Can CT and MR Shape and Textural Features Differentiate Benign Versus Malignant Pleural Lesions?

[Modern Radiological Approaches to the Diagnosis and Staging of Pleural Mesothelioma]

Pleural mesothelioma (PM) is a highly aggressive tumour, mainly associated with prior asbestos exposure. Symptoms typically do not manifest until 20 to 50 years after exposure. Depending on the histological subtype of PM, the prognosis varies, though the median survival time is only 12-18 months. Imaging plays a central role in the management of PM patients, particularly in assessing operability and treatment response. However, PM presents a unique challenge for radiology due to its rarity, complex morphology, and tendency to invade multiple tissue layers simultaneously. Contrast-enhanced computed tomography (CT) is the central imaging modality in the diagnosis, preoperative planning and therapy monitoring of pleural mesothelioma (PM). Ideally, CT should include both the thorax and abdomen to capture the entire pleural space. Tumour thickness and volume, as determined by CT, are important prognostic factors for patient survival in PM. In addition, PET/CT, using radioactively labelled fluorodeoxyglucose (18F-FDG), offers additional valuable insights into tumour metabolism. Since PM is typically metabolically active, PET/CT is particularly effective in detecting smaller lesions, occult metastases, and assessing morphologically ambiguous lesions. Magnetic resonance imaging (MRI), on the other hand, offers distinct advantages over CT, due to its superior soft tissue contrast, particularly in visualizing tumour extent and infiltration of adjacent structures. Dynamic contrast-enhanced imaging, diffusion-weighted imaging, and 4D sequences provide valuable additional information. On the basis of the "Best Practices" of the expert panel from the International Mesothelioma Interest Group (iMig), we provide an overview of the common imaging modalities, including conventional X-ray, CT, MRI, and PET/CT. Additionally, we discuss staging based on the TNM classification (Tumour, Node, Metastasis), which evaluates the local invasion of the tumour (T), lymph node involvement (N), and the presence of metastases (M). We also present examples of assessing treatment response and highlight recent developments in diagnostic imaging.

The value of computed tomography-based radiomics for predicting malignant pleural effusions

Background

Malignant pleural effusion (MPE) is a common clinical problem that requires cytological and/or histological confirmation obtained by invasive examination to establish a definitive diagnosis. Radiomics is rapidly evolving and can provide a non-invasive tool to identify MPE.

Objectives

We aimed to develop a model based on radiomic features extracted from unenhanced chest computed tomography (CT) images and investigate its value in predicting MPE.

Method

This retrospective study included patients with pleural effusions between January 2016 and June 2020. All patients underwent a chest CT scanning and medical thoracoscopy after artificial pneumothorax. Cases were divided into a training cohort and a test cohort for modelling and verifying respectively. The Mann-Whitney U test and the least absolute shrinkage and selection operator (LASSO) were applied to determine the optimal features. We built a radiomics model based on support vector machines (SVM) and evaluated its performance using ROC and calibration curve analysis.

Results

Twenty-nine patients with MPE and fifty-two patients with non-MPE were enrolled. A total of 944 radiomic features were quantitatively extracted from each sample and reduced to 14 features for modeling after selection. The AUC of the radiomics model was 0.96 (95% CI: 0.912-0.999) and 0.86 (95% CI: 0.657~1.000) in the training and test cohorts, respectively. The calibration curves for model were in good agreement between predicted and actual data.

Conclusions

The radiomics model based on unenhanced chest CT has good performance for predicting MPE and may provide a powerful tool for doctors in clinical decision-making.

Computed tomography radiomic feature analysis of thymic epithelial tumors: Differentiation of thymic epithelial tumors from thymic cysts and prediction of histological subtypes

PURPOSE

To investigate the value of computed tomography (CT) radiomic feature analysis for the differential diagnosis between thymic epithelial tumors (TETs) and thymic cysts, and prediction of histological subtypes of TETs.

MATERIALS AND METHODS

Twenty-four patients with TETs (13 low-risk and 9 high-risk thymomas, and 2 thymic carcinomas) and 12 with thymic cysts were included in this study. For each lesion, the radiomic features of a volume of interest covering the lesion were extracted from non-contrast enhanced CT images. The Least Absolute Shrinkage and Selection Operator (Lasso) method was used for the feature selection. Predictive models for differentiating TETs from thymic cysts (model A), and high risk thymomas + thymic carcinomas from low risk thymomas (model B) were created from the selected features. The receiver operating characteristic curve was used to evaluate the effectiveness of radiomic feature analysis for differentiating among these tumors.

RESULTS

In model A, the selected 5 radiomic features for the model A were NGLDM_Contrast, GLCM_Correlation, GLZLM_SZLGE, DISCRETIZED_HISTO_Entropy_log2, and DISCRETIZED_HUmin. In model B, sphericity was the only selected feature. The area under the curve, sensitivity, and specificity of radiomic feature analysis were 1 (95% confidence interval [CI]: 1-1), 100%, and 100%, respectively, for differentiating TETs from thymic cysts (model A), and 0.76 (95%CI: 0.53-0.99), 64%, and 100% respectively, for differentiating high-risk thymomas + thymic carcinomas from low-risk thymomas (model B).

CONCLUSION

CT radiomic analysis could be utilized as a non-invasive imaging technique for differentiating TETs from thymic cysts, and high-risk thymomas + thymic carcinomas from low-risk thymomas.

Pleural Neoplasms-What Could MRI Change?

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

Old Borders and New Horizons in Multimodality Imaging of Malignant Pleural Mesothelioma

BACKGROUND

The purpose of this article is to describe the various imaging techniques involved in detection, staging, and preoperative planning in malignant pleural mesothelioma (MPM) focusing on new imaging modalities.

METHODS

For this purpose, first a brief summary of the etiology of MPM is given. Second, not only the commonly known, but also novel imaging modalities used in MPM will be discussed.

RESULTS

A wide range of imaging methods, from conventional chest radiography, through computed tomography and hybrid imaging to radiomics and artificial intelligence, can be used to evaluate MPM.

CONCLUSION

Nowadays multimodality imaging is considered the cornerstone in MPM diagnosis and staging.

The value of magnetic resonance imaging-based tumor shape features for assessing microsatellite instability status in endometrial cancer

BACKGROUND

Microsatellite instability (MSI) status can be used for the classification and risk stratification of endometrial cancer (EC). This study aimed to investigate whether magnetic resonance imaging (MRI)-based tumor shape features can help assess MSI status in EC before surgery.

METHODS

The medical records of 88 EC patients with MSI status were retrospectively reviewed. Quantitative and subjective shape features based on MRI were used to assess MSI status. Variables were compared using the Student's t-test, χ² test, or Wilcoxon rank-sum test where appropriate. Univariate and multivariate analyses were performed by the logistic regression model. The area under the curve (AUC) was used to estimate the discrimination performance of variables.

RESULTS

There were 23 patients with MSI, and 65 patients with microsatellite stability (MSS) in this study. Eccentricity and shape type showed significant differences between MSI and MSS (P=0.039 and P=0.033, respectively). The AUC values of eccentricity, shape type, and the combination of 2 features for assessing MSI were 0.662 [95% confidence interval (CI): 0.554-0.770], 0.627 (95% CI: 0.512-0.743), and 0.727 (95% CI: 0.613-0.842), respectively. Considering the International Federation of Gynecology and Obstetrics (FIGO) staging, eccentricity maintained a significant difference in stages I-II (P=0.039), while there was no statistical difference in stages III-IV (P=0.601).

CONCLUSIONS

It is possible that MRI-based tumor shape features, including eccentricity and shape type, could be promising markers for assessing MSI status. The features may aid in the preliminary screening of EC patients with MSI.

Automated Detection, Segmentation, and Classification of Pleural Effusion From Computed Tomography Scans Using Machine Learning

OBJECTIVE

This study trained and evaluated algorithms to detect, segment, and classify simple and complex pleural effusions on computed tomography (CT) scans.

MATERIALS AND METHODS

For detection and segmentation, we randomly selected 160 chest CT scans out of all consecutive patients (January 2016-January 2021, n = 2659) with reported pleural effusion. Effusions were manually segmented and a negative cohort of chest CTs from 160 patients without effusions was added. A deep convolutional neural network (nnU-Net) was trained and cross-validated (n = 224; 70%) for segmentation and tested on a separate subset (n = 96; 30%) with the same distribution of reported pleural complexity features as in the training cohort (eg, hyperdense fluid, gas, pleural thickening and loculation). On a separate consecutive cohort with a high prevalence of pleural complexity features (n = 335), a random forest model was implemented for classification of segmented effusions with Hounsfield unit thresholds, density distribution, and radiomics-based features as input. As performance measures, sensitivity, specificity, and area under the curves (AUCs) for detection/classifier evaluation (per-case level) and Dice coefficient and volume analysis for the segmentation task were used.

RESULTS

Sensitivity and specificity for detection of effusion were excellent at 0.99 and 0.98, respectively (n = 96; AUC, 0.996, test data). Segmentation was robust (median Dice, 0.89; median absolute volume difference, 13 mL), irrespective of size, complexity, or contrast phase. The sensitivity, specificity, and AUC for classification in simple versus complex effusions were 0.67, 0.75, and 0.77, respectively.

CONCLUSION

Using a dataset with different degrees of complexity, a robust model was developed for the detection, segmentation, and classification of effusion subtypes. The algorithms are openly available at https://github.com/usb-radiology/pleuraleffusion.git.

Contrast-Enhanced CT-Based Radiomics for the Differentiation of Nodular Goiter from Papillary Thyroid Carcinoma in Thyroid Nodules

Background

Papillary thyroid carcinoma (PTC) and nodular goiter (NG) represent the most commonly malignant and benign diseases of thyroid nodules and are often confused in diagnosis. CT examination has a certain diagnostic value for the diagnosis of suspected malignant thyroid nodules. The application of machine learning to radiomics features provides a new diagnostic approach, which has been widely used in ultrasound examination of the thyroid, but there are few literatures on CT examination.

Purpose

To explore the efficacy of a diagnostic model aided by machine learning for preoperative differentiation of nodular goiter and papillary thyroid carcinoma thyroid nodules on the basis of 3D arterial-phase contrast-enhanced computed tomography (CECT) features.

Materials and Methods

We collected the data of 193 NG and 214 PTC thyroid nodules from 407 patients in CT examinations. Together with the pathologist findings and radiology diagnosis, we built a radiomics model using the 1218 features extracted from the arterial phase of CECT images. By comparing the diagnostic performance of the radiomics model with that of the clinical diagnosis, we assessed the performance of the radiomics model.

Results

The radiomics model was developed based on multivariable logistic regression with the optimal 12 radiomics features after feature dimension reduction. The radiomics model performed well on the classification accuracy of the PTC and NG thyroid nodules in the training group and validation group.

Conclusion

The radiomics model based on the 3D arterial phase of CECT features performed better than the group of experienced radiologists in differentiating NG and PTC thyroid nodules.

Added value of diffusion-weighted MRI in assessment of pleural lesions

Background

There are many causes of pleural disease including variable benign and malignant etiologies. DWI is a non-enhanced functional MRI technique that allows qualitative and quantitative characterization of tissues based on their water molecules diffusivity. The aim of this study was to evaluate the diagnostic value of DWI-MRI in detection and characterization of pleural diseases and its capability in differentiating benign from malignant pleural lesions.

Results

Conventional MRI was able to discriminate benign from malignant lesions by using morphological features (contour and thickness) with sensitivity 89.29%, specificity 76%, positive predictive value 89%, negative predictive value 76.92%, and accuracy 85.37%. ADC value as a quantitative parameter of DWI found that ADC values of malignant pleural diseases were significantly lower than that of benign lesions (P < 0.001). Hence, we discovered that using ADC mean value of 1.68 × 10-3 mm2/s as a cutoff value can differentiate malignant from benign pleural diseases with sensitivity 89.3%, specificity 100%, positive predictive value 100%, negative predictive value 81.2%, and accuracy 92.68% (P < 0.001).

Conclusion

Although DWI-MRI is unable to differentiate between malignant and benign pleural effusion, its combined morphological and functional information provide valid non-invasive method to accurately characterize pleural soft tissue diseases differentiating benign from malignant lesions with higher specificity and accuracy than conventional MRI.

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.

Radiomics Nomogram for Identifying Sub-1 cm Benign and Malignant Thyroid Lesions

PURPOSE

To develop and validate a radiomics nomogram for identifying sub-1 cm benign and malignant thyroid lesions.

METHOD

A total of 171 eligible patients with sub-1 cm thyroid lesions (56 benign and 115 malignant) who were treated in Yantai Yuhuangding Hospital between January and September 2019 were retrospectively collected and randomly divided into training (n = 136) and validation sets (n = 35). The radiomics features were extracted from unenhanced and arterial contrast-enhanced computed tomography images of each patient. In the training set, one-way analysis of variance and least absolute shrinkage and selection operator (LASSO) logistic regression were used to select the features related to benign and malignant lesions, and the LASSO algorithm was used to construct the radiomics signature. Combined with clinical independent predictive factors, a radiomics nomogram was constructed with a multivariate logistic regression model. The performance of the radiomics nomogram was evaluated by using the receiver operating characteristic (ROC) and calibration curves in the training and validation sets. The clinical usefulness was evaluated by using decision curve analysis (DCA).

RESULTS

The radiomics signature consisting of 13 selected features achieved favorable prediction efficiency. The radiomics nomogram, which incorporated radiomics signature and clinical independent predictive factors including age and Thyroid Imaging Reporting and Data System category, showed good calibration and discrimination in the training (area under the ROC [AUC]: 0.853; 95% confidence interval [CI]: 0.797, 0.899) and validation sets (AUC: 0.851; 95% CI: 0.735, 0.931). DCA demonstrated that the nomogram was clinically useful.

CONCLUSION

As a noninvasive preoperative prediction tool, the radiomics nomogram incorporating radiomics signature and clinical predictive factors shows favorable predictive efficiency for identifying sub-1 cm benign and malignant thyroid lesions.

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

OBJECTIVES

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSION

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

The Evolving Landscape of the Molecular Epidemiology of Malignant Pleural Mesothelioma

Malignant pleural mesothelioma (MPM) is a rare and aggressive malignancy that most commonly affects the pleural lining of the lungs. It has a strong association with exposure to biopersistent fibers, mainly asbestos (80% of cases) and-in specific geographic regions-erionite, zeolites, ophiolites, and fluoro-edenite. Individuals with a chronic exposure to asbestos generally have a long latency with no or few symptoms. Then, when patients do become symptomatic, they present with advanced disease and a worse overall survival (about 13/15 months). The fibers from industrial production not only pose a substantial risk to workers, but also to their relatives and to the surrounding community. Modern targeted therapies that have shown benefit in other human tumors have thus far failed in MPM. Overall, MPM has been listed as orphan disease by the European Union. However, molecular high-throughput profiling is currently unveiling novel biomarkers and actionable targets. We here discuss the natural evolution, mainly focusing on the novel concept of molecular epidemiology. The application of innovative endpoints, quantification of genetic damages, and definition of genetic susceptibility are reviewed, with the ultimate goal to point out new tools for screening of exposed subject and for designing more efficient diagnostic and therapeutic strategies.

FDG PET versus CT radiomics to predict outcome in malignant pleural mesothelioma patients

BACKGROUND

Careful selection of malignant pleural mesothelioma (MPM) patients for curative treatment is of highest importance, as the multimodal treatment regimen is challenging for patients and harbors a high risk of substantial toxicity. Radiomics-a quantitative method for image analysis-has shown its prognostic ability in different tumor entities and could therefore play an important role in optimizing patient selection for radical cancer treatment. So far, radiomics as a prognostic tool in MPM was not investigated.

MATERIALS AND METHODS

This study is based on 72 MPM patients treated with surgery in a curative intent at our institution between 2009 and 2017. Pre-treatment Fluorine-18 fluorodeoxyglucose (FDG) PET and CT scans were used for radiomics outcome modeling. After extraction of 1404 CT and 1410 FDG PET features from each image, a preselection by principal component analysis was performed to include only robust, non-redundant features for the cox regression to predict the progression-free survival (PFS) and the overall survival (OS). Results were validated on a separate cohort. Additionally, SUVmax and SUVmean, and volume were tested for their prognostic ability for PFS and OS.

RESULTS

For the PFS a concordance index (c-index) of 0.67 (95% CI 0.52-0.82) and 0.66 (95% CI 0.57-0.78) for the training cohort (n = 36) and internal validation cohort (n = 36), respectively, were obtained for the PET radiomics model. The PFS advantage of the low-risk group translated also into an OS advantage. On CT images, no radiomics model could be trained. SUV max and SUV mean were also not prognostic in terms of PFS and OS.

CONCLUSION

We were able to build a successful FDG PET radiomics model for the prediction of PFS in MPM. Radiomics could serve as a tool to aid clinical decision support systems for treatment of MPM in future.

Malignant Pleural Mesothelioma: Genetic and Microenviromental Heterogeneity as an Unexpected Reading Frame and Therapeutic Challenge

Mesothelioma is a malignancy of serosal membranes including the peritoneum, pleura, pericardium and the tunica vaginalis of the testes. Malignant mesothelioma (MM) is a rare disease with a global incidence in countries like Italy of about 1.15 per 100,000 inhabitants. Malignant Pleural Mesothelioma (MPM) is the most common form of mesothelioma, accounting for approximately 80% of disease. Although rare in the global population, mesothelioma is linked to industrial pollutants and mineral fiber exposure, with approximately 80% of cases linked to asbestos. Due to the persistent asbestos exposure in many countries, a worldwide progressive increase in MPM incidence is expected for the current and coming years. The tumor grows in a loco-regional pattern, spreading from the parietal to the visceral pleura and invading the surrounding structures that induce the clinical picture of pleural effusion, pain and dyspnea. Distant spreading and metastasis are rarely observed, and most patients die from the burden of the primary tumor. Currently, there are no effective treatments for MPM, and the prognosis is invariably poor. Some studies average the prognosis to be roughly one-year after diagnosis. The uniquely poor mutational landscape which characterizes MPM appears to derive from a selective pressure operated by the environment; thus, inflammation and immune response emerge as key players in driving MPM progression and represent promising therapeutic targets. Here we recapitulate current knowledge on MPM with focus on the emerging network between genetic asset and inflammatory microenvironment which characterize the disease as amenable target for novel therapeutic approaches.

MRI-based radiomic signature as predictive marker for patients with head and neck squamous cell carcinoma

PURPOSE

To develop magnetic resonance imaging (MRI)-based radiomic signature and nomogram for preoperatively predicting prognosis in head and neck squamous cell carcinoma (HNSCC) patients.

METHOD

This retrospective study consisted of a training cohort (n = 85) and a validation cohort (n = 85) of patients with HNSCC. LASSO Cox regression model was used to select the most useful prognostic features with their coefficients, upon which a radiomic signature was generated. The receiver operator characteristics (ROC) analysis and association of the radiomic signature with overall survival (OS) of patients was assessed in both cohorts. A nomogram incorporating the radiomic signature and independent clinical predictors was then constructed. The incremental prognostic value of the radiomic signature was evaluated.

RESULTS

The radiomic signature, consisted of 7 selected features from MR images, was significantly associated with OS of patients with HNSCC (P < 0.0001 for training cohort, P = 0.0013 for validation cohort). The radiomic signature and TNM stage were proved to be independently associated with OS of HNSCC patients, which therefore were incorporated to generate the radiomic nomogram. In the training cohort, the nomogram showed a better prognostic capability than TNM stage only (P =  0.005), which was confirmed in the validation cohort (P =  0.01). Furthermore, the calibration curves of the nomogram demonstrated good agreement with actual observation.

CONCLUSIONS

MRI-based radiomic signature is an independent prognostic factor for HNSCC patients. Nomogram based on radiomic signature and TNM stage shows promising in non-invasively and preoperatively predicting prognosis of HNSCC patient in clinical practice.

The complexity of tumor shape, spiculatedness, correlates with tumor radiomic shape features

Radiomics extracts high-throughput quantitative data from medical images to contribute to precision medicine. Radiomic shape features have been shown to correlate with patient outcomes. However, how radiomic shape features vary in function of tumor complexity and tumor volume, as well as with method used for meshing and voxel resampling, remains unknown. The aims of this study are to create tumor models with varying degrees of complexity, or spiculatedness, and evaluate their relationship with quantitatively extracted shape features. Twenty-eight tumor models were mathematically created using spherical harmonics with the spiculatedness degree d being increased by increments of 3 (d = 11 to d = 92). Models were 3D printed with identical bases of 5 cm, imaged with a CT scanner with two different slice thicknesses, and semi-automatically delineated. Resampling of the resulting masks on a 1 × 1 × 1 mm3 grid was performed, and the voxel size of each model was then calculated to eliminate volume differences. Four MATLAB-based algorithms (isosurface (M1), isosurface filter (M2), isosurface remeshing (M3), and boundary (M4)) were used to extract nine 3D features (Volume, Surface area, Surface-to-volume, Compactness1, Compactness2, Compactness3, Spherical Disproportion, Sphericity and Fractional Concavity). To quantify the impact of 3D printing, acquisition, segmentation and meshing, features were computed directly from the stereolithography (STL) file format that was used for 3D printing, and compared to those computed. Changes in feature values between 0.6 and 2 mm slice acquisitions were also compared. Spearman's rank-order correlation coefficients were computed to determine the relationship of each shape feature with spiculatedness for each of the four meshing algorithms. Percent changes were calculated between shape features extracted from the original and resampled contoured images to evaluate the influence of spatial resampling. Finally, the percent change in shape features when the volume was changed from 25% to 150% of their original volume was quantified for three distinct tumor models and compared to the percent change observed when modifying the spiculatedness of the model from d = 11 to d = 92. Values extracted using isosurface remeshing method are the closest to the STL reference ones, with mean differences less than 10.8% (Compactness2) for all features. Seven of the eight features had strong significant correlations with tumor model complexity irrespective of the meshing algorithm (r > 0.98, p < 10-4), with fractional concavity having the lowest correlation coefficient (r = 0.83, p < 10-4, M2). Comparisons of features extracted from the 0.6 and 2 mm slice thicknesses showed that mean differences were from 2.1% (Compactness3) to 12.7% (Compactness2) for the isosurface remeshing method. Resampling on a 1 × 1 × 1 mm3 grid resulted in between 1.3% (Compactness3) to 9.5% (Fractional Concavity) mean changes in feature values. Compactness2, Compactness3, Spherical Disproportion, Sphericity and Fractional Concavity were the features least affected by volume changes. Compactness1 had a 90.4% change with volume, which was greater than the change between the least and most spiculated models. This is the first methodological study that directly demonstrates the relationship of tumor spiculatedness with radiomic shape features, that also produced 3D tumor models, which may serve as reference phantoms for future radiomic studies. Surface Area, Surface-to-volume, and Spherical Disproportion had direct relationships with spiculatedness while the three formulas for Compactness, Sphericity and Fractional Concavity had inverse relationships. The features Compactness2, Compactness3, Spherical Disproportion, and Sphericity should be prioritized as these have minimal variations with volume changes, slice thickness and resampling.

Computed tomography-based texture analysis of bladder cancer: differentiating urothelial carcinoma from micropapillary carcinoma

PURPOSE

The purpose of the study is to determine the feasibility of using computed tomography-based texture analysis (CTTA) in differentiating between urothelial carcinomas (UC) of the bladder from micropapillary carcinomas (MPC) of the bladder.

METHODS

Regions of interests (ROIs) of computerized tomography (CT) images of 33 MPCs and 33 UCs were manually segmented and saved. Custom MATLAB code was used to extract voxel information corresponding to the ROI. The segmented tumors were input to a pre-existing radiomics platform with a CTTA panel. A total of 58 texture metrics were extracted using four different texture extraction techniques and statistically analyzed using a Wilcoxon rank-sum test to determine the differences between UCs and MPCs.

RESULTS

Of the 58 texture metrics extracted using the gray level co-occurrence matrix (GLCM) and gray level difference matrix (GLDM), 28 texture metrics were statistically significant (p < 0.05) for differences in tumor textures and 27 texture metrics were statistically significant (p < 0.05) for peritumoral fat textures. The remaining nine metrics extracted using histogram and fast Fourier transform analyses did not show significant differences between the textures of the tumors and their peritumoral fat.

CONCLUSIONS

CTTA shows that MPC have a more heterogeneous texture compared to UC. As visual discrimination of MPC from UC from clinical CT scans are difficult, results from this study suggest that tumor heterogeneity extracted using GLCM and GLDM may be a good imaging aid in segregating MPC from UC. This tool can aid clinicians in further sub-classifying bladder cancers on routine imaging, a process which has potential to alter treatment and patient care.

MR Imaging of Pleural Neoplasms

The pleura may be affected by primary tumors or metastatic spread of intrathoracic or extrathoracic neoplasms. Primary pleural neoplasms represent ∼10% of all pleural tumors, and malignant lesions are more common than benign lesions. The most common primary tumors include malignant pleural mesothelioma and solitary fibrous tumor. Although pleural neoplasms may initially be evaluated with computed tomography (CT) and/or fluorodeoxyglucose positron emission tomography (PET)/CT, magnetic resonance (MR) imaging is complementary to these other imaging modalities for disease staging and evaluation of patients. In this article, we discuss the etiology, clinical presentation, and imaging of pleural neoplasms, with specific attention given to the role of MR imaging.

Diagnostic approach to pleural diseases: new tricks for an old trade

The burden of pleural diseases has substantially increased in the past decade because of a rise in the incidence of pleural space infections and pleural malignancies in a patient population that is older and more immunocompromised and has more comorbidities. This complexity increasingly requires minimally invasive diagnostic options and tailored management. Implications for patients are such that the limitations of current diagnostic methods need to be addressed by multidisciplinary teams of investigators from the fields of imaging, biology, and engineering. Ignored for a long time as an epiphenomenon at the crossroad of many unrelated medical problems, pleural diseases are finally getting the attention they deserve and have spurred a vibrant and exciting field of research.