Clinical radiomics-based machine learning versus three-dimension convolutional neural network analysis for differentiation of thymic epithelial tumors from other prevascular mediastinal tumors on chest computed tomography scan

PurposeTo compare the diagnostic performance of radiomic analysis with machine learning (ML) model with a convolutional neural network (CNN) in differentiating thymic epithelial tumors (TETs) from other prevascular mediastinal tumors (PMTs).MethodsA retrospective study was performed in patients with PMTs and undergoing surgical resection or biopsy in National Cheng Kung University Hospital, Tainan, Taiwan, E-Da Hospital, Kaohsiung, Taiwan, and Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan between January 2010 and December 2019. Clinical data including age, sex, myasthenia gravis (MG) symptoms and pathologic diagnosis were collected. The datasets were divided into UECT (unenhanced computed tomography) and CECT (enhanced computed tomography) for analysis and modelling. Radiomics model and 3D CNN model were used to differentiate TETs from non-TET PMTs (including cyst, malignant germ cell tumor, lymphoma and teratoma). The macro F1-score and receiver operating characteristic (ROC) analysis were performed to evaluate the prediction models.ResultIn the UECT dataset, there were 297 patients with TETs and 79 patients with other PMTs. The performance of radiomic analysis with machine learning model using LightGBM with Extra Tree (macro F1-Score = 83.95%, ROC-AUC = 0.9117) had better performance than the 3D CNN model (macro F1-score = 75.54%, ROC-AUC = 0.9015). In the CECT dataset, there were 296 patients with TETs and 77 patients with other PMTs. The performance of radiomic analysis with machine learning model using LightGBM with Extra Tree (macro F1-Score = 85.65%, ROC-AUC = 0.9464) had better performance than the 3D CNN model (macro F1-score = 81.01%, ROC-AUC = 0.9275).ConclusionOur study revealed that the individualized prediction model integrating clinical information and radiomic features using machine learning demonstrated better predictive performance in the differentiation of TETs from other PMTs at chest CT scan than 3D CNN model

Demographic and Clinical Characteristics of Patients with Severe Asthma in the Asian Pacific Region : data from the International Severe Asthma Registry (ISAR)

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Androgen deprivation modulates the inflammatory response induced by irradiation

<p>Abstract</p> <p>Background</p> <p>The aim of this study was to determine whether radiation (RT)-induced inflammatory responses and organ damage might be modulated by androgen deprivation therapies.</p> <p>Methods</p> <p>The mRNA and tissue sections obtained from the lungs, intestines and livers of irradiated mice with or without androgen deprivation were analyzed by real-time PCR and histological analysis. Activation of NF-kappa B was examined by measuring nuclear protein levels in the intestine and lung 24 h after irradiation. We also examined the levels of cyclooxygenase-2 (COX-2), TGF-β1 and p-AKT to elucidate the related pathway responsible to irradiation (RT) -induced fibrosis.</p> <p>Results</p> <p>We found androgen deprivation by castration significantly augmented RT-induced inflammation, associated with the increase NF-κB activation and COX-2 expression. However, administration of flutamide had no obvious effect on the radiation-induced inflammation response in the lung and intestine. These different responses were probably due to the increase of RT-induced NF-κB activation and COX-2 expression by castration or lupron treatment. In addition, our data suggest that TGF-β1 and the induced epithelial-mesenchymal transition (EMT) via the PI3K/Akt signaling pathway may contribute to RT-induced fibrosis.</p> <p>Conclusion</p> <p>When irradiation was given to patients with total androgen deprivation, the augmenting effects on the RT-induced inflammation and fibrosis should take into consideration for complications associated with radiotherapy.</p