patient survival was 12.6 months from baseline. In a multivariate survival model, changes in disease volume were significantly associated with patient survival along with disease histology, Eastern Cooperative Oncology Group performance status, and presence of dyspnea.

CONCLUSIONS

Analysis of the trajectories of disease volumes during chemotherapy for patients with mesothelioma indicates that increasing disease volume was significantly and independently associated with poor patient prognosis in both univariate and multivariate survival models.

Computerized segmentation and measurement of malignant pleural mesothelioma

PURPOSE

The current linear method to track tumor progression and evaluate treatment efficacy is insufficient for malignant pleural mesothelioma (MPM). A volumetric method for tumor measurement could improve the evaluation of novel treatments, but a fully manual implementation of volume measurement is too tedious and time-consuming. This manuscript presents a computerized method for the three-dimensional segmentation and volumetric analysis of MPM.

METHODS

The computerized MPM segmentation method segments the lung parenchyma and hemithoracic cavities to define the pleural space. Nonlinear diffusion and a k-means classifier are then implemented to identify MPM in the pleural space. A database of 31 computed tomography scans from 31 patients with pathologically confirmed MPM was retrospectively collected. Three observers independently outlined five randomly selected sections in each scan. The Jaccard similarity coefficient (J) between each of the observers and between the observer-defined and computer-defined segmentations was calculated. The computer-defined and the observer-defined segmentation areas (averaged over all observers) were both calculated for each axial section and compared using Bland-Altman plots.

RESULTS

The median J value among observers averaged over all sections was 0.517. The median J between the computer-defined and manual segmentations was 0.484. The difference between these values was not statistically significant. The area delineated by the computerized method demonstrated variability and bias comparable to the tumor area calculated from manual delineations.

CONCLUSIONS

A computerized method for segmentation and measurement of MPM was developed. This method requires minimal initialization by the user and demonstrated good agreement with manually drawn outlines and area measurements. This method will allow volumetric tracking of tumor progression and may improve the evaluation of novel MPM treatments.