Implementation of a high-resolution, high-contrast magnetic resonance imaging protocol with extended delayed phases for peritoneal mesothelioma.

Background: Imaging of peritoneal malignancies using conventional cross-sectional imaging is challenging, but accurate assessment of peritoneal disease burden could guide better selection for definitive surgery. Here we demonstrate feasibility of high-resolution, high-contrast magnetic resonance imaging (MRI) of peritoneal mesothelioma and explore optimal timing for delayed post-contrast imaging. Methods: Prospective data from inpatients with malignant peritoneal mesothelioma (MPM), imaged with a novel MRI protocol, were analyzed. The new sequences augmenting the clinical protocol were (I) pre-contrast coronal high-resolution T2-weighted single-shot fast spin echo (COR hr T2w SSH FSE) of abdomen and pelvis; and (II) post-contrast coronal high-resolution three-dimensional (3D) T1-weighted modified Dixon (COR hr T1w mDIXON) of abdomen, acquired at five delay times, up to 20 min after administration of a double dose of contrast agent. Quantitative analysis of contrast enhancement was performed using linear regression applied to normalized signal in lesion regions of interest (ROIs). Qualitative analysis was performed by three blinded radiologists. Results: MRI exams from 14 participants (age: mean ± standard deviation, 60±12 years; 71% male) were analyzed. The rate of lesion contrast enhancement was strongly correlated with tumor grade (cumulative nuclear score) (r=-0.65, P<0.02), with 'early' delayed phase (12 min post-contrast) and 'late' delayed phase (19 min post-contrast) performing better for higher grade and lower grade tumors, respectively, in agreement with qualitative scoring of image contrast. Conclusions: High-resolution, high-contrast MRI with extended post-contrast imaging is a viable modality for imaging peritoneal mesothelioma. Multiple, extended (up to 20 min post-contrast) delayed phases are necessary for optimal imaging of peritoneal mesothelioma, depending on the grade of disease.

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

Malignant pleural mesothelioma (MPM) is the most common form of malignant mesothelioma, with exposure to asbestos being the primary cause of the disease. To assess response to treatment, tumor measurements are acquired and evaluated based on a patient's longitudinal computed tomography (CT) scans. Tumor volume, however, is the more accurate metric for assessing tumor burden and response. Automated segmentation methods using deep learning can be employed to acquire volume, which otherwise is a tedious task performed manually. The deep learning-based tumor volume and contours can then be compared with a standard reference to assess the robustness of the automated segmentations. The purpose of this study was to evaluate the impact of probability map threshold on MPM tumor delineations generated using a convolutional neural network (CNN). Eighty-eight CT scans from 21 MPM 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 standard reference provided by the radiologist and CNN outputs for thresholds ranging from 0.001 to 0.9. CNN annotations consistently yielded smaller tumor volumes than radiologist contours. Reducing the probability threshold from 0.5 to 0.1 decreased the absolute percent volume difference, on average, from 43.96% to 24.18%. Median and mean DSC ranged from 0.58 to 0.60, with a peak at a threshold of 0.5; 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.

Magnetic resonance imaging preprocessing and radiomic features for classification of autosomal dominant polycystic kidney disease genotype.

Purpose: Our study aims to investigate the impact of preprocessing on magnetic resonance imaging (MRI) radiomic features extracted from the noncystic kidney parenchyma of patients with autosomal dominant polycystic kidney disease (ADPKD) in the task of classifying PKD1 versus PKD2 genotypes, which differ with regard to cyst burden and disease outcome. Approach: The effect of preprocessing on radiomic features was investigated using a single T2-weighted fat saturated (T2W-FS) MRI scan from PKD1 and PKD2 subjects (29 kidneys in total) from the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease study. Radiomic feature reproducibility using the intraclass correlation coefficient (ICC) was computed across MRI normalizations (z-score, reference-tissue, and original image), gray-level discretization, and upsampling and downsampling pixel schemes. A second dataset for genotype classification from 136 subjects T2W-FS MRI images previously enrolled in the HALT Progression of Polycystic Kidney Disease study was matched for age, gender, and Mayo imaging classification class. Genotype classification was performed using a logistic regression classifier and radiomic features extracted from (1) the noncystic kidney parenchyma and (2) the entire kidney. The area under the receiver operating characteristic curve (AUC) was used to evaluate the classification performance across preprocessing methods. Results: Radiomic features extracted from the noncystic kidney parenchyma were sensitive to preprocessing parameters, with varying reproducibility depending on the parameter. The percentage of features with good-to-excellent ICC scores ranged from 14% to 58%. AUC values ranged between 0.47 to 0.68 and 0.56 to 0.73 for the noncystic kidney parenchyma and entire kidney, respectively. Conclusions: Reproducibility of radiomic features extracted from the noncystic kidney parenchyma was dependent on the preprocessing parameters used, and the effect on genotype classification was sensitive to preprocessing parameters. The results suggest that texture features may be indicative of genotype expression in ADPKD.

Germline Variants Incidentally Detected via Tumor-Only Genomic Profiling of Patients With Mesothelioma.

Importance: Patients with mesothelioma often have next-generation sequencing (NGS) of their tumor performed; tumor-only NGS may incidentally identify germline pathogenic or likely pathogenic (P/LP) variants despite not being designed for this purpose. It is unknown how frequently patients with mesothelioma have germline P/LP variants incidentally detected via tumor-only NGS. Objective: To determine the prevalence of incidental germline P/LP variants detected via tumor-only NGS of mesothelioma. Design, Setting, and Participants: A series of 161 unrelated patients with mesothelioma from a high-volume mesothelioma program had tumor-only and germline NGS performed during April 2016 to October 2021. Follow-up ranged from 18 months to 7 years. Tumor and germline assays were compared to determine which P/LP variants identified via tumor-only NGS were of germline origin. Data were analyzed from January to March 2023. Main Outcomes and Measures: The proportion of patients with mesothelioma who had P/LP germline variants incidentally detected via tumor-only NGS. Results: Of 161 patients with mesothelioma, 105 were male (65%), the mean (SD) age was 64.7 (11.2) years, and 156 patients (97%) self-identified as non-Hispanic White. Most (126 patients [78%]) had at least 1 potentially incidental P/LP germline variant. The positive predictive value of a potentially incidental germline P/LP variant on tumor-only NGS was 20%. Overall, 26 patients (16%) carried a P/LP germline variant. Germline P/LP variants were identified in ATM, ATR, BAP1, CHEK2, DDX41, FANCM, HAX1, MRE11A, MSH6, MUTYH, NF1, SAMD9L, and TMEM127. Conclusions and Relevance: In this case series of 161 patients with mesothelioma, 16% had confirmed germline P/LP variants. Given the implications of a hereditary cancer syndrome diagnosis for preventive care and familial counseling, clinical approaches for addressing incidental P/LP germline variants in tumor-only NGS are needed. Tumor-only sequencing should not replace dedicated germline testing. Universal germline testing is likely needed for patients with mesothelioma.

A Competition, Benchmark, Code, and Data for Using Artificial Intelligence to Detect Lesions in Digital Breast Tomosynthesis.

Importance: An accurate and robust artificial intelligence (AI) algorithm for detecting cancer in digital breast tomosynthesis (DBT) could significantly improve detection accuracy and reduce health care costs worldwide. Objectives: To make training and evaluation data for the development of AI algorithms for DBT analysis available, to develop well-defined benchmarks, and to create publicly available code for existing methods. Design, Setting, and Participants: This diagnostic study is based on a multi-institutional international grand challenge in which research teams developed algorithms to detect lesions in DBT. A data set of 22 032 reconstructed DBT volumes was made available to research teams. Phase 1, in which teams were provided 700 scans from the training set, 120 from the validation set, and 180 from the test set, took place from December 2020 to January 2021, and phase 2, in which teams were given the full data set, took place from May to July 2021. Main Outcomes and Measures: The overall performance was evaluated by mean sensitivity for biopsied lesions using only DBT volumes with biopsied lesions; ties were broken by including all DBT volumes. Results: A total of 8 teams participated in the challenge. The team with the highest mean sensitivity for biopsied lesions was the NYU B-Team, with 0.957 (95% CI, 0.924-0.984), and the second-place team, ZeDuS, had a mean sensitivity of 0.926 (95% CI, 0.881-0.964). When the results were aggregated, the mean sensitivity for all submitted algorithms was 0.879; for only those who participated in phase 2, it was 0.926. Conclusions and Relevance: In this diagnostic study, an international competition produced algorithms with high sensitivity for using AI to detect lesions on DBT images. A standardized performance benchmark for the detection task using publicly available clinical imaging data was released, with detailed descriptions and analyses of submitted algorithms accompanied by a public release of their predictions and code for selected methods. These resources will serve as a foundation for future research on computer-assisted diagnosis methods for DBT, significantly lowering the barrier of entry for new researchers.

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.

The role of imaging in diagnosis and management of malignant peritoneal mesothelioma: a systematic review.

PURPOSE: Imaging of the peritoneum and related pathology is a challenge. Among peritoneal diseases, malignant peritoneal mesothelioma (MPeM) is an uncommon tumor with poor prognosis. To date, there are no specific guidelines or imaging protocols dedicated for the peritoneum and MPeM. The objective of this study was to analyze the literature describing imaging modalities used for MPeM to determine their relative clinical efficacy and review commonly reported imaging features of MPeM to promote standardized reporting. METHODS: We performed a systematic review of original research articles discussing imaging modalities in MPeM from 1999 to 2020. Effectiveness measures and common findings were compared across imaging modalities. RESULTS: Among 582 studies analyzed, the most-used imaging modality was CT (54.3%). In the differentiation of MPeM from peritoneal carcinomatosis, one study found CT had a diagnostic sensitivity of 53%, specificity of 100%, and accuracy of 68%. Two studies found fluorodeoxyglucose positron emission tomography (FDG-PET) had sensitivity of 86-92%, specificity of 83-89%, and accuracy of 87-89%. Another study found magnetic resonance imaging (MRI) was the best predictor of the peritoneal carcinomatosis index. Characteristics shown to best differentiate MPeM from other diseases included ascites, peritoneal thickening, mesenteric thickening, pleural plaques, maximum tumor dimension, and number of masses. CONCLUSION: Most published MPeM imaging studies utilized CT. PET/CT or MRI appear promising, and future studies should compare effectiveness of these modalities. MPeM imaging reports should highlight ascites, number of and maximum tumor dimension, peritoneal/mesenteric thickening, and associated pleural plaques, allowing for better aggregation of MPeM imaging data across studies.

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.

Effects of variability in radiomics software packages on classifying patients with radiation pneumonitis.

Purpose: While radiomics feature values can differ when extracted using different radiomics software, the effects of these variations when applied to a particular clinical task are currently unknown. The goal of our study was to use various radiomics software packages to classify patients with radiation pneumonitis (RP) and to quantify the variation in classification ability among packages. Approach: A database of serial thoracic computed tomography scans was obtained from 105 patients with esophageal cancer. Patients were treated with radiation therapy (RT), resulting in 20 patients developing RP grade ≥ 2 . Regions of interest (ROIs) were randomly placed in the lung volume of the pre-RT scan within high-dose regions ( ≥ 30 Gy ), and corresponding ROIs were anatomically matched in the post-RT scan. Three radiomics packages were compared: A1 (in-house), IBEX v1.0 beta, and PyRadiomics v.2.0.0. Radiomics features robust to deformable registration and common among radiomics packages were calculated: four first-order and four gray-level co-occurrence matrix features. Differences in feature values between time points were calculated for each feature, and logistic regression was used in conjunction with analysis of variance to classify patients with and without RP ( p < 0.006 ). Classification ability for each package was assessed using receiver operating characteristic (ROC) analysis and compared using the area under the ROC curve (AUC). Results: Of the eight radiomics features, five were significantly correlated with RP status for all three packages, whereas one feature was not significantly correlated with RP for all three packages. The remaining two features differed in whether or not they were significantly associated with RP status among the packages. Seven of the eight features agreed among the packages in whether the AUC value was significantly > 0.5 . Conclusions: Radiomics features extracted using different software packages can result in differences in classification ability.

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.

EURACAN/IASLC Proposals for Updating the Histologic Classification of Pleural Mesothelioma: Towards a More Multidisciplinary Approach.

INTRODUCTION: Molecular and immunologic breakthroughs are transforming the management of thoracic cancer, although advances have not been as marked for malignant pleural mesothelioma where pathologic diagnosis has been essentially limited to three histologic subtypes. METHODS: A multidisciplinary group (pathologists, molecular biologists, surgeons, radiologists, and oncologists), sponsored by European Network for Rare Adult Solid Cancers/International Association for the Study of Lung Cancer, met in 2018 to critically review the current classification. RESULTS: Recommendations include: (1) classification should be updated to include architectural patterns and stromal and cytologic features that refine prognostication; (2) subject to data accrual, malignant mesothelioma in situ could be an additional category; (3) grading of epithelioid malignant pleural mesotheliomas should be routinely undertaken; (4) favorable/unfavorable histologic characteristics should be routinely reported; (5) clinically relevant molecular data (programmed death ligand 1, BRCA 1 associated protein 1 [BAP1], and cyclin dependent kinase inhibitor 2A) should be incorporated into reports, if undertaken; (6) other molecular data should be accrued as part of future trials; (7) resection specimens (i.e., extended pleurectomy/decortication and extrapleural pneumonectomy) should be pathologically staged with smaller specimens being clinically staged; (8) ideally, at least three separate areas should be sampled from the pleural cavity, including areas of interest identified on pre-surgical imaging; (9) image-acquisition protocols/imaging terminology should be standardized to aid research/refine clinical staging; (10) multidisciplinary tumor boards should include pathologists to ensure appropriate treatment options are considered; (11) all histologic subtypes should be considered potential candidates for chemotherapy; (12) patients with sarcomatoid or biphasic mesothelioma should not be excluded from first-line clinical trials unless there is a compelling reason; (13) tumor subtyping should be further assessed in relation to duration of response to immunotherapy; and (14) systematic screening of all patients for germline mutations is not recommended, in the absence of a family history suspicious for BAP1 syndrome. CONCLUSIONS: These multidisciplinary recommendations for pathology classification and application will allow more informative pathologic reporting and potential risk stratification, to support clinical practice, research investigation and clinical trials.

Radiologic Considerations and Standardization of Malignant Pleural Mesothelioma Imaging Within Clinical Trials: Consensus Statement from the NCI Thoracic Malignancy Steering Committee - International Association for the Study of Lung Cancer - Mesothelioma Applied Research Foundation Clinical Trials Planning Meeting.

Detailed guidelines pertaining to radiological assessment of malignant pleural mesothelioma are currently lacking due to the rarity of the disease, complex morphology, propensity to invade multiple planes simultaneously, and lack of specific recommendations within the radiology community about assessment, reporting, and follow-up. In March 2017, a multidisciplinary meeting of mesothelioma experts was co-sponsored by the National Cancer Institute Thoracic Malignancy Steering Committee, International Association for the Study of Lung Cancer, and the Mesothelioma Applied Research Foundation. One of the outcomes of this conference was the foundation of detailed, multidisciplinary consensus imaging and management guidelines. Here, we present the recommendations for radiologic assessment of malignant pleural mesothelioma in the setting of clinical trial enrollment. We discuss optimization of imaging parameters across modalities, standardized reporting, and response assessment within clinical trials.

Accuracy of the Vancouver Lung Cancer Risk Prediction Model Compared With That of Radiologists.

BACKGROUND: Risk models have been developed that include the subject's pretest risk profile and imaging findings to predict the risk of cancer in an objective way. We assessed the accuracy of the Vancouver Lung Cancer Risk Prediction Model compared with that of trainee and experienced radiologists using a subset of size-matched nodules from the National Lung Screening Trial (NLST). METHODS: One hundred cases from the NLST database were selected (size range, 4-20 mm), including 20 proven cancers and 80 size-matched benign nodules. Three experienced thoracic radiologists and three trainee radiologists were asked to estimate the likelihood of cancer in each case, first independently, and then with knowledge of the model's risk prediction. The results generated by the model alone also were estimated using receiver operating characteristic (ROC) analysis. The area under the ROC curve (AUC) for each viewing condition was calculated, and statistical significance in their differences was tested by using the Dorfman-Berbaum-Metz method. RESULTS: Human observers were more accurate (AUC value of 0.85 ± 0.05 [SD]) than was the model (0.77 ± 0.06) in estimating the risk of malignancy (P = .0010), and use of the model did not improve their accuracy (0.84 ± 0.06). Experienced radiologists performed better than did trainees. Human observers could distinguish benign from malignant nodule morphology more accurately than could the model, which relies mainly on nodule size for risk estimation. CONCLUSIONS: Experienced and trainee radiologists had superior ability to predict the risk of cancer in size-matched nodules from a screening trial compared with that of the Vancouver model, and use of the model did not improve their accuracy.

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.

Correlation of patient survival with clinical tumor measurements in malignant pleural mesothelioma.

OBJECTIVES: To evaluate differences in the tumor response classifications that result from clinical measurements and to compare these response classifications with overall survival for patients with malignant pleural mesothelioma (MPM). METHODS: One hundred thirty-one computed tomography (CT) scans were collected from 41 MPM patients enrolled in a clinical trial. Primary measurements had been acquired by clinical radiologists at a single center during routine clinical workflow, and the variability of these measurements was investigated. Retrospective measurements were acquired by a single radiologist in compliance with the study protocol based on the modified response evaluation criteria in solid tumors (RECIST). Differences in response classification categories by the two measurement approaches were evaluated and compared with patient survival. RESULTS: Eleven (27%) of the 41 MPM patients had primary measurements at baseline or at follow-up that deviated from the guidelines of the clinical trial protocol. Among the 41 baseline scans, no statistical difference was observed in summed tumor measurements between primary and retrospective measurements. Response classification based on primary and retrospective measurements was different in 23 (26%) of the 90 follow-up scans, and best response was the different in seven (17%) of the 41 patients. Using Harrell's C statistic as a measure of correlation, response based on retrospective measurements correlated better with survival (C = 0.62) than did response based on primary measurements (C = 0.57). CONCLUSIONS: Strict compliance with the measurement protocol yields tumor response classifications that may differ from those obtained in clinical practice. Response based on retrospective measurements correlated better with survival than did response based on primary measurements. KEY POINTS: • Response classifications could be different between clinical primary and retrospective measurements for malignant pleural mesothelioma. • Response classifications obtained by strict compliance with the trial-specific protocol correlated better with survival than the classifications based on primary measurements. • Quality assurance and radiologist training measures should be used to ensure the integrity of image-based tumor measurements in mesothelioma clinical trials.

Dynamic contrast-enhanced CT for the assessment of tumour response in malignant pleural mesothelioma: a pilot study.

OBJECTIVES: The aim of this pilot study was to investigate the utility of haemodynamic parameters derived from dynamic contrast-enhanced computed tomography (DCE-CT) scans in the assessment of tumour response to treatment in malignant pleural mesothelioma (MPM) patients. METHODS: The patient cohort included nine patients undergoing chemotherapy and five patients on observation. Each patient underwent two DCE-CT scans separated by approximately 2 months. The DCE-CT parameters of tissue blood flow (BF) and tissue blood volume (BV) were obtained within the dynamically imaged tumour. Mean relative changes in tumour DCE-CT parameters between scans were compared between the on-treatment and on-observation cohorts. DCE-CT parameter changes were correlated with relative change in tumour bulk evaluated according to the modified RECIST protocol. RESULTS: Differing trends in relative change in BF and BV between scans were found between the two patient groups (p = 0.19 and p = 0.06 for BF and BV, respectively). No significant rank correlations were found when comparing relative changes in DCE-CT parameters with relative change in tumour bulk. CONCLUSIONS: Differing trends in the relative change of BF and BV between patients on treatment and on observation indicate the potential of DCE-CT for the assessment of pharmacodynamic endpoints with respect to treatment in MPM. A future study with a larger patient cohort and unified treatment regimens should be undertaken to confirm the results of this pilot study. KEY POINTS: • CT-derived haemodynamic parameters show differing trends between malignant pleural mesothelioma patients on treatment and patients off treatment • Changes in haemodynamic parameters do not correlate with changes in tumour bulk as measured according to the modified RECIST protocol • Differing trends across the two patient groups indicate the potential sensitivity of DCE-CT to assess pharmacodynamic endpoints in the treatment of MPM.

Computer-assisted Curie scoring for metaiodobenzylguanidine (MIBG) scans in patients with neuroblastoma.

BACKGROUND: Radiolabeled metaiodobenzylguanidine (MIBG) is sensitive and specific for detecting neuroblastoma. The extent of MIBG-avid disease is assessed using Curie scores. Although Curie scoring is prognostic in patients with high-risk neuroblastoma, there is no standardized method to assess the response of specific sites of disease over time. The goal of this study was to develop approaches for Curie scoring to facilitate the calculation of scores and comparison of specific sites on serial scans. PROCEDURE: We designed three semiautomated methods for determining Curie scores, each with increasing degrees of computer assistance. Method A was based on visual assessment and tallying of MIBG-avid lesions. For method B, scores were tabulated from a schematic that associated anatomic regions to MIBG-positive lesions. For method C, an anatomic mesh was used to mark MIBG-positive lesions with automatic assignment and tallying of scores. Five imaging physicians experienced in MIBG interpretation scored 38 scans using each method, and the feasibility and utility of the methods were assessed using surveys. RESULTS: There was good reliability between methods and observers. The user-interface methods required 57 to 110 seconds longer than the visual method. Imaging physicians indicated that it was useful that methods B and C enabled tracking of lesions. Imaging physicians preferred method B to method C because of its efficiency. CONCLUSIONS: We demonstrate the feasibility of semiautomated approaches for Curie score calculation. Although more time was needed for strategies B and C, the ability to track and document individual MIBG-positive lesions over time is a strength of these methods.