Dataset Information

Differentiating thymoma from thymic cyst in anterior mediastinal abnormalities smaller than 3 cm.

ABSTRACT:

Background

Computed tomography (CT) screening for lung cancer has led to frequent findings of small anterior mediastinal masses. It is very hard to distinguish small thymomas from thymic cysts. The objective of this study was to develop a clinical model for predicting small thymomas (<3 cm) in asymptomatic patients.

Methods

Patients who underwent thymectomy for anterior mediastinal masses between 2004 and 2016 were included. All preoperative CT scans (pre- and post-enhanced) were retrospectively reviewed. Size, location, contour, shape, presence of calcification, and enhancement [Hounsfield units (HU)] were evaluated. A nomogram was built based on the predictive factors. For external validation, patients undergoing thymectomy in 2017 were enrolled and thymoma prediction was computed using the proposed nomogram.

Results

The study population consisted of 43 patients with thymoma and 57 with thymic cyst. The multivariable analysis identified a lobulated contour and a large difference in HU between post- and pre-enhancement as predictive factors of thymoma. These factors were included in the nomogram, which showed 95% (19/20) power for predicting thymoma after external validation.

Conclusions

This clinical model can be used to predict thymoma in patients with small, asymptomatic thymic abnormalities on CT screening.

SUBMITTER: Jung W

PROVIDER: S-EPMC7212157 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Publications

Differentiating thymoma from thymic cyst in anterior mediastinal abnormalities smaller than 3 cm.

Jung Woohyun W Cho Sukki S Yum Sungwon S Lee Young Kyung YK Kim Kwhanmien K Jheon Sanghoon S

Journal of thoracic disease 20200401 4

<h4>Background</h4>Computed tomography (CT) screening for lung cancer has led to frequent findings of small anterior mediastinal masses. It is very hard to distinguish small thymomas from thymic cysts. The objective of this study was to develop a clinical model for predicting small thymomas (<3 cm) in asymptomatic patients.<h4>Methods</h4>Patients who underwent thymectomy for anterior mediastinal masses between 2004 and 2016 were included. All preoperative CT scans (pre- and post-enhanced) were ...[more]

PMID: 32395273

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Project description:ObjectiveTo investigate the clinical efficacy of the subxiphoid approach for early anterior mediastinal thymoma and evaluate its advantages over the lateral intercostal approach.MethodsA total of 345 patients with early anterior mediastinal thymoma were retrospectively analyzed from January 2016 to December 2020 in the First Affiliated Hospital of Soochow University. Out of these, 99 patients underwent subxiphoid video-assisted thoracoscopic thymectomy and 246 patients underwent transthoracic video-assisted thoracoscopic thymectomy. We compared the intraoperative conditions (such as operation time and intraoperative blood loss), postoperative conditions [such as postoperative pleural drainage volume, extubation time, postoperative hospital stay, and postoperative visual analogue scale (VAS) pain score], and postoperative complications (such as death, pneumonia, delayed wound healing, cardiac arrhythmia, and phrenic nerve injury) of the two groups and analyzed the clinical advantages of the subxiphoid approach for treating early anterior mediastinal thymoma.ResultsThere was no significant difference between the two groups in terms of general clinical features, operation time, and postoperative complications (P > 0.05).However, there was a significant difference in terms of intraoperative blood loss, postoperative pleural drainage volume, tube extubation time, postoperative hospital stay, postoperative VAS pain score, and postoperative analgesics (a significantly decreased flurbiprofen axetil amount) (P < 0.05).ConclusionCompared with the lateral intercostal thoracic approach, the subxiphoid approach had advantages in terms of intraoperative blood loss, postoperative hospital stay, tube extubation time, postoperative pleural drainage volume, postoperative VAS pain score, and analgesics dosage. It could provide a better view of the bilateral pleural cavities and more thorough thymectomy and superior cosmesis, and it proved to be a safe and feasible minimally invasive surgical method.

Project description:BackgroundIt is difficult for radiologists, especially junior radiologists with limited experience to make differential diagnoses between mediastinal lymphomas and thymic epithelial tumors (TETs) due to the overlapping imaging features. The purpose of this study was to develop and validate a CT-based clinico-radiomics model for differentiating lymphomas from TETs and to investigate whether a human-machine hybrid system can assist junior radiologists in improving their diagnostic performance.MethodsThe patients who underwent contrast-enhanced chest CT and pathologically confirmed with lymphoma or TET at two centers from January 2011 to December 2019 and from January 2017 to December 2021 were retrospectively included and split as training/validation set and external test set, respectively. Clinical and radiomic signatures were pre-selected by elastic-net, and the models were established with the selected signatures using ensemble learning. Three radiologists independently reviewed CT images and assessed each case of the external test set with knowledge of the relevant clinical information. The diagnoses of reader 1, reader 2, and reader 3 were compared with those of the models in the external test set and further separately input to the model's ensemble process as a human-machine system to make final decisions in the external test set. The improvement of diagnostic performance of radiologists by human-machine system was evaluated by the area under the receiver operating characteristic curve and increase rate.ResultsA total of 95 patients (51 with lymphomas and 44 with TETs) at Center 1 and 94 (52 with lymphomas and 42 with TETs) at Center 2 were enrolled and divided into training/validation sets and external test set, respectively. The diagnostic performance of the clinico-radiomics model has outperformed the junior radiologists and senior radiologist in AUC (clinico-radiomics model: 0.85 (0.76,0.92); reader 2: 0.70 (0.60,0.80); reader 3: 0.60 (0.49,0.71), reader 1: 0.76 (0.66,0.86), respectively) in the external test set. The human-machine hybrid system demonstrated significant increases in AUC (reader 1 + model: 0.87 (0.79,0.94), an increase of 14%; reader 2 + model: 0.86 (0.77,0.93), an increase of 23%; reader 3 + model: 0.84 (0.76,0.91), an increase of 40%), compared to the human performance alone.ConclusionsThe clinico-radiomics model outperformed three radiologists in differentiating lymphomas from TETs on CT. The use of the human-machine hybrid system significantly improved the performance of radiologists, especially junior radiologists. It provides a real-time decision tool to reduce bias and mistakes in radiologist diagnosis and enhances the diagnostic confidence of junior radiologists. This attempt may lead to more human-machine hybrid systems being explored in the diagnosis of different diseases to drive future clinical applications.

Dataset Information

Differentiating thymoma from thymic cyst in anterior mediastinal abnormalities smaller than 3 cm.

Background

Methods

Results

Conclusions

Publications

Differentiating thymoma from thymic cyst in anterior mediastinal abnormalities smaller than 3 cm.

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