[Concurrent Thymoma and Mature Teratoma in the Mediastinum].

A 41-year-old asymptomatic male with no significant medical history had a heterogenous cystic tumor with a diameter of 5.1 cm containing fatty density in the anterior mediastinum and a nearby homogeneous enhancing nodule with a diameter of 2.0 cm were observed on chest computed tomography( CT). A malignant teratoma with mediastinal lymph node metastasis was suspected preoperatively. The tumor was completely removed via median sternotomy, with concomitant resection of the lung, pericardium, and right phrenic nerve. Postoperative pathological examination revealed a large mature cystic teratoma, 6.0 cm in diameter, and a small nodule, 3.7 cm in diameter, diagnosed as stageⅠ, type B2 thymoma. The postoperative course was uneventful, with no recurrence 30 months later. The simultaneous occurrence of mature teratoma and stageⅠthymoma is extremely rare. When suspecting a teratoma with small satellite nodules preoperatively, consideration of concurrent small thymoma is suggested.

Minimally invasive thoracoscopic resection of a micronodular thymoma with lymphoid stroma via a subxiphoid single-incision approach: A case report.

RATIONALE: This study aims to present a novel surgical approach for the resection of anterior mediastinal tumors, specifically focusing on micronodular thymoma with lymphoid stroma (MNT), a rare and distinct variant of thymoma. The single subxiphoid incision technique, although reported in limited cases, offers a minimally invasive option with potential benefits. We report the case of a 76-year-old male who underwent this innovative procedure and was diagnosed with MNT, providing insight into the management and outcomes of this rare pathology. PATIENT CONCERNS: The patient presented for the excision of an anterior mediastinal tumor, with the surgery facilitated by sternal hooks to improve visualization. The rarity of MNT and its unclear prognosis underscore the need for enhanced diagnostic accuracy and tailored treatment strategies. DIAGNOSES: Initially diagnosed preoperatively with a thymic cyst, the patient's final diagnosis was revised to MNT following surgery, highlighting the diagnostic challenges associated with this rare tumor. INTERVENTIONS: The tumor was successfully removed using minimally invasive thoracoscopic surgery through a subxiphoid single-incision, demonstrating the feasibility and potential advantages of this approach. OUTCOMES: The patient had a favorable postoperative course, with a swift recovery and no complications, and remained in good health without signs of relapse at the 9-month follow-up. LESSONS: This case underscores the importance of recognizing the unique pathological features of MNT and the need for a cautious diagnostic approach to differentiate it from other cystic lesions. Additionally, the successful use of single-port thoracoscopy under the xiphoid process for the removal of thymic tumors suggests its potential as an effective surgical method for these challenging cases.

Differentiating low-risk thymomas from high-risk thymomas: preoperative radiomics nomogram based on contrast enhanced CT to minimize unnecessary invasive thoracotomy.

BACKGROUND: This study was designed to develop a combined radiomics nomogram to preoperatively predict the risk categorization of thymomas based on contrast-enhanced computed tomography (CE-CT) images. MATERIALS: The clinical and CT data of 178 patients with thymoma (100 patients with low-risk thymomas and 78 patients with high-risk thymomas) collected in our hospital from March 2018 to July 2023 were retrospectively analyzed. The patients were randomly divided into a training set (n = 125) and a validation set (n = 53) in a 7:3 ratio. Qualitative radiological features were recorded, including (a) tumor diameter, (b) location, (c) shape, (d) capsule integrity, (e) calcification, (f) necrosis, (g) fatty infiltration, (h) lymphadenopathy, and (i) enhanced CT value. Radiomics features were extracted from each CE-CT volume of interest (VOI), and the least absolute shrinkage and selection operator (LASSO) algorithm was performed to select the optimal discriminative ones. A combined radiomics nomogram was further established based on the clinical factors and radiomics scores. The differentiating efficacy was determined using receiver operating characteristic (ROC) analysis. RESULTS: Only one clinical factor (incomplete capsule) and seven radiomics features were found to be independent predictors and were used to establish the radiomics nomogram. In differentiating low-risk thymomas (types A, AB, and B1) from high-risk ones (types B2 and B3), the nomogram demonstrated better diagnostic efficacy than any single model, with the respective area under the curve (AUC), accuracy, sensitivity, and specificity of 0.974, 0.921, 0.962 and 0.900 in the training cohort, 0.960, 0.892, 0923 and 0.897 in the validation cohort, respectively. The calibration curve showed good agreement between the prediction probability and actual clinical findings. CONCLUSIONS: The nomogram incorporating clinical factors and radiomics features provides additional value in differentiating the risk categorization of thymomas, which could potentially be useful in clinical practice for planning personalized treatment strategies.

RadioGraphics Update: The Ninth Edition TNM Staging System for Thymic Epithelial Tumors.

Editor's Note.-RadioGraphics Update articles supplement or update information found in full-length articles previously published in RadioGraphics. These updates, written by at least one author of the previous article, provide a brief synopsis that emphasizes important new information such as technological advances, revised imaging protocols, new clinical guidelines involving imaging, or updated classification schemes.

Predicting the risk category of thymoma with machine learning-based computed tomography radiomics signatures and their between-imaging phase differences.

The aim of this study was to develop a medical imaging and comprehensive stacked learning-based method for predicting high- and low-risk thymoma. A total of 126 patients with thymomas and 5 patients with thymic carcinoma treated at our institution, including 65 low-risk patients and 66 high-risk patients, were retrospectively recruited. Among them, 78 patients composed the training cohort, while the remaining 53 patients formed the validation cohort. We extracted 1702 features each from the patients' arterial-, venous-, and plain-phase images. Pairwise subtraction of these features yielded 1702 arterial-venous, arterial-plain, and venous-plain difference features each. The Mann‒Whitney U test and least absolute shrinkage and selection operator (LASSO) and SelectKBest methods were employed to select the best features from the training set. Six models were built with a stacked learning algorithm. By applying stacked ensemble learning, three machine learning algorithms (XGBoost, multilayer perceptron (MLP), and random forest) were combined by XGBoost to produce the the six basic imaging models. Then, the XGBoost algorithm was applied to the six basic imaging models to construct a combined radiomic model. Finally, the radiomic model was combined with clinical information to create a nomogram that could easily be used in clinical practice to predict the thymoma risk category. The areas under the curve (AUCs) of the combined radiomic model in the training and validation cohorts were 0.999 (95% CI 0.988-1.000) and 0.967 (95% CI 0.916-1.000), respectively, while those of the nomogram were 0.999 (95% CI 0.996-1.000) and 0.983 (95% CI 0.990-1.000). This study describes the application of CT-based radiomics in thymoma patients and proposes a nomogram for predicting the risk category for this disease, which could be advantageous for clinical decision-making for affected patients.

[Intraspinal Metastasis of Thymic Carcinoma:Report of One Case].

Intraspinal metastasis from malignant carcinomas in other body parts is rarely reported.Intraspinal metastases are often epidural,with primary tumors mostly from the lung and prostate.The extramedullary subdural metastasis of thymic carcinoma is particularly rare and prone to misdiagnosis due to overlapping imaging features with primary intraspinal tumors.This article reports one case of intraspinal metastasis of thymic carcinoma,with the main diagnostic clues including a history of thymic carcinoma,fast growth rate,and irregular shape.

Primary glomus tumor of the thymus in a 66-year-old patient.

We report a unique case of a 66-year-old man who was incidentally identified to have a mass in the thymus region by computerized tomography scan. CT revealed a well-defined 1.6 × 1 × 0.9 cm thymus mass with moderate uniform enhancement. Thoracoscopic thymectomy was performed, and the pathological diagnosis was primary glomus tumor of the thymus. There were no atypia or malignant histological features, and no primary tumors in other sites. To our knowledge, this is the first case of primary thymic glomus tumor reported in the literature.

Tumor habitat and peritumoral region evolution-based imaging features to assess risk categorization of thymomas.

AIM: To develop an aggregate model that integrated clinical data, habitat characteristics, and intratumoral and peritumoral features to assess the risk categorization of thymomas. MATERIALS AND METHODS: We retrospectively analyzed 140 thymoma patients (70 low-risk and 70 high-risk), including pathological data. The patients were randomly divided into training cohort (n = 114) and test cohort (n = 26). The k-means clustering was utilized to partition the primary tumor into habitats based on intratumoral radiomic features, 6 distinct habitats were identified. By expanding the region of interest (ROI) mask, 2 peritumoral regions were obtained. Finally, 7 clinical characteristics, 3 habitat values, 20 radiomic features were utilized to develop an aggregated model, to predict the risk of thymoma. Shapley additive explanations (SHAP) interpretation was used for features importance ranking. The accuracy and area under curve (AUC) were used to analyze the performance of the models. RESULTS: The aggregated model, which utilized the XGBoost classifier, demonstrated the best performance with an AUC of 0.811 and an accuracy of 0.769. In comparison, the radiomic model produced an AUC of 0.654 and an accuracy of 0.692. Additionally, the Intratumoral + peritumoral model exhibited an AUC of 0.728 and an accuracy of 0.769. CONCLUSION: Our study establishes a novel tool to predict the risk of thymoma with a good performance. If prospectively validated, the model may refine thymoma patient selection for risk-adaptative therapy and improve prognosis.

Intrathyroidal thymic carcinoma.

A young woman in her early 30s presented with a right thyroid mass and progressive hoarseness due to a right vocal cord palsy. The preoperative fine-needle aspiration cytology was classified as Bethesda V and she underwent a total thyroidectomy and neck dissection. Intraoperatively, the thyroid mass was adherent to the oesophagus, trachea and encasing the right recurrent laryngeal nerve which was sacrificed. Final histopathology diagnosed a rare subtype of thyroid cancer known as intrathyroidal thymic carcinoma (ITC). She was then sent for adjuvant radiotherapy after a multidisciplinary tumour board discussion. This case report highlights the difficulty in preoperative diagnosis of ITC and the importance of immunohistochemical staining in clinching the diagnosis. In view of its rarity, there have been no published consensus on the treatment of ITC, hence we would like to share some learning points through a comprehensive literature review.

Ectopic cervical thymoma: a rare entity in the anterior neck.

Thymoma is a rare malignancy with usual location in the antero-superior mediastinum. Ectopic cervical thymoma (ECT) is an extremely rare tumor that originates from ectopic tissue, and is caused by the aberrant migration of the embryonic thymus. Our patient was a 56-year-old man who had a nodular lesion in the neck for several years. Computed tomography and Enhanced magnetic resonance imaging were performed. He underwent surgery, and a histological examination resulted in a diagnosis of type AB thymoma.

[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.

Late coronary artery injury following chemoradiotherapy for thymic carcinoma: a case report.

INTRODUCTION: Surgery remains the primary treatment modality for thymic carcinoma, with adjuvant radiotherapy being recommended to effectively mitigate local recurrence and metastasis rates subsequent to incomplete or complete resection. Chemoradiotherapy has the potential to induce coronary artery occlusion, thereby potentially impacting patients' long-term survival rates. The existing literature currently lacks comprehensive research on the lesion characteristics of coronary artery injury resulting from chemoradiotherapy. CASE PRESENTATION: The male patient, aged 55, was admitted to the hospital due to recurrent chest tightness and pain persisting for one week. Notably, the patient had previously undergone curative resection surgery for thymic carcinoma seven years ago. After the surgical procedure, the patient underwent a course of adjuvant chemotherapy comprising docetaxel and platinum. 11 months later, imaging examination diagnosed tumor recurrence, and concurrent chemoradiotherapy was administered at a total dose of 62 Gy/31F for planning gross target volume (PGTV) and 54 Gy/31F for planning target volume (PTV) with 2 cycles of paclitaxel and cisplatin. Re-admission of the patient occurred after a 7-year interval subsequent to the completion of concurrent chemoradiotherapy, leading to a subsequent diagnosis of acute non-ST segment elevation myocardial infarction. Following administration of antiplatelet, anticoagulant, and anti-myocardial ischemia therapy, coronary angiography revealed the presence of a bifurcation lesion at the distal end of the left main trunk. Intravascular ultrasound (IVUS) examination demonstrated significant negative remodeling of both the main trunk and its branches at the bifurcation site, characterized by minimal atherosclerotic plaque components. CONCLUSIONS: Chemoradiotherapy may induce damage to endothelial cells, resulting in an inflammatory response. Negative remodeling of blood vessels is likely to occur, primarily characterized by vasoconstriction but with less atherosclerotic plaque burden. Routine stent implantation in negatively remodeled areas may lead to vascular rupture, necessitating intravascular imaging examination.

Intratumoral metabolic heterogeneity by 18F-FDG PET/CT to predict prognosis for patients with thymic epithelial tumors.

BACKGROUND: The aim of the present study was to evaluate the impact of intratumoral metabolic heterogeneity and quantitative 18F-FDG PET/CT imaging parameters in predicting patient outcomes in thymic epithelial tumors (TETs). METHODS: This retrospective study included 100 patients diagnosed with TETs who underwent pretreatment 18F-FDG PET/CT. The maximum and mean standardized uptake values (SUVmax and SUVmean), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) on PET/CT were measured. Heterogeneity index-1 (HI-1; standard deviation [SD] divided by SUVmean) and heterogeneity index-2 (HI-2; linear regression slopes of the MTV according with different SUV thresholds), were evaluated as heterogeneity indices. Associations between these parameters and patient survival outcomes were analyzed. RESULTS: The univariate analysis showed that Masaoka stage, TNM stage, WHO classification, SUVmax, SUVmean, TLG, and HI-1 were significant prognostic factors for progression-free survival (PFS), while MTV, HI-2, age, gender, presence of myasthenia gravis, and maximum tumor diameter were not. Subsequently, multivariate analyses showed that HI-1 (p < 0.001) and TNM stage (p = 0.002) were independent prognostic factors for PFS. For the overall survival analysis, TNM stage, WHO classification, SUVmax, and HI-1 were significant prognostic factors in the univariate analysis, while TNM stage remained an independent prognostic factor in multivariate analyses (p = 0.024). The Kaplan Meier survival analyses showed worse prognoses for patients with TNM stages III and IV and HI-1 ≥ 0.16 compared to those with stages I and II and HI-1 < 0.16 (log-rank p < 0.001). CONCLUSION: HI-1 and TNM stage were independent prognostic factors for progression-free survival in TETs. HI-1 generated from baseline 18F-FDG PET/CT might be promising to identify patients with poor prognosis.

Deep learning for risk stratification of thymoma pathological subtypes based on preoperative CT images.

OBJECTIVES: This study aims to develop an innovative, deep model for thymoma risk stratification using preoperative CT images. Current algorithms predominantly focus on radiomic features or 2D deep features and require manual tumor segmentation by radiologists, limiting their practical applicability. METHODS: The deep model was trained and tested on a dataset comprising CT images from 147 patients (82 female; mean age, 54 years ± 10) who underwent surgical resection and received subsequent pathological confirmation. The eligible participants were divided into a training cohort (117 patients) and a testing cohort (30 patients) based on the CT scan time. The model consists of two stages: 3D tumor segmentation and risk stratification. The radiomic model and deep model (2D) were constructed for comparative analysis. Model performance was evaluated through dice coefficient, area under the curve (AUC), and accuracy. RESULTS: In both the training and testing cohorts, the deep model demonstrated better performance in differentiating thymoma risk, boasting AUCs of 0.998 and 0.893 respectively. This was compared to the radiomic model (AUCs of 0.773 and 0.769) and deep model (2D) (AUCs of 0.981 and 0.760). Notably, the deep model was capable of simultaneously identifying lesions, segmenting the region of interest (ROI), and differentiating the risk of thymoma on arterial phase CT images. Its diagnostic prowess outperformed that of the baseline model. CONCLUSIONS: The deep model has the potential to serve as an innovative decision-making tool, assisting on clinical prognosis evaluation and the discernment of suitable treatments for different thymoma pathological subtypes. KEY POINTS: • This study incorporated both tumor segmentation and risk stratification. • The deep model, using clinical and 3D deep features, effectively predicted thymoma risk. • The deep model improved AUCs by 16.1pt and 17.5pt compared to radiomic model and deep model (2D) respectively.

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.

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.