Differentiating thymoma from thymic cyst in anterior mediastinal abnormalities smaller than 3 cm

Distinguishing thymic cysts from low-risk thymomas via [18F]FDG PET/CT

BACKGROUND

Thymic cysts are a rare benign disease that needs to be distinguished from low-risk thymoma. [18F]fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) is a non-invasive imaging technique used in the differential diagnosis of thymic epithelial tumours, but its usefulness for thymic cysts remains unclear. Our study evaluated the utility of visual findings and quantitative parameters of [18F]FDG PET/CT for differentiating between thymic cysts and low-risk thymomas.

METHODS

Patients who underwent preoperative [18F]FDG PET/CT followed by thymectomy for a thymic mass were retrospectively analyzed. The visual [18F]FDG PET/CT findings evaluated were PET visual grade, PET central metabolic defect, and CT shape. The quantitative [18F]FDG PET/CT parameters evaluated were PET maximum standardized uptake value (SUVmax), CT diameter (cm), and CT attenuation in Hounsfield units (HU). Findings and parameters for differentiating thymic cysts from low-risk thymomas were assessed using Pearson's chi-square test, the Mann-Whitney U-test, and receiver operating characteristics (ROC) curve analysis.

RESULTS

Seventy patients (18 thymic cysts and 52 low-risk thymomas) were finally included. Visual findings of PET visual grade (P < 0.001) and PET central metabolic defect (P < 0.001) showed significant differences between thymic cysts and low-risk thymomas, but CT shape did not. Among the quantitative parameters, PET SUVmax (P < 0.001), CT diameter (P < 0.001), and CT HU (P = 0.004) showed significant differences. In ROC analysis, PET SUVmax demonstrated the highest area under the curve (AUC) of 0.996 (P < 0.001), with a cut-off of equal to or less than 2.1 having a sensitivity of 100.0% and specificity of 94.2%. The AUC of PET SUVmax was significantly larger than that of CT diameter (P = 0.009) and CT HU (P = 0.004).

CONCLUSIONS

Among the [18F]FDG PET/CT parameters examined, low FDG uptake (SUVmax ≤ 2.1, equal to or less than the mediastinum) is a strong diagnostic marker for a thymic cyst. PET visual grade and central metabolic defect are easily accessible findings.

Development and Validation of Contrast-Enhanced CT-Based Deep Transfer Learning and Combined Clinical-Radiomics Model to Discriminate Thymomas and Thymic Cysts: A Multicenter Study

RATIONALE AND OBJECTIVES

This study aims to evaluate the feasibility and effectiveness of deep transfer learning (DTL) and clinical-radiomics in differentiating thymoma from thymic cysts.

MATERIALS AND METHODS

Clinical and imaging data of 196 patients pathologically diagnosed with thymoma and thymic cysts were retrospectively collected from center 1. (training cohort: n = 137; internal validation cohort: n = 59). An independent external validation cohort comprised 68 thymoma and thymic cyst patients from center 2. Region of interest (ROI) delineation was performed on contrast-enhanced chest computed tomography (CT) images, and eight DTL models including Densenet 169, Mobilenet V2, Resnet 101, Resnet 18, Resnet 34, Resnet 50, Vgg 13, Vgg 16 were constructed. Radiomics features were extracted from the ROI on the CT images of thymoma and thymic cyst patients, and feature selection was performed using intra-observer correlation coefficient (ICC), Spearman correlation analysis, and least absolute shrinkage and selection operator (LASSO) algorithm. Univariate analysis and multivariable logistic regression (LR) were used to select clinical-radiological features. Six machine learning classifiers, including LR, support vector machine (SVM), k-nearest neighbors (KNN), Light Gradient Boosting Machine (LightGBM), Adaptive Boosting (AdaBoost), and Multilayer Perceptron (MLP), were used to construct Radiomics and Clinico-radiologic models. The selected features from the Radiomics and Clinico-radiologic models were fused to build a Combined model. Receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA) were used to evaluate the discrimination, calibration, and clinical utility of the models, respectively. The Delong test was used to compare the AUC between different models. K-means clustering was used to subdivide the lesions of thymomas or thymic cysts into subregions, and traditional radiomics methods were used to extract features and compare the ability of Radiomics and DTL models to reflect intratumoral heterogeneity using correlation analysis.

RESULTS

The Densenet 169 based on DTL performed the best, with AUC of 0.933 (95% CI: 0.875-0.991) in the internal validation cohort and 0.962 (95% CI: 0.923-1.000) in the external validation cohort. The AdaBoost classifier achieved AUC of 0.965 (95% CI: 0.923-1.000) and 0.959 (95% CI: 0.919-1.000) in the internal and external validation cohorts, respectively, for the Radiomics model. The LightGBM classifier achieved AUC of 0.805 (95% CI: 0.690-0.920) and 0.839 (95% CI: 0.736-0.943) in the Clinico-radiologic model. The AUC of the Combined model in the internal and external validation cohorts was 0.933 (95% CI: 0.866-1.000) and 0.945 (95% CI: 0.897-0.994), respectively. The results of the Delong test showed that the Radiomics model, DTL model, and Combined model outperformed the Clinico-radiologic model in both internal and external validation cohorts (p-values were 0.002, 0.004, and 0.033 in the internal validation cohort, while in the external validation cohort, the p-values were 0.014, 0.006, and 0.015, respectively). But there was no statistical difference in performance among the three models (all p-values <0.05). Correlation analysis showed that radiomics performed better than DTL in quantifying intratumoral heterogeneity differences between thymoma and thymic cysts.

CONCLUSION

The developed DTL model and the Combined model based on radiomics and clinical-radiologic features achieved excellent diagnostic performance in differentiating thymic cysts from thymoma. They can serve as potential tools to assist clinical decision-making, particularly when endoscopic biopsy carries a high risk.

MRI-Based Stepwise Approach to Anterior Mediastinal Cystic Lesions for Diagnosis and Further Management

As the majority of incidentally detected lesions in the anterior mediastinum is small nodules with soft tissue appearance, the differential diagnosis has typically included thymic neoplasm and prevascular lymph node, with benign cyst. Overestimation or misinterpretation of these lesions can lead to unnecessary surgery for ultimately benign conditions. nonsurgical anterior mediastinal lesions. The pitfalls of MRI evaluation for anterior mediastinal cystic lesions are as follows: first, we acknowledge the limitation of T2-weighted images for evaluating benign cystic lesions. Due to variable contents within benign cystic lesions, such as hemorrhage, T2 signal intensity may be variable. Second, owing to extensive necrosis and cystic changes, the T2 shine-through effect may be seen on diffusion-weighted images (DWI), and small solid portions might be missed on enhanced images. Therefore, both enhancement and DWI with apparent diffusion coefficient values should be considered. An algorithm will be suggested for the diagnostic evaluation of anterior mediastinal cystic lesions, and finally, a management strategy based on MRI features will be suggested.

Histological Classification and Invasion Prediction of Thymoma by Machine Learning-Based Computed Tomography Imaging

Purpose

The values of machine learning-based computed tomography (CT) imaging in histological classification and invasion prediction of thymoma were investigated.

Methods

181 patients diagnosed with thymoma by surgery or biopsy in Shantou Central Hospital between February 2017 and March 2022 were selected. According to the concept of simplified histological classification and the latest histological classification by the WHO, thymoma was divided into two groups, including low-risk (types A, AB, B1, and metaplastic type) and high-risk groups (types B2 and B3). CT images were reconstructed by filtering back projection (FBP) algorithm. CT image features were collected for statistical analysis.

Results

The main symptoms of patients diagnosed with thymoma included respiratory tract infection, chest distress and shortness of breath, and chest pain. 35.91% of them suffered from complicated myasthenia gravis. Tumor size and position in low-risk and high-risk groups showed no statistical significance (P > 0.05). Tumor morphology and boundary between the two groups suggested statistical difference (P < 0.05). Whether tumor invaded adjacent tissues was apparently correlated with simplified histological classification (P < 0.01). The sensitivity and specificity of CT images for the invasion of mediastinal pleura or pericardium were around 90% and negative predictive values both reached above 95%. Those of the CT images for lung invasion were over 80%. The negative and positive predictive values were 93.54% and 63.82%, respectively. Those of the CT images for blood vessel invasion were 67.32% and 97.93%. The negative and positive predictive values were 98.21% and 83%, respectively.

Conclusion

The machine learning-based CT image had significant values in the prediction of different histological classification and even invasion level.

CT-Based Radiomics Nomogram for Differentiation of Anterior Mediastinal Thymic Cyst From Thymic Epithelial Tumor

Objectives

This study aimed to distinguish preoperatively anterior mediastinal thymic cysts from thymic epithelial tumors via a computed tomography (CT)-based radiomics nomogram.

Methods

This study analyzed 74 samples of thymic cysts and 116 samples of thymic epithelial tumors as confirmed by pathology examination that were collected from January 2014 to December 2020. Among the patients, 151 cases (scanned at CT 1) were selected as the training cohort, and 39 cases (scanned at CT 2 and 3) served as the validation cohort. Radiomics features were extracted from pre-contrast CT images. Key features were selected by SelectKBest and least absolute shrinkage and selection operator and then used to build a radiomics signature (Rad-score). The radiomics nomogram developed herein via multivariate logistic regression analysis incorporated clinical factors, conventional CT findings, and Rad-score. Its performance in distinguishing the samples of thymic cysts from those of thymic epithelial tumors was assessed via discrimination, calibration curve, and decision curve analysis (DCA).

Results

The radiomics nomogram, which incorporated 16 radiomics features and 3 conventional CT findings, including lesion edge, lobulation, and CT value, performed better than Rad-score, conventional CT model, and the clinical judgment by radiologists in distinguishing thymic cysts from thymic epithelial tumors. The area under the receiver operating characteristic (ROC) curve of the nomogram was 0.980 [95% confidence interval (CI), 0.963–0.993] in the training cohort and 0.992 (95% CI, 0.969–1.000) in the validation cohort. The calibration curve and the results of DCA indicated that the nomogram has good consistency and valuable clinical utility.

Conclusion

The CT-based radiomics nomogram presented herein may serve as an effective and convenient tool for differentiating thymic cysts from thymic epithelial tumors. Thus, it may aid in clinical decision-making.

Role of quantitative energy spectrum CT parameters in differentiating thymic epithelial tumours and thymic cysts

AIM

To explore the utility of multiple energy spectrum computed tomography (CT) parameters in distinguishing thymic epithelial tumours (TETs) from thymic cysts among lesions <5 cm in diameter.

MATERIALS AND METHODS

Data pertaining to 56 patients with TETs and thymic cysts <5 cm in diameter were assessed retrospectively. All patients underwent surgical resection and the diagnosis was confirmed histopathologically. Thirty-five patients with TETs (average age, 51.97 years) and 21 patients with thymic cysts (average age, 50.54 years) were included. The region of interest for the lesion on the energy spectrum CT was delineated on the post-processing workstation, and multiple parameters of the energy spectrum CT were obtained. The diagnostic efficacies of the parameters were analysed using receiver operating characteristic (ROC) curves.

RESULTS

To distinguish small TETs from thymic cysts, a single-energy CT value of 60 keV showed good differential diagnostic performance in the arterial phase (cut-off value = 68.42 HU; area under the curve [AUC] = 0.978), a single-energy CT value of 70 keV showed good differential diagnostic performance in the venous phase (cut-off value = 59.77 HU; AUC = 0.956). In the arterial and venous phases, effective atomic numbers of 8.065 and 8.175, respectively, were used as cut-off values to distinguish small TETs from thymic cysts (AUC = 0.972 and AUC = 0.961, respectively). Iodine concentrations of 10.99 and 11.05 were used as cut-off values to distinguish small TETs from thymic cysts (AUC = 0.956 and AUC = 0.924, respectively).

CONCLUSION

According to the present study, energy spectrum CT parameters may have clinical value in the differential diagnosis of TETs and thymic cysts.