Correlation of clinical and computed tomography features of thymic epithelial tumours with World Health Organization classification and Masaoka-Koga staging

Risk factor analysis for major mediastinal vessel invasion in thymic epithelial tumors based on multi-slice CT Imaging

Objective

To explore the characteristics and risk factors for major mediastinal vessel invasion in different risk grades of thymic epithelial tumors (TETs) based on computed tomography (CT) imaging, and to develop prediction models of major mediastinal artery and vein invasion.

Methods

One hundred and twenty-two TET patients confirmed by histopathological analysis who underwent thorax CT were enrolled in this study. Clinical and CT data were retrospectively reviewed for these patients. According to the abutment degree between the tumor and major mediastinal vessels, the arterial invasion was divided into grade I, II, and III (< 25%, 25 - 49%, and ≥ 50%, respectively); the venous invasion was divided into grade I and II (< 50% and ≥ 50%). The degree of vessel invasion was compared among different defined subtypes or stages of TETs using the chi-square tests. The risk factors associated with TET vascular invasion were identified using multivariate logistic regression analysis.

Results

Based on logistic regression analysis, male patients (β = 1.549; odds ratio, 4.824) and the pericardium or pleural invasion (β = 2.209; odds ratio, 9.110) were independent predictors of 25% artery invasion, and the midline location (β = 2.504; odds ratio, 12.234) and mediastinal lymphadenopathy (β = 2.490; odds ratio, 12.06) were independent predictors of 50% artery invasion. As for 50% venous invasion, the risk factors include midline location (β = 2.303; odds ratio, 10.0), maximum tumor diameter larger than 5.9 cm (β = 4.038; odds ratio, 56.736), and pericardial or pleural effusion (β = 1.460; odds ratio, 4.306). The multivariate logistic model obtained relatively high predicting efficacy, and the area under the curve (AUC), sensitivity, and specificity were 0.944, 84.6%, and 91.7% for predicting 50% artery invasion, and 0.913, 81.8%, and 86.0% for 50% venous invasion in TET patients, respectively.

Conclusion

Several CT features can be used as independent predictors of ≥50% artery or venous invasion. A multivariate logistic regression model based on CT features is helpful in predicting the vascular invasion grades in patients with TET.

An unusual ectopic thymoma clonal evolution analysis: A case report

Thymomas and thymic carcinomas are rare and primary tumors of the mediastinum which is derived from the thymic epithelium. Thymomas are the most common primary anterior mediastinal tumor, while ectopic thymomas are rarer. Mutational profiles of ectopic thymomas may help expand our understanding of the occurrence and treatment options of these tumors. In this report, we sought to elucidate the mutational profiles of two ectopic thymoma nodules to gain deeper understanding of the molecular genetic information of this rare tumor and to provide guidance treatment options. We presented a case of 62-year-old male patient with a postoperative pathological diagnosis of type A mediastinal thymoma and ectopic pulmonary thymoma. After mediastinal lesion resection and thoracoscopic lung wedge resection, the mediastinal thymoma was completely removed, and the patient recovered from the surgery and no recurrence was found by examination until now. Whole exome sequencing was performed on both mediastinal thymoma and ectopic pulmonary thymoma tissue samples of the patient and clonal evolution analysis were further conducted to analyze the genetic characteristics. We identified eight gene mutations that were co-mutated in both lesions. Consistent with a previous exome sequencing analysis of thymic epithelial tumor, HRAS was also observed in both mediastinal lesion and lung lesion tissues. We also evaluated the intratumor heterogeneity of non-silent mutations. The results showed that the mediastinal lesion tissue has higher degree of heterogeneity and the lung lesion tissue has relatively low amount of variant heterogeneity in the detected variants. Through pathology and genomics sequencing detection, we initially revealed the genetic differences between mediastinal thymoma and ectopic thymoma, and clonal evolution analysis showed that these two lesions originated from multi-ancestral regions.

Machine learning-based radiomic computed tomography phenotyping of thymic epithelial tumors: Predicting pathological and survival outcomes

OBJECTIVE

For patients with thymic epithelial tumors, accurately predicting clinicopathological outcomes remains challenging. We aimed to investigate the performance of machine learning-based radiomic computed tomography phenotyping for predicting pathological (World Health Organization [WHO] type and TNM stage) and survival outcomes (overall and progression-free survival) in patients with thymic epithelial tumors.

METHODS

This retrospective study included patients with thymic epithelial tumors between January 2001 and January 2022. The radiomic features were extracted from preoperative unenhanced computed tomography images. After strict feature selection, random forest and random survival forest models were fitted to predict pathological and survival outcomes, respectively. The model performance was assessed by the area under the curve (AUC) and validated internally by the bootstrap method.

RESULTS

In total, 124 patients with a median age of 61 years were included. The radiomics random forest models of WHO type and TNM stage showed satisfactory performance with an AUC_WHO of 0.898 (95% CI, 0.753-1.000) and an AUC_TNM of 0.766 (95% CI, 0.642-0.886). For overall survival and progression-free survival prediction, the radiomics random survival forest models showed good performance (integrated AUCs, 0.923; 95% CI, 0.691-1.000 and 0.702; 95% CI, 0.513-0.875, respectively), and the integrated AUCs increased to 0.935 (95% CI, 0.705-1.000) and 0.811 (95% CI, 0.647-0.942), respectively, when combined with clinicopathological features.

CONCLUSIONS

Machine learning-based radiomic computed tomography phenotyping might allow for the satisfactory prediction of pathological and survival outcomes and further improve prognostic performance when integrated with clinicopathological features in patients with thymic epithelial tumors.

Epigenetics of Thymic Epithelial Tumors

Thymic epithelial tumors (TETs) arise from the epithelial cells of the thymus and consist in the 1% of all adult malignancies, despite the fact that they are the most common lesions of the anterior mediastinum. TETs can be divided mainly into thymomas, thymic carcinomas, and the rarest ad aggressive neuroendocrine forms. Despite the surgical resection is quite resolving, the diagnosis of TETs is complicated by the absence of symptoms and the clinical presentation aggravated by several paraneoplastic disorders, including myasthenia gravis. Thus, the heterogeneity of TETs prompts the search for molecular biomarkers that could be helpful for tumor characterization and clinical outcomes prediction. With these aims, several researchers investigated the epigenetic profiles of TETs. In this manuscript, we narratively review the works investigating the deregulation of epigenetic mechanisms in TETs, highlighting the need for further studies combining genetic, epigenetic, and expression data to better characterize the different molecular subtypes and identify, for each of them, the most relevant epigenetic biomarkers of clinical utility.

Micronodular thymoma with lymphoid stroma: Contrast-enhanced CT features with histopathological correlation in 10 patients

Objectives

This study aimed to evaluate and summarize the contrast-enhanced computed tomography (CECT) imaging features of micronodular thymoma with lymphoid stroma (MTWLS) based on all MTWLS patients at our institution and was the first imaging study of MTWLS worldwide.

Methods

This retrospective study included 10 MTWLS patients who underwent CECT between April 2012 and November 2021. We collected and analyzed the CECT imaging features, including the location, size, shape, tumor density, classification, and CT value of the solid component. Descriptive statistical analysis was performed using the SPSS software (version 26.0; IBM).

Results

Ten patients (five males [50%], five females [50%]; median age, 61.4 years; range, 54-72 years) underwent CECT. Of the 10 cases, one case was purely cystic, seven cases were cystic-solid, and two cases were purely solid. Six cases were round/oval in shape, and four cases were irregularly shaped. Excluding a purely cystic tumor with an unmeasurable degree of enhancement, two cases showed moderate enhancement, and seven cases showed significant enhancement. Among the solid or cystic-solid cases, the mean CT value of the measurable solid component on the enhanced scan was 93.9 HU. Nine masses were located adjacent to the mediastinal pleura, pericardium, or large vessels. Additionally, there were no malignant tumor signs in any patient, including penetration of the mediastinal pleura or involvement of the pericardium, pleural effusion, elevation of the diaphragm, or direct vascular invasion.

Conclusion

MTWLS demonstrates certain features on CECT, such as a high rate of cystic change, significant solid component enhancement, and no malignant, invasive imaging features. These CECT features are helpful for diagnosing MTWLS.