[Thymoma with Partial Anomalous Pulmonary Venous Drainage from the Left Upper Lobe:Report of a Case].

A 73-year-old woman presented with left anterior chest pain and back pain. Computed tomography (CT) scan showed an anterior mediastinal tumor. It also showed partial anomalous pulmonary venous drainage (left superior pulmonary vein draining into the left brachiocephalic vein), and the tumor was located near the left brachiocephalic vein. The operation was performed through a median sternotomy to resect the thymus and tumor with partial resection of the left upper lobe due to the tumor's adhesion to the left upper lobe. One of the vascular anomalies encountered in adult thoracic surgery is partial anomalous pulmonary venous drainage. It is important to recognize the presence of such an anomaly on imaging and to anticipate the surgical procedure with a preoperative surgical technique.

Thymic Imaging Pitfalls and Strategies for Optimized Diagnosis.

Thymic imaging is challenging because the imaging appearance of a variety of benign and malignant thymic conditions are similar. CT is the most commonly used modality for mediastinal imaging, while MRI and fluorine 18 fluorodeoxyglucose (FDG) PET/CT are helpful when they are tailored to the correct indication. Each of these imaging modalities has limitations and technical pitfalls that may lead to an incorrect diagnosis and mismanagement. CT may not be sufficient for the characterization of cystic thymic processes and differentiation between thymic hyperplasia and thymic tumors. MRI can be used to overcome these limitations but is subject to other potential pitfalls such as an equivocal decrease in signal intensity at chemical shift imaging, size limitations, unusual signal intensity for cysts, subtraction artifacts, pseudonodularity on T2-weighted MR images, early imaging misinterpretation, flow and spatial resolution issues hampering assessment of local invasion, and the overlap of apparent diffusion coefficients between malignant and benign thymic entities. FDG PET/CT is not routinely indicated due to some overlap in FDG uptake between thymomas and benign thymic processes. However, it is useful for staging and follow-up of aggressive tumors (eg, thymic carcinoma), particularly for detection of occult metastatic disease. Pitfalls in imaging after treatment of thymic malignancies relate to technical challenges such as postthymectomy sternotomy streak metal artifacts, differentiation of postsurgical thymic bed changes from tumor recurrence, or human error with typical "blind spots" for identification of metastatic disease. Understanding these pitfalls enables appropriate selection of imaging modalities, improves diagnostic accuracy, and guides patient treatment. ©RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material.

Making an accurate diagnosis of anterior mediastinal lesions: a proposal for a new diagnostic algorithm from the BTOG Thymic Malignancies Special Interest Group.

Due to the rising demand in cross-sectional thoracic imaging, anterior mediastinal lesions are being identified with increasing frequency. Following iterative and multidisciplinary discussions, the BTOG Thymic Malignancies Special Interest Group have developed an algorithm to standardise the diagnostic approach for these relatively uncommon but important conditions which span from benign (thymic remnant, thymic hyperplasia and thymic cysts) to suspected localised thymomas to suspected more aggressive malignancy (thymic carcinoma, lymphoma and germ cell tumours). For each condition, we provide a brief description, an overview of the key radiological findings and a description of the proposed algorithm including the rationale behind the recommendations. We also highlight the role of magnetic resonance (MR) imaging for the characterisation of anterior mediastinal masses in specific indications when the necessary local resources and expertise exist. In addition, we hope this provides the rationale for service development in MR of the anterior mediastinum where current resource and expertise requires development. Through this standardised pathway, we hope to drive improvements in patient care by rationalising surveillance schedules, avoiding unnecessary resections of benign entities with their associated morbidity and optimising the diagnostic work-up prior to the appropriate treatment of anterior mediastinal malignancies.

Resection and a rare type of reconstruction of the superior vena cava with the left brachiocephalic vein.

Resection and reconstruction of the superior vena cava (SVC) are required in a selected group of patients with anterior mediastinal tumors and lung neoplasms. We present the case of a 63-year-old woman who underwent invasive type B2 thymoma resection and a rare type of reconstruction of the superior vena cava using a patch of the left brachiocephalic vein (LBV). The various types of reconstruction of the superior vena cava are discussed.

MHD-Net: Memory-Aware Hetero-Modal Distillation Network for Thymic Epithelial Tumor Typing With Missing Pathology Modality.

Fusing multi-modal radiology and pathology data with complementary information can improve the accuracy of tumor typing. However, collecting pathology data is difficult since it is high-cost and sometimes only obtainable after the surgery, which limits the application of multi-modal methods in diagnosis. To address this problem, we propose comprehensively learning multi-modal radiology-pathology data in training, and only using uni-modal radiology data in testing. Concretely, a Memory-aware Hetero-modal Distillation Network (MHD-Net) is proposed, which can distill well-learned multi-modal knowledge with the assistance of memory from the teacher to the student. In the teacher, to tackle the challenge in hetero-modal feature fusion, we propose a novel spatial-differentiated hetero-modal fusion module (SHFM) that models spatial-specific tumor information correlations across modalities. As only radiology data is accessible to the student, we store pathology features in the proposed contrast-boosted typing memory module (CTMM) that achieves type-wise memory updating and stage-wise contrastive memory boosting to ensure the effectiveness and generalization of memory items. In the student, to improve the cross-modal distillation, we propose a multi-stage memory-aware distillation (MMD) scheme that reads memory-aware pathology features from CTMM to remedy missing modal-specific information. Furthermore, we construct a Radiology-Pathology Thymic Epithelial Tumor (RPTET) dataset containing paired CT and WSI images with annotations. Experiments on the RPTET and CPTAC-LUAD datasets demonstrate that MHD-Net significantly improves tumor typing and outperforms existing multi-modal methods on missing modality situations.

Imaging and Histopathological Features Of Primary Thymic Neuroendocrine Tumor.

OBJECTIVES: To investigate CT, MRI, and PET/CT features with histopathological findings of primary thymic neuroendocrine tumor. MATERIALS AND METHODS: All 9 cases with pathologically proven primary thymic neuroendocrine tumors were reviewed retrospectively. Among them, 7 underwent enhanced CT, 1 with MRI (enhanced) and another with PET/CT scan. Multiple characters were examined, including tumor location, contour, CT attenuation, enhancement pattern, involvement of surrounding structure and lymphadenopathy. RESULTS: Among 9 patients studied, 7 (77%) masses were located in the anterior superior mediastinum, 1 in the anterior superior-middle mediastinum, and 1 in the anterior and middle mediastinum. The maximum diameter (longitudinal) ranged from 4.2 to 23 cm (mean ± standard deviation, 9.5 cm ± 2.8). Four masses had irregular, 3 had lobulated, and 2 had smooth contours, while 8 masses had clear margins and 1 had an ill-defined margin. Six masses showed heterogeneous attenuation with necrotic/cystic component (n=5), calcification (n=2) and hemorrhage(n=1), and 3 showed homogeneous attenuation on the non-enhanced image. After contrast administration, 8 masses showed heterogeneous attenuation, and 1 showed homogeneous attenuation with tumor vessels visible in 4 masses. Among all, 8 masses showed strong enhancement, and 1 showed moderate enhancement in comparison to muscles in the anterior thoracic wall on enhanced images. Involvement of adjacent mediastinal structures was observed in 5 cases. Immunohistochemical analysis showed that the tumor cells were positive for CgA, Syn, CK, CD56 and EMA. CONCLUSION: Primary NETs are large masses located anterior superior mediastinum, irregular in contour, showing heterogeneous attenuation with necrotic/cystic component and strong heterogeneous enhancement with tumor vessels, compressing local mediastinal structures. In addition, immunohistochemical examination is required in such a diagnosis.

Advanced thymic carcinoma with a hepatic metastasis treated with chemotherapy and staged resection.

Thymic carcinoma is rare, with resulting treatment of patients with extrathoracic metastasis being on a case-by-case basis. We describe the management of a woman in her 70s with an incidentally discovered cystic hepatic lesion with confirmation of a solitary extrathoracic metastasis from a synchronous primary thymic carcinoma. Following chemotherapy and staged resection of the metastasis and the primary tumour, the patient remained free of disease on radiological surveillance 6 months postoperatively.

A diagnostic model based on 18F-FDG PET/CT parameters in improving the differential diagnosis of invasive thymic epithelial tumors and anterior mediastinal lymphomas.

BACKGROUND: Accurately distinguishing between invasive thymic epithelial tumors (TETs) and anterior mediastinal lymphoma before surgery is crucial for subsequent treatment choices. But currently, the diagnosis of invasive TET is sometimes difficult to distinguish from anterior mediastinal lymphoma. OBJECTIVE: To assess the application of fluorine-18-fluorodeoxyglucose (18F-FDG) positron emission tomography/computer tomography (PET/CT) in the differential diagnosis of TETs and anterior mediastinal lymphomas. METHODS: 18F-FDG PET/CT images of 133 invasive TETs and anterior mediastinal lymphomas patients were retrospectively analyzed. In particular, the tumor's longest diameter and maximum standardized uptake value (SUVmax) were evaluated. The SUVmax and longest diameter values of the two groups were analyzed by using the receiver operating characteristic (ROC) curve to determine the optimal threshold and diagnostic efficiency. RESULTS: Age, myasthenia gravis, SUVmax and tumor longest diameter differed significantly between invasive TETs and anterior mediastinal lymphomas patients. The tumor location, calcification, relationship with adjacent vessels and distant metastasis differed significantly between the groups. The ROC analysis showed an AUC for SUVmax and tumor longest diameter of 0.841 and 0.737. Respectively, the cutoff values with the best diagnostic performance were 9.65 (sensitivity: 77.78%, specificity: 81.97%) and 6.65 (sensitivity: 80.56%, specificity: 62.30%) for SUVmax and tumor longest diameter. The diagnostic model of SUVmax, calcification, relationship with surrounding blood vessels, lymph node metastasis and lung metastasis in the highest AUC of 0.935 (sensitivity: 90.16%, specificity: 88.89%). In addition, we incorporated splenic involvement and metastatic sub-diaphragmatic lymph node into Model 2 as a new predictive model 3 for differential diagnosis and found a significant improvement in the diagnostic performance of Model 3. CONCLUSION: The diagnostic model composed of 18F-FDG PET parameters is improving the differential diagnosis of invasive TETs and anterior mediastinal lymphomas.

Histogram analysis based on unenhanced CT for identifying thymoma and lymphoma among prevascular mediastinal incidentalomas.

OBJECTIVE: To determine whether histogram analysis based on unenhanced CT can play a role in the differential diagnosis of thymoma and lymphoma from thymic hyperplasia and cyst (mean CT attenuation > 10 HU). MATERIALS AND METHODS: This retrospective study included consecutive asymptomatic participants who have prevascular mediastinal lesions incidentally detected by unenhanced CT between December 2013 and August 2020, and with definitive diagnosis by pathology or additional radiologic work-ups. A total of thirteen histogram parameters on enhanced CT were calculated for each lesion, then were compared between tumor (thymoma + lymphoma) and non-tumor (hyperplasia + cyst). Receiver operating characteristic analysis was conducted to investigate the performance of histogram parameter for identifying tumor. RESULTS: The study population included 192 patients (106 men and 86 women) with a mean age of 50.5 years at the time of CT examination. Of them, 94 patients have tumor (87 thymomas and 7 lymphoma) and 98 have non-tumor (48 thymic hyperplasia and 50 cysts). Nine of the thirteen histogram parameters revealed significant difference between the two groups, including median, minimum, range, 10th percentile, 90th percentile, kurtosis, skewness, uniformity and entropy. No significant difference was observed in the mean CT attenuation between groups. Higher median was found to be independent predictors for distinguishing tumor from non-tumor, and can achieve an area under the curve (AUC) of 0.785 (95% confidence interval [95% IC], 0.720-0.841). CONCLUSIONS: Histogram analysis based on unenhanced CT may be able to provide some help in the differential diagnosis of incidental lesions in prevascular mediastinal. GRAND SUPPORT: This study was sponsored by Natural Science Foundation of Shanghai (No. 21ZR1459700).

CT-guided pretreatment biopsy diagnosis in patients with thymic epithelial tumours: diagnostic accuracy and risk of seeding.

AIM: To investigate the diagnostic performance of computed tomography (CT)-guided percutaneous transthoracic needle biopsy (PTNB) for thymic epithelial tumours (TETs) and the complication rate after PTNB including seeding after PTNB. MATERIALS AND METHODS: This retrospective study identified PTNBs for anterior mediastinal lesions between May 2007 and September 2021. The diagnostic performance for TETs and complications were investigated. The concordance of the histological grades of TETs between PTNB and surgery was evaluated. The factors associated with pleural seeding after PTNB were determined using Cox regression analysis. RESULTS: Of 387 PTNBs, 235 PTNBs from 225 patients diagnosed as TETs (124 thymomas and 101 thymic carcinomas) and 150 PTNBs from 133 patients diagnosed as other than TETs were included. The sensitivity, specificity, and accuracy for TETs were 89.4% (210/235), 100% (210/210), and 93.5% (360/385), respectively, with an immediate complication rate of 4.4% (17/385). The concordance rate of the histological grades between PTNB and surgery was 73.3% (77/105) after excluding uncategorised types of thymomas. During follow-up after PTNB (median duration, 38.8 months; range, 0.3-164.6 months), no tract seeding was observed. Pleural seeding was observed in 26 patients. Thymic carcinoma (hazard ratio [HR], 5.94; 95% confidence interval [CI], 2.07-17.08; p=0.001) and incomplete resection (HR, 3.29; 95% CI, 1.20-9.02; p=0.02) were associated with pleural seeding, while the biopsy approach type (transpleural versus parasternal) was not associated (p=0.12). CONCLUSIONS: Pretreatment biopsy for TETs was accurate and safe and may be considered for diagnosing TETs, particularly when the diagnosis is challenging and histological diagnosis is mandatory.

Contrast-enhanced CT-based radiomics differentiate anterior mediastinum lymphoma from thymoma without myasthenia gravis and calcification.

AIM: To explore the value of a radiomics model based on enhanced computed tomography (CT) in differentiating anterior mediastinal lymphoma (AML) and thymoma without myasthenia gravis (MG) and calcification. MATERIALS AND METHODS: The present study analysed patients who were diagnosed histologically with AML and thymoma in three independent institutions. All pre-treatment patients underwent enhanced CT. In the training group of patients from institutions 1 (the First Affiliated Hospital of Kunming Medical University) and 3 (the Yunnan Cancer Hospital), two radiologists independently analysed the enhanced CT images and performed manual segmentation of each tumour. Radiomics features were screened using interobserver interclass coefficient (ICC) analysis, feature correlation analysis, and L1 regularisation. The discriminative efficacy of the logistic regression model was evaluated using receiver operating characteristic (ROC) analysis. Validation group of patients from institution 2 (the Second Affiliated Hospital of Zhejiang University School of Medicine) was used to validate the proposed models. RESULTS: A total of 114 patients were enrolled in this study and 1,743 radiomics features were extracted from the enhanced CT images. After feature screening, the remaining 37 robust radiomics features were used to construct the model. In the training group, the AUC of the model was 0.987 (95% confidence interval [CI]: 0.976-0.999), the sensitivity, specificity, and accuracy were 0.912, 0.946, and 0.924, respectively. In the validation group, the AUC of the model was 0.798 (95% CI: 0.683-0.913), the sensitivity, specificity, and accuracy were 0.760, 0.700, and 0.743, respectively. CONCLUSION: The radiomics model created provided effective information to assist in the selection of clinical strategies, thus reducing unnecessary procedures in patients with AML and guiding direct surgery in patients with thymoma to avoid biopsy.

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.

Lobulated tumor contour as a predictor of preoperative tumor invasion of the lung or pericardium in thymoma patients.

PURPOSE: Preoperative investigations to detect invasion to neighboring organs are important for deciding on the most appropriate surgical approach for thymoma. We evaluated preoperative computed tomography (CT) findings in thymoma patients to identify the CT features associated with tumor invasion. METHODS: Clinicopathologic information on 193 patients who underwent surgical resection for thymoma at Chiba University Hospital between 2002 and 2016 was collected retrospectively. The surgical pathology identified invasion of thymoma in 35 patients: in the lung (n = 18), pericardium (n = 11), or both (n = 6). Contact lengths between the tumor contour and lung (CLTL) or pericardium (CLTP) were measured at the maximum section of the tumor on axial CT. Univariate and multivariate analyses were performed to analyze the relationship between pathological invasion of the lung or pericardium and the clinicopathologic features. RESULTS: The mean CLTL and CLTP were significantly longer in patients with invasion of the neighboring organs than in those without invasion. A lobulated tumor contour was identified in 95.6% of the patients with invasion of the neighboring organs. A multivariate analysis revealed that a lobulated tumor contour was significantly associated with both lung and pericardial invasion. CONCLUSIONS: A lobulated tumor contour was significantly associated with lung and/or pericardial invasion in thymoma patients.

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.

Review of clinical and diagnostic imaging of the thymus: from age-related changes to thymic tumors and everything in between.

The thymus, a primary lymphoid organ of the immune system, undergoes several changes due to a variety of reasons, ranging from aging to pathological conditions. These changes can make distinguishing between benign and neoplastic changes in the thymus challenging, thereby complicating the histopathological diagnoses of thymic tumors. Moreover, most patients with thymic tumors are asymptomatic at the time of diagnosis. Therefore, imaging plays an extremely important role in the evaluation of thymic lesions. In this review, we introduced the imaging characteristics of the thymus, ranging from benign findings, such as normal maturation and benign lesions, to neoplasms.