CT-based radiomics features analysis for predicting the risk of anterior mediastinal lesions

Sui, He; Liu, Lin; Li, Xuejia; Zuo, Panli; Cui, Jingjing; Mo, Zhanhao

doi:10.21037/jtd.2019.05.32

Cited by 26 publications

(30 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wang X et al [25] obtained the results that the AUCs were 0.829 and 0.860 for the radiomics signature based on unenhanced and enhanced CT images in differentiating advanced stage thymomas from early stage thymomas, respectively. However, He Sui et al [26] believed that the unenhanced phase could better distinguish high-risk and low-risk thymomas than the enhanced phase because more texture features were selected from the unenhanced phase than from the enhanced phase, and tumour heterogeneity was better detected in the unenhanced phase, in contrast to our results. Further research is needed in the future.…”

Section: Discussioncontrasting

confidence: 95%

Can Computed Tomography-Based Radiomics Potentially Discrimination Between Anterior Mediastinal Cysts and type B1 and B2 thymomas

Liu

Pang

et al. 2020

Preprint

View full text Add to dashboard Cite

Background Anterior mediastinal cysts (AMC) are often misdiagnosed as thymomas and undergo surgical resection, which caused unnecessary treatment and medical resources waste. The purpose of this study was to explore potential possibility of computed tomography (CT)-based radiomics for the diagnosis of AMC and type B1 and B2 thymomas. Methods A group of 188 patients with pathologically confirmed AMC (106 cases mischarged as thymomas in CT) and thymomas (82 cases) and underwent routine chest CT from January 2010 to December 2018 were retrospectively analyzed. The lesions were manually delineated using ITK-SNAP software, and radiomics features were performed using the Artificial Intelligence Kit (AK) software. A total of 396 tumor texture features were extracted from enhanced CT and unenhanced CT, respectively. The general test, correlation analysis and LASSO were used to features selection and then the radiomics signature (radscore) were obtained. The combined model including radscore and independent clinical factors were developed. The model performances were evaluated on discrimination, calibration curve. Results Two radscore model were constructed from the unenhanced and enhanced phases based on the selected 4 and 3 features, respectively. The AUC, sensitivity, and specificity of the enhanced radscore model were 0.928, 89.3% and 83.8% in the training dataset and 0.899, 84.6%, 87.5% in the test dataset (higher than the unenhanced radscore model). The combined model of enhanced CT including radiomics features and independent clinical factors yielded an AUC, sensitivity and specificity of 0.941, 82.1%, and 94.6% in the training dataset and 0.938, 92.3%, and 87.5% in the test dataset (higher than the unenhanced combined model and enhanced radscore model). Conclusions The study suggested the possibility that the combined model in enhanced CT provided a potential tool to facilitate the differential diagnosis of AMC and type B1 and B2 thymomas.

show abstract

Section: Discussioncontrasting

confidence: 95%

Can Computed Tomography-Based Radiomics Potentially Discrimination Between Anterior Mediastinal Cysts and type B1 and B2 thymomas

Liu

Pang

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Wang et al [ 26 ] obtained the results that the AUCs were 0.829 and 0.860 for the radiomics signature based on unenhanced and enhanced CT images in differentiating advanced stage thymomas from early stage thymomas, respectively. However, Sui et al [ 27 ] believed that the unenhanced phase could better distinguish high-risk and low-risk thymomas than the enhanced phase, because more texture features were selected from the unenhanced phase than from the enhanced phase, and tumour heterogeneity was better detected in the unenhanced phase, the above studies were similar to our results that the enhanced CT and unenhanced CT radiomics can better differentiating the different stages of thymoma, the enrolled thymomas were confirmed by Masaoka clinical stage and WHO histologic classification. In our study, only AMC distinguished from type B1 and B2 thymomas was enrolled to research, because most of type B3 thymic carcinomas can easily be distinguished from cysts preoperatively by CT manifestations.…”

Section: Discussionmentioning

confidence: 99%

Can computed tomography-based radiomics potentially discriminate between anterior mediastinal cysts and type B1 and B2 thymomas?

Liu

Pang³

et al. 2020

BioMed Eng OnLine

View full text Add to dashboard Cite

Background Anterior mediastinal cysts (AMC) are often misdiagnosed as thymomas and undergo surgical resection, which caused unnecessary treatment and medical resource waste. The purpose of this study is to explore potential possibility of computed tomography (CT)-based radiomics for the diagnosis of AMC and type B1 and B2 thymomas. Methods A group of 188 patients with pathologically confirmed AMC (106 cases misdiagnosed as thymomas in CT) and thymomas (82 cases) and underwent routine chest CT from January 2010 to December 2018 were retrospectively analyzed. The lesions were manually delineated using ITK-SNAP software, and radiomics features were performed using the artificial intelligence kit (AK) software. A total of 180 tumour texture features were extracted from enhanced CT and unenhanced CT, respectively. The general test, correlation analysis, and LASSO were used to features selection and then the radiomics signature (radscore) was obtained. The combined model including radscore and independent clinical factors was developed. The model performances were evaluated on discrimination, calibration curve. Results Two radscore models were constructed from the unenhanced and enhanced phases based on the selected four and three features, respectively. The AUC, sensitivity, and specificity of the enhanced radscore model were 0.928, 89.3%, and 83.8% in the training dataset and 0.899, 84.6%, and 87.5% in the test dataset (higher than the unenhanced radscore model). The combined model of enhanced CT including radiomics features and independent clinical factors yielded an AUC, sensitivity and specificity of 0.941, 82.1%, and 94.6% in the training dataset and 0.938, 92.3%, and 87.5% in the test dataset (higher than the unenhanced combined model and enhanced radscore model). Conclusions The study suggested the possibility that the combined model in enhanced CT provided a potential tool to facilitate the differential diagnosis of AMC and type B1 and B2 thymomas.

show abstract

“…He et al [24] evaluated the effects of contrast-enhancement on the diagnostic performance of radiomics signatures in solitary pulmonary nodules (SPNs), which indicated that contrast-enhancement can affect the diagnostic performance of radiomics signatures in SPNs and that noncontrast CT is more informative. Similarly, Sui et al [25] confirmed that the radiomics features of the non-contrast CT have a better predictive performance than those of contrast CT in anterior mediastinal lesion risk grading. In the research of classifying mediastinal LNM of NSCLC from 18 F-FDG PET/CT images, Yao et al [26] summarized the diagnostic results from 22 research centers and found that the overall sensitivity and specificity were 0.66 and 0.82, respectively.…”

Section: Discussionmentioning

confidence: 88%

Discrimination of mediastinal metastatic lymph nodes in NSCLC based on radiomic features in different phases of CT imaging

et al. 2020

View full text Add to dashboard Cite

Background: We aimed to develop radiomic models based on different phases of computed tomography (CT) imaging and to investigate the efficacy of models for diagnosing mediastinal metastatic lymph nodes (LNs) in nonsmall cell lung cancer (NSCLC). Methods: Eighty-six NSCLC patients were enrolled in this study, and we selected 231 mediastinal LNs confirmed by pathology results as the subjects which were divided into training (n = 163) and validation cohorts (n = 68). The regions of interest (ROIs) were delineated on CT scans in the plain phase, arterial phase and venous phase, respectively. Radiomic features were extracted from the CT images in each phase. A least absolute shrinkage and selection operator (LASSO) algorithm was used to select features, and multivariate logistic regression analysis was used to build models. We constructed six models (orders 1-6) based on the radiomic features of the single-and dual-phase CT images. The performance of the radiomic model was evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV). Results: A total of 846 features were extracted from each ROI, and 10, 9, 5, 2, 2, and 9 features were chosen to develop models 1-6, respectively. All of the models showed excellent discrimination, with AUCs greater than 0.8. The plain CT radiomic model, model 1, yielded the highest AUC, specificity, accuracy and PPV, which were 0.926 and 0.925; 0.860 and 0.769; 0.871 and 0.882; and 0.906 and 0.870 in the training and validation sets, respectively. When the plain and venous phase CT radiomic features were combined with the arterial phase CT images, the sensitivity increased from 0.879 and 0.919 to 0.949 and 0979 and the NPV increased from 0.821 and 0.789 to 0.878 and 0.900 in the training group, respectively. Conclusions: All of the CT radiomic models based on different phases all showed high accuracy and precision for the diagnosis of LN metastasis (LNM) in NSCLC patients. When combined with arterial phase CT, the sensitivity and NPV of the model was be further improved.

show abstract

CT-based radiomics features analysis for predicting the risk of anterior mediastinal lesions

Cited by 26 publications

References 23 publications

Can Computed Tomography-Based Radiomics Potentially Discrimination Between Anterior Mediastinal Cysts and type B1 and B2 thymomas

Can Computed Tomography-Based Radiomics Potentially Discrimination Between Anterior Mediastinal Cysts and type B1 and B2 thymomas

Can computed tomography-based radiomics potentially discriminate between anterior mediastinal cysts and type B1 and B2 thymomas?

Discrimination of mediastinal metastatic lymph nodes in NSCLC based on radiomic features in different phases of CT imaging

Contact Info

Product

Resources

About