Clinical significance of age at diagnosis among patients with thymic epithelial tumors: a population-based study

Differentiating thymic epithelial tumors from mediastinal lymphomas: preoperative nomograms based on PET/CT radiomic features to minimize unnecessary anterior mediastinal surgery

PURPOSE

Clinical feasibility nomograms were developed to facilitate the differentiation between thymic epithelial tumors (TETs) and mediastinal lymphomas (MLs), aiming to minimize the occurrence of non-therapeutic thymectomy.

METHODS

A total of 255 patients diagnosed with TETs or MLs underwent pre-treatment 18F-FDG PET/CT. Comprehensive clinical and imaging data were collected, including age, gender, lactate dehydrogenase (LDH) level, pathological results, presence of myasthenia gravis symptoms, B symptoms, mass size, location, morphology, margins, density, and metabolic parameters derived from PET/CT. Radiomic features were extracted from the region of interest (ROI) of the primary lesion. Feature selection techniques were employed to identify the most discriminative subset of features. Machine learning methods were utilized to build candidate models, which were subsequently evaluated based on their area under the curve (AUC). Finally, nomograms were constructed using the optimal model to provide a clinical tool for improved diagnostic accuracy. The performance of the radiomic models was evaluated by their calibration, discrimination, and clinical utility.

RESULTS

Several independent risk factors were identified for distinguishing TETs from MLs, including average standardized uptake value (SUVavg), LDH, age, mass size, and radiomic score (rad-score). Significance was observed in differentiating the two types of tumors based on these factors. The best clinical efficacy was demonstrated by the combined model, with an impressive AUC of 0.954. Decision curve analysis and calibration curves indicated that the combined model was clinically advantageous for discriminating TETs from MLs. Besides, the results of external validation showed a sensitivity of 0.8 and a specificity of 0.78.

CONCLUSION

Preoperatively, the differentiation of TETs from MLs can be facilitated by the utilization of the combined clinical information and radiomics model. This approach holds promise in minimizing the occurrence of unnecessary anterior mediastinal surgeries.

Pathological tumor long-to-short axis ratio as a prognostic factor in patients with thymic epithelial tumors

Background

Thymic epithelial tumors (TETs) exhibit irregular shapes reflective of the heterogeneity in tumor growth and invasive properties. We aimed to identify the prognostic value of the pathological tumor long‐to‐short axis (L/S) ratio in TETs.

Methods

A retrospective study was performed on patients with TETs who underwent extended thymectomy between January 1999 and December 2019 in our institute. Patients were divided into two groups according to the threshold of the L/S ratio. Overall survival (OS) and progression‐free survival (PFS) were evaluated by Kaplan‐Meier analysis. The independent prognostic factors of TETs were identified by multivariate analysis. The performance of prediction models for the above survival outcomes with and without the L/S ratio was evaluated using an integrated time‐dependent area under the curve (iAUC).

Results

Eligible patients were divided into two groups based on higher (n = 42) and lower (n = 94) L/S ratios according to a threshold value of 1.39. A significant difference was found between the two groups only in disease progression (p = 0.001). Poorer survival outcomes were found from Kaplan‐Meier curves in the higher L/S ratio group (p < 0.05). In the multivariable analysis, the L/S ratio showed significant effects on OS and PFS (p < 0.05). The performance of models with the L/S ratio was better than that without the L/S ratio in predicting survival outcomes.

Conclusions

The pathological tumor L/S ratio is an independent prognostic factor for OS and PFS in patients with TETs, and an L/S ratio >1.39 is associated with worse survival outcomes.

The Studies of Prognostic Factors and the Genetic Polymorphism of Methylenetetrahydrofolate Reductase C667T in Thymic Epithelial Tumors

Objective

To describe the clinical features of a cohort of patients with thymic epithelial tumors (TETs) and to analyze their prognostic factors. In particular, we investigated the correlation between the genetic polymorphism of methylenetetrahydrofolate reductase (MTHFR) C667T and the incidence of TETs.

Methods

Pathological records were reviewed from the database of the Second Affiliated Hospital of Jiaxing University, from January 2010 to December 2020, and 84 patients with TETs were recruited for this study. Univariate and multivariate analyses were performed to determine the prognostic factors. The genetic polymorphism of MTHFR C667T was examined in the patients with TETs and in a group of healthy individuals. The correlation between MTHFR transcriptional levels and methylation was analyzed using The Cancer Genome Atlas (TCGA) thymoma dataset from the cBioPortal platform.

Results

Kaplan–Meier univariate survival analysis showed that sex, age, the maximum tumor diameter, surgery, chemotherapy, radiotherapy, WHO histological classification, Masaoka–Koga stage, and 8th UICC/AJCC TNM staging, were statistically significantly correlated with the prognosis of patients with TETs. The Masaoka–Koga stage and 8th UICC/AJCC TNM staging were strongly correlated with each other in this study (r=0.925, P<0.001). Cox multivariate survival analysis showed that the maximum tumor diameter, Masaoka–Koga stage, and 8th UICC/AJCC TNM staging were independent prognostic factors affecting the overall survival (OS) of patients with TETs (P<0.05). The MTHFR C667T genotype (χ^{2 =} 7.987, P=0.018) and allele distribution (χ^{2 =} 5.750, P=0.016) were significantly different between the patients and healthy controls. CT heterozygous and TT homozygous genotypes at this MTHFR polymorphism significantly increased the risk of TETs (odds ratio [OR] =4.721, P=0.008). Kaplan–Meier univariate survival analysis showed that there was no correlation between different genotypes and the prognosis of TETs (CC versus CT + TT, χ^{2 =}0.003, P=0.959). Finally, a negative correlation between the transcriptional and methylation levels of MTHFR was observed in the TCGA thymoma dataset (r=-0.24, P=0.010).

Conclusions

The Masaoka–Koga stage, 8th UICC/AJCC TNM staging, and maximum tumor diameter were independent prognostic factors for TETs. Reduced methylation levels of MTHFR and particular polymorphic variants may contribute to the susceptibility to developing TETs.

Development of a competing risk nomogram for the prediction of cause-specific mortality in patients with thymoma: a population-based analysis

Background

This study was developed to assess the odds of cause-specific mortality and other types of mortality in thymoma patients. In addition, these analyses were leveraged to develop a comprehensive competing risk model-based nomogram capable of predicting cause-specific mortality as a result of thymoma.

Methods

Thymoma patients included within the Surveillance, Epidemiology, and End Results (SEER) database from 2004–2016 were identified, and the odds of cause-specific mortality due to thymoma and other forms of mortality for these patients were estimated. In addition, Fine and Gray’s proportional subdistribution hazard model was constructed, and a competing risk nomogram was developed using this model that was capable of predicting the odds of 3-, 5-, and 10-year cause-specific mortality in thymoma patients.

Results

In total, 1,591 relevant cases in the SEER database were selected for analysis. In this patient cohort, the respective 5-year cumulative incidence rates for cause-specific mortality and mortality attributable to other causes were 12.4% and 8.2%. Variables significantly associated with cause-specific mortality included age, chemotherapy, surgery, and Masaoka stage. Additionally, the odds of other-cause-specific mortality rose with increasing patient age, and chemotherapy was correlated with other-cause-specific mortality. The competing risk nomogram that was developed exhibited good discriminative ability as a means of predicting cause-specific mortality, as evidenced by a concordance index (C-index) value of 0.84. Calibration curves further revealed excellent consistency between predicted and actual mortality when using this nomogram.

Conclusions

In summary, we herein assessed the odds of cause-specific and other-cause-specific mortality among thymoma patients, and we designed a novel nomogram capable of predicting cause-specific mortality for thymoma, providing a promising tool that may be of value in the context of individualized patient prognostic evaluation.

A novel risk classifier for predicting the overall survival of patients with thymic epithelial tumors based on the eighth edition of the TNM staging system: A population-based study

OBJECTIVE

Thymic epithelial tumors (TETs) are rare tumors that originated from thymic epithelial cells, with limited studies investigating their prognostic factors. This study aimed to investigate the prognostic factors of TETs and develop a new risk classifier to predict their overall survival (OS).

METHODS

This retrospective study consisted of 1224 TETs patients registered in the Surveillance, Epidemiology, and End Results (SEER) database, and 75 patients from the First Affiliated Hospital of Xi'an Jiaotong University. The univariate and multivariate Cox regression analyses were adopted to select the best prognostic variables. A nomogram was developed to predict the OS of these patients. The discriminative and calibrated abilities of the nomogram were assessed using the receiver operating characteristics curve (ROC) and calibration curve. Decision curve analysis (DCA), net reclassification index (NRI), and integrated discrimination improvement (IDI) were adopted to assess its net clinical benefit and reclassification ability.

RESULTS

The multivariate analysis revealed that age, sex, histologic type, TNM staging, tumor grade, surgery, radiation, and tumor size were independent prognostic factors of TETs, and a nomogram was developed to predict the OS of these patients based on these variables. The time-dependent ROC curves displayed that the nomogram yielded excellent performance in predicting the 12-, 36- and 60-month OS of these patients. Calibration curves presented satisfying consistencies between the actual and predicted OS. DCA illustrated that the nomogram will bring significant net clinical benefits to these patients compared to the classic TNM staging system. The estimated NRI and IDI showed that the nomogram could significantly increase the predictive ability of 12-, 36- and 60-month OS compared to the classic TNM staging system. Consistent findings were discovered in the internal and external validation cohorts.

CONCLUSION

The constructed nomogram is a reliable risk classifier to achieve personalized survival probability prediction of TETs, and could bring significant net clinical benefits to these patients.