BACKGROUND: Primary cardiac tumors, while rare, present significant clinical challenges due to their diverse pathology and presentation. Lung cancer frequently metastasizes to the heart; however, cases involving primary cardiac tumors of different origins alongside primary lung cancer are exceedingly rare in the literature.
METHODS: We report the case of a 53-year-old female who presented with hemoptysis and was subsequently diagnosed with a left atrial myxoma, pulmonary squamous cell carcinoma, and a thymic cyst. This coexistence of multiple non-homologous tumors in a single patient is exceedingly rare.
CONCLUSIONS: This case underscores the complexity of diagnosing and managing patients with multiple distinct tumors. The simultaneous occurrence of a primary cardiac myxoma, pulmonary squamous cell carcinoma, and thymic cyst is unprecedented, providing valuable insights for future clinical practice.

暂无摘要。

This case report details a rare thymic basaloid carcinoma initially misinterpreted as a mediastinal teratoma, underscoring the diagnostic challenges posed by such tumors. A 71-year-old female presented with an asymptomatic anterior mediastinal tumor discovered incidentally during a routine health examination. Surgical intervention, followed by pathological and immunohistochemical analysis including CK-pan, p63, p40, and CD117 molecules, led to a definitive diagnosis of basaloid carcinoma of the thymus. This case highlights the critical importance of differential diagnosis in mediastinal lesions, especially those presenting with multilocular thymic cysts on chest CT. The subxiphoid video-assisted thoracoscopic surgery enabled complete tumor resection with minimal trauma and favorable postoperative outcomes. The patient opted against further radiotherapy or chemotherapy and she has survived for over eight months without recurrence. This case report contributes to the growing understanding of thymic basaloid carcinoma, a rare and potentially aggressive thymic carcinoma subtype. It emphasizes the necessity for precise surgical techniques and enhanced diagnostic acumen among cardiothoracic surgeons and oncologists.

暂无摘要。

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

BACKGROUND: We describe 1 case of fetal giant pericardial cyst was diagnosed by fetal echocardiography in the second trimester, and the changes of the cyst were recorded by follow-up observation in the late trimester and after birth. We then review and discuss the knowledge about its diagnosis and treatment.
METHODS: A 34-year-old pregnant woman was referred to our hospital because of a diagnosis of a fetal pericardial effusion at 22 5/7 weeks at another hospital.
METHODS: Fetal echocardiography revealed an irregular anechoic area in the right side of the fetal right atrium and right ventricle that was closely related to but not communicated with the pericardiumis and suggested fetal pericardial cyst. Fetal cardiothoracic magnetic resonance imaging showed cystic FIESTA signal in the right lung region, with clear boundary, and a seemingly line-like low signal shadow within.
METHODS: Since fetal pericardial cysts keep decreasing in size during maternal pregnancy, follow-up observation measures are taken.
RESULTS: Fetal pericardial cysts disappear on their own 4 months after delivery.
CONCLUSIONS: Asymptomatic pericardial cysts in the fetal period can be followed up and observed, and intervention is performed only when the cyst rapidly enlarges or ruptures and becomes infected in the fetal or neonatal period. Echocardiography can be used as a first-line detection method for their initial detection and follow-up.

OBJECTIVE: To compare the efficacy and safety of robotic-assisted thoracoscopic surgery (RATS) with video-assisted thoracoscopic surgery (VATS) in the treatment of mediastinal cysts.
METHODS: Retrospective analysis on clinical data of 70 cases of minimally invasive surgery for mediastinal cysts completed in the Department of Thoracic Surgery, Gansu Provincial People\'s Hospital from April 2014 to December 2022. There were 34 cases in the RATS group with a cyst diameter of (3.70 ± 1.16) cm and 36 cases in the VATS group with a cyst diameter of (4.07 ± 1.20) cm. All cysts were evaluated preoperatively using magnetic resonance imaging (MRI) or chest computed tomography (CT) localization. Surgery-related indices were compared among the two groups.
RESULTS: All patients in two groups successfully completed resection of mediastinal cysts without perioperative deaths. Compared with the VATS group, the RATS group possessed shorter operative time [(75.32 ± 17.80) min vs. (102.22 ± 19.80) min, P < 0.001], lesser intraoperative bleeding [10 (5.00, 26.00) ml vs. 17.50 (5.00, 50.50) ml, P = 0.009], shorter postoperative chest drainage time [2 (1.00, 6.00) ml vs. 3 (2.00, 6.50) ml, P = 0.006] and shorter postoperative hospital stay [3 (2.00, 6.50) d vs. 4 (3.00, 7.50) d, P = 0.001]. There was no statistically significant discrepancy in intermediate openings and complications in both groups (P > 0.05).
CONCLUSIONS: Compared with VATS, RATS is safety and effectivity in the treatment of mediastinal cysts and thus has advantages in operative time, intraoperative bleeding, postoperative chest drainage time and postoperative hospital stay.

BACKGROUND: Pericardial cysts are a rare benign disorder with a variable clinical presentation depending on their size and location. The diagnosis of pericardial cysts is usually based on imaging examinations. The definitive treatment is surgical resection.
METHODS: A 36-year-old woman presented with progressive left-sided chest pain and exertional dyspnea, with symptoms resembling pleural effusion.
METHODS: The patient was diagnosed with a pericardial cyst based on imaging and video-assisted thoracoscopic surgery (VATS).
METHODS: VATS was performed.
RESULTS: The patient\'s symptoms improved after successful removal of the pericardial cyst. Follow-up chest computed tomography exhibited no evidence of recurrence.
CONCLUSIONS: Clinicians should include pericardial cysts in the differential diagnosis of pseudopleural effusion. VATS is a feasible and safe method to treat symptomatic and large pericardial cysts.

Mediastinal masses are uncommon and difficult to diagnose. Endoscopic ultrasound with fine-needle aspiration (EUS-FNA) is a minimally invasive technique for diagnosis of mediastinal lesions with few complications. Our report described a mediastinal bronchogenic cyst with soft tissue density infected by Streptococcus pyogenes (S. pyogenes) after EUS-FNA, accompanied by respiratory cardiac arrest and superior vena cava syndrome. The patient underwent cardiopulmonary resuscitation to gain the chance for emergency surgery and recovered. Clinicians should be aware that mediastinal mass with soft tissue density shown on imaging may be mediastinal cyst containing high density mucin, FNA should be avoided if cystic masses cannot be ruled out.

UNASSIGNED: This study aimed to investigate the diagnostic value of machine-learning (ML) models with multiple classifiers based on non-enhanced CT Radiomics features for differentiating anterior mediastinal cysts (AMCs) from thymomas, and high-risk from low risk thymomas.
UNASSIGNED: In total, 201 patients with AMCs and thymomas from three centers were included and divided into two groups: AMCs vs. thymomas, and high-risk vs low-risk thymomas. A radiomics model (RM) was built with 73 radiomics features that were extracted from the three-dimensional images of each patient. A combined model (CM) was built with clinical features and subjective CT finding features combined with radiomics features. For the RM and CM in each group, five selection methods were adopted to select suitable features for the classifier, and seven ML classifiers were employed to build discriminative models. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic performance of each combination.
UNASSIGNED: Several classifiers combined with suitable selection methods demonstrated good diagnostic performance with areas under the curves (AUCs) of 0.876 and 0.922 for the RM and CM in group 1 and 0.747 and 0.783 for the RM and CM in group 2, respectively. The combination of support vector machine (SVM) as the feature-selection method and Gradient Boosting Decision Tree (GBDT) as the classification algorithm represented the best comprehensive discriminative ability in both group. Comparatively, assessments by radiologists achieved a middle AUCs of 0.656 and 0.626 in the two groups, which were lower than the AUCs of the RM and CM. Most CMs exhibited higher AUC value compared to RMs in both groups, among them only a few CMs demonstrated better performance with significant difference in group 1.
UNASSIGNED: Our ML models demonstrated good performance for differentiation of AMCs from thymomas and low-risk from high-risk thymomas. ML based on non-enhanced CT radiomics may serve as a novel preoperative tool.