Dual-energy CT perfusion imaging for differentiating WHO subtypes of thymic epithelial tumors

Accuracy and Precision of Iodine Quantification in Subtracted Micro-Computed Tomography: Effect of Reconstruction and Noise Removal Algorithms

PURPOSE

To evaluate the effect of reconstruction and noise removal algorithms on the accuracy and precision of iodine concentration (CI) quantified with subtracted micro-computed tomography (micro-CT).

PROCEDURES

Two reconstruction algorithms were evaluated: a filtered backprojection (FBP) algorithm and a simultaneous iterative reconstruction technique (SIRT) algorithm. A 3D bilateral filter (BF) was used for noise removal. A phantom study evaluated and compared the image quality, and the accuracy and precision of CI in four scenarios: filtered FBP, filtered SIRT, non-filtered FBP, and non-filtered SIRT. In vivo experiments were performed in an animal model of chemically-induced mammary cancer.

RESULTS

Linear relationships between the measured and nominal CI values were found for all the scenarios in the phantom study (R2 > 0.95). SIRT significantly improved the accuracy and precision of CI compared to FBP, as given by their lower bias (adj. p-value = 0.0308) and repeatability coefficient (adj. p-value < 0.0001). Noise removal enabled a significant decrease in bias in filtered SIRT images only; non-significant differences were found for the repeatability coefficient. The phantom and in vivo studies showed that CI is a reproducible imaging parameter for all the scenarios (Pearson r > 0.99, p-value < 0.001). The contrast-to-noise ratio showed non-significant differences among the evaluated scenarios in the phantom study, while a significant improvement was found in the in vivo study when SIRT and BF algorithms were used.

CONCLUSIONS

SIRT and BF algorithms improved the accuracy and precision of CI compared to FBP and non-filtered images, which encourages their use in subtracted micro-CT imaging.

The thymus: what's normal and what's not? Problem-solving with MRI

Anterior mediastinal masses can be difficult to characterise on computed tomography (CT) due to the wide spectrum of normal appearances of thymic tissue as well as the challenge of differentiating between benign and malignant pathologies. Additionally, attenuation of cystic mediastinal lesions can be misinterpreted on CT due to varying attenuation values. Anecdotally, non-vascular magnetic resonance imaging (MRI) of the thorax is underutilised across radiology departments in the UK, but has been shown to improve diagnostic certainty and reduce unnecessary surgical intervention. T2-weighted MRI is useful in confirming the cystic nature of lesions, whereas chemical shift techniques can be utilised to document the presence of macroscopic and intra-cellular fat and thus help distinguish between benign and malignant pathologies. In this review article, we present a practical approach to using MRI for the characterisation of anterior mediastinal lesions based on our clinical experience in a UK district general hospital.

The maximal contrast-enhanced range of CT for differentiating the WHO pathological subtypes and risk subgroups of thymic epithelial tumors

OBJECTIVE

To explore the value of maximal contrast-enhanced (CEmax) range using contrast-enhanced CT (CECT) imaging in differentiating the pathological subtypes and risk subgroups of thymic epithelial tumors (TETs).

METHODS

The pre-treatment-CECT images of 319 TET patients from May 2012 to November 2021 were analyzed retrospectively. The CEmax was defined as the maximum difference between the CT value of the solid tumor on pre-contrast and contrast-enhanced images. The mean CEmax value was calculated at three different tumor levels.

RESULTS

There was a significant difference in the CEmax among the eight main pathological subtypes [types A, AB, B1, B2, and B3 thymoma, thymic carcinoma (TC), low-grade neuroendocrine tumor (NET) and high-grade NET] (p < 0.001). Among the eight subtypes, the CEmax values of types A, AB, and low-risk NET were higher than those of the other subtypes (all p < 0.001), and there was no difference among types B1-B3 and high-risk NET (all p > 0.05). There was no difference for CEmax values between NET and TC (p = 0.491). For the risk subgroups, the CEmax of TC (including NET) was 35.35 ± 11.41 HU, which was lower than that of low-risk thymoma (A and AB) (57.73±21.24 HU) (P < 0.001) and was higher than that of high-risk thymoma (B1-B3) (27.37±8.27 HU) (P < 0.001). The CEmax cut-off values were 38.5 HU and 30.5 HU respectively (AUC: 0.829 and 0.712; accuracy, 72.4% and 67.7%).

CONCLUSION

The tumor CEmax on CECT helps differentiate the pathological subtypes and risk subgroups of TETs.

ADVANCES IN KNOWLEDGE

In this study, an improved simplified risk grouping method was proposed based on the traditional (2004 edition) simplified risk grouping method for TETs. If Type B1 thymoma is classified as high-risk, radiologists using this improved method may improve the accuracy in differentiating risk level of TETs compared with the traditional method.

Thoracic Diseases: Technique and Applications of Dual-Energy CT

Dual-energy computed tomography (DECT) is one of the most promising technological innovations made in the field of imaging in recent years. Thanks to its ability to provide quantitative and reproducible data, and to improve radiologists' confidence, especially in the less experienced, its applications are increasing in number and variety. In thoracic diseases, DECT is able to provide well-known benefits, although many recent articles have sought to investigate new perspectives. This narrative review aims to provide the reader with an overview of the applications and advantages of DECT in thoracic diseases, focusing on the most recent innovations. The research process was conducted on the databases of Pubmed and Cochrane. The article is organized according to the anatomical district: the review will focus on pleural, lung parenchymal, breast, mediastinal, lymph nodes, vascular and skeletal applications of DECT. In conclusion, considering the new potential applications and the evidence reported in the latest papers, DECT is progressively entering the daily practice of radiologists, and by reading this simple narrative review, every radiologist will know the state of the art of DECT in thoracic diseases.

Feasibility of iodine concentration and extracellular volume fraction measurement derived from the equilibrium phase dual-energy CT for differentiating thymic epithelial tumors

PURPOSE

To assess the diagnostic feasibility of iodine concentration (IC) and extracellular volume (ECV) fraction measurement using the equilibrium phase dual-energy CT (DECT) for the evaluation of thymic epithelial tumors (TETs).

MATERIALS AND METHODS

This study included 33 TETs (11 low-risk thymomas, 11 high-risk thymomas, and 11 thymic carcinomas) that were assessed by pretreatment DECT. IC was measured during the equilibrium phases and ECV fraction was calculated using IC of the thymic lesion and the aorta. IC and ECV fraction were compared among TET subtypes using the Kruskal-Wallis H test and Mann-Whitney U test. Receiver-operating characteristic (ROC) curve analysis was performed to evaluate the ability of IC and ECV fraction to diagnose thymic carcinoma.

RESULTS

IC during the equilibrium phase and ECV fraction differed among the three TET groups (both p < 0.001). IC during the equilibrium phase and ECV fraction was significantly higher in thymic carcinomas than in thymomas (1.9 mg/mL vs. 1.2 mg/mL, p < 0.001; 38.2% vs. 25.9%, p < 0.001; respectively). The optimal cutoff values of IC during the equilibrium phase and of ECV fraction to diagnose thymic carcinoma were 1.5 mg/mL (AUC, 0.955; sensitivity, 100%; specificity, 90.9%) and 26.8% (AUC, 0.888; sensitivity, 100%; specificity, 72.7%), respectively.

CONCLUSION

IC and ECV fraction measurement using DECT are helpful in diagnosing TETs. High IC during the equilibrium phase and high ECV fraction are suggestive of thymic carcinoma.

Application of a combined radiomics nomogram based on CE-CT in the preoperative prediction of thymomas risk categorization

Objective

This study aimed to establish a combined radiomics nomogram to preoperatively predict the risk categorization of thymomas by using contrast-enhanced computed tomography (CE-CT) images.

Materials and Methods

The clinical, pathological, and CT data of 110 patients with thymoma (50 patients with low-risk thymomas and 60 patients with high-risk thymomas) collected in our Hospital from July 2017 to March 2022 were retrospectively analyzed. The study subjects were randomly divided into the training set (n = 77) and validation set (n = 33) in a 7:3 ratio. Radiomics features were extracted from the CT images, and the least absolute shrinkage and selection operator (LASSO) algorithm was performed to select 13 representative features. Five models, including logistic regression (LR), support vector machine (SVM), random forest (RF), decision tree (DT), and gradient boosting decision tree (GBDT) were constructed to predict thymoma risks based on these features. A combined radiomics nomogram was further established based on the clinical factors and radiomics scores. The performance of the models was evaluated using receiver operating characteristic (ROC) curve, DeLong tests, and decision curve analysis.

Results

Maximum tumor diameter and boundary were selected to build the clinical factors model. Thirteen features were acquired by LASSO algorithm screening as the optimal features for machine learning model construction. The LR model exhibited the highest AUC value (0.819) among the five machine learning models in the validation set. Furthermore, the radiomics nomogram combining the selected clinical variables and radiomics signature predicted the categorization of thymomas at different risks more effectively (the training set, AUC = 0.923; the validation set, AUC = 0.870). Finally, the calibration curve and DCA were utilized to confirm the clinical value of this combined radiomics nomogram.

Conclusion

We demonstrated the clinical diagnostic value of machine learning models based on CT semantic features and the selected clinical variables, providing a non-invasive, appropriate, and accurate method for preoperative prediction of thymomas risk categorization.

Dual-energy CT perfusion imaging for differentiating invasive thymomas, thymic carcinomas, and lymphomas in adults

AIM

To evaluate the role of dual-energy computed tomography perfusion (DECTP) imaging in differentiating invasive thymomas (ITs), thymic cancers (TCs), and lymphomas in adults.

MATERIALS AND METHODS

Ninety-five patients with solid masses in the prevascular mediastinum who underwent DECTP examinations were enrolled in this study. The perfusion parameters (blood flow, BF; blood volume, BV; mean transit time, MTT; permeability surface, PS) and spectral parameters (water concentration, WC; iodine concentration, IC; normalised iodine concentration, NIC; the slope of spectral radiodensity [Hounsfield units] curve, λ_HU) of the lesions were analysed.

RESULTS

There were no differences in the MTT or WC values among ITs, TCs, and lymphomas (all p>0.05). The IC, NIC, and λ_HU values in the optimal arterial and venous phases and PS values of TCs were higher than those of ITs and lymphomas (all p<0.05), and there were no differences between ITs and lymphomas (all p>0.05). The BF and BV values of lymphomas were lower than those of ITs and TCs (all p<0.05), and there were no differences between ITs and TCs (all p>0.05). The cut-off values for BF and BV used to differentiate lymphomas from ITs and TCs were 42.83 ml/min/100 g and 4.66 ml/100 g, respectively (area under the receiver operating characteristic curve: 0.847 and 0.839; sensitivity, 80.6% and 82.1%; specificity, 75% and 71.4%; accuracy, 78.9% and 81.1%).

CONCLUSIONS

The perfusion and spectral parameters of DECTP imaging help to identify ITs, TCs, and lymphomas, and BF and BV values help to differentiate lymphomas from ITs and TCs.

Contrast-enhanced CT-based radiomics model for differentiating risk subgroups of thymic epithelial tumors

Background

To validate a contrast-enhanced CT (CECT)-based radiomics model (RM) for differentiating various risk subgroups of thymic epithelial tumors (TETs).

Methods

A retrospective study was performed on 164 patients with TETs who underwent CECT scans before treatment. A total of 130 patients (approximately 79%, from 2012 to 2018) were designated as the training set, and 34 patients (approximately 21%, from 2019 to 2021) were designated as the testing set. The analysis of variance and least absolute shrinkage and selection operator algorithm methods were used to select the radiomics features. A logistic regression classifier was constructed to identify various subgroups of TETs. The predictive performance of RMs was evaluated based on receiver operating characteristic (ROC) curve analyses.

Results

Two RMs included 16 and 13 radiomics features to identify three risk subgroups of traditional risk grouping [low-risk thymomas (LRT: Types A, AB and B1), high-risk thymomas (HRT: Types B2 and B3), thymic carcinoma (TC)] and improved risk grouping [LRT* (Types A and AB), HRT* (Types B1, B2 and B3), TC], respectively. For traditional risk grouping, the areas under the ROC curves (AUCs) of LRT, HRT, and TC were 0.795, 0.851, and 0.860, respectively, the accuracy was 0.65 in the training set, the AUCs were 0.621, 0.754, and 0.500, respectively, and the accuracy was 0.47 in the testing set. For improved risk grouping, the AUCs of LRT*, HRT*, and TC were 0.855, 0.862, and 0.869, respectively, and the accuracy was 0.72 in the training set; the AUCs were 0.778, 0.716, and 0.879, respectively, and the accuracy was 0.62 in the testing set.

Conclusions

CECT-based RMs help to differentiate three risk subgroups of TETs, and RM established according to improved risk grouping performed better than traditional risk grouping.

CT and MRI findings of cystic renal cell carcinoma: comparison with cystic collecting duct carcinoma

BACKGROUND

Cystic renal cell carcinoma (CRCC) and cystic collecting duct carcinoma (CCDC) share similar oncogeni and some imaging findings. The aim of this study was to characterize the clinical and CT imagings features of CRCC and CCDC.

METHODS

Thirty-three patients with CRCC and thirteen patients with CCDC with pathologically proven were retrospectively studied. Tumor characteristics were assessed.

RESULTS

On CT imaging, 33 patients(100 %) with CRCC and 13 patients(100 %) with CCDC, tumors calcifications (8 vs. 9, P < 0.0001), had a clear boundary (capsule sign, 30 vs. 2, P < 0.0001), infiltrative appearance (1 vs. 13, P < 0.0001), exogenous appearance (29 vs. 3, P < 0.0001), invaded the renal pelvis or ureter (1 vs. 10, P < 0.0001), hemorrhage (1 vs. 10, P < 0.0001), had retroperitoneal lymph node or distant metastasis (2 vs. 10, P < 0.0001), thickened enhancing internal septations (31 vs. 2, P < 0.0001), and mural soft-tissue nodules (21 vs. 1, P < 0.0001). On MR imaging,13 patients(39 %) with CRCC and 4 patients(31 %) with CCDC, all CRCCs appeared hypointense on T1-weighted images and hyperintense on T2-weighted images, however, all CCDCs appeared hypointense on T1-weighted images and hypointense on T2-weighted images(P < 0.0001). 33 patients with CRCC, they were all alive from3 years to 10 years follow-up, however, 13 patients with CCDC, of which 11 patients were able to be followed up, and 9 patients expired within 5 years of the initial diagnosis and the others are currently still alive.

CONCLUSIONS

Distinguishing features of CRCC and CCDC included calcifications, capsule signs, infiltrative appearance, metastasis, internal septations, mural nodules and signal on CT or MR images. These imaging features may help in differentiating the two renal tumor types.

Advancement in Diagnostic Imaging of Thymic Tumors

Simple Summary

Diagnostic imaging is pivotal for the diagnosis and staging of thymic tumors. It is important to distinguish thymoma and other tumor histotypes amenable to surgery from lymphoma. Furthermore, in cases of thymoma, it is necessary to differentiate between early and advanced disease before surgery since patients with locally advanced tumors require neoadjuvant chemotherapy for improving survival. This review aims to provide to radiologists a full spectrum of findings of thymic neoplasms using traditional and innovative imaging modalities.

Abstract

Thymic tumors are rare neoplasms even if they are the most common primary neoplasm of the anterior mediastinum. In the era of advanced imaging modalities, such as functional MRI, dual-energy CT, perfusion CT and radiomics, it is possible to improve characterization of thymic epithelial tumors and other mediastinal tumors, assessment of tumor invasion into adjacent structures and detection of secondary lymph nodes and metastases. This review aims to illustrate the actual state of the art in diagnostic imaging of thymic lesions, describing imaging findings of thymoma and differential diagnosis.

Fat content quantification using dual-energy CT for differentiation of anterior mediastinal lesions from normal or hyperplastic thymus

BACKGROUND

Detection of fat content in thymic lesions is crucial to differentiate thymic hyperplasia from thymic tumors or other anterior mediastinal pathologies.

PURPOSE

To assess the feasibility of dual-energy CT (DECT) fat content quantification for the differentiation of anterior mediastinal lesions from benign thymic lesions and the normal spectrum of the thymus.

MATERIALS AND METHODS

Chest DECT images of 465 patients (median 61 years, 63% female) were visually evaluated by two radiologists and semiquantitatively scored based on the degree of fatty degeneration ranging from completely fatty (score 0) to predominantly soft-tissue (score 3), and anterior mediastinal mass (score 4). A subset of scans (n =134 including all cases with scores 2-4 and 20 randomly-selected cases from scores 0 and 1) underwent quantitative DECT analysis (fat fraction, iodine density, and conventional CT value). DECT values were compared across the semiquantitative scores.

RESULTS

Results of visual evaluation included 35 with predominantly solid thymus (score 3) and 15 with anterior mediastinal mass (score 4). The most common clinical diagnoses of the 15 masses (including 8 with pathologic confirmation) were metastases (n = 10) and lymphoma (n = 4). CT values in the abnormal thymus were significantly higher than those in score 3 (median: 69.7 HU versus 19.9 HU, P <0.001). There was no significant difference in iodine density values (median: 1.7 mg/ml versus 1 mg/ml, P = 0.09). However, the fat fraction value was significantly lower in the abnormal thymus (score 4) than in the predominantly soft-tissue attenuation thymuses (score 3) (median: 12.8% versus 38.7%, P <0.001). ROC curve analysis showed that fat fraction had an AUC of 0.96 (P <0.001), with a cutoff of <39.2% fat fraction yielding 100% sensitivity and 85% specificity.

CONCLUSION

DECT fat fraction measurements of the thymus may provide additional value in distinguishing anterior mediastinal lesions from benign thymus. Use of DECT may reduce the need for subsequent imaging evaluation.

Quantitative Imaging Parameters of Contrast-Enhanced Micro-Computed Tomography Correlate with Angiogenesis and Necrosis in a Subcutaneous C6 Glioma Model

The aim of this work was to systematically obtain quantitative imaging parameters with static and dynamic contrast-enhanced (CE) X-ray imaging techniques and to evaluate their correlation with histological biomarkers of angiogenesis in a subcutaneous C6 glioma model. Enhancement (E), iodine concentration (CI), and relative blood volume (rBV) were quantified from single- and dual-energy (SE and DE, respectively) micro-computed tomography (micro-CT) images, while rBV and volume transfer constant (Ktrans) were quantified from dynamic contrast-enhanced (DCE) planar images. CI and rBV allowed a better discernment of tumor regions from muscle than E in SE and DE images, while no significant differences were found for rBV and Ktrans in DCE images. An agreement was found in rBV for muscle quantified with the different imaging protocols, and in CI and E quantified with SE and DE protocols. Significant strong correlations (Pearson r > 0.7, p < 0.05) were found between a set of imaging parameters in SE images and histological biomarkers: E and CI in tumor periphery were associated with microvessel density (MVD) and necrosis, E and CI in the complete tumor with MVD, and rBV in the tumor periphery with MVD. In conclusion, quantitative imaging parameters obtained in SE micro-CT images could be used to characterize angiogenesis and necrosis in the subcutaneous C6 glioma model.