Development and Validation of Contrast-Enhanced CT-Based Deep Transfer Learning and Combined Clinical-Radiomics Model to Discriminate Thymomas and Thymic Cysts: A Multicenter Study.

RATIONALE AND OBJECTIVES: This study aims to evaluate the feasibility and effectiveness of deep transfer learning (DTL) and clinical-radiomics in differentiating thymoma from thymic cysts. MATERIALS AND METHODS: Clinical and imaging data of 196 patients pathologically diagnosed with thymoma and thymic cysts were retrospectively collected from center 1. (training cohort: n = 137; internal validation cohort: n = 59). An independent external validation cohort comprised 68 thymoma and thymic cyst patients from center 2. Region of interest (ROI) delineation was performed on contrast-enhanced chest computed tomography (CT) images, and eight DTL models including Densenet 169, Mobilenet V2, Resnet 101, Resnet 18, Resnet 34, Resnet 50, Vgg 13, Vgg 16 were constructed. Radiomics features were extracted from the ROI on the CT images of thymoma and thymic cyst patients, and feature selection was performed using intra-observer correlation coefficient (ICC), Spearman correlation analysis, and least absolute shrinkage and selection operator (LASSO) algorithm. Univariate analysis and multivariable logistic regression (LR) were used to select clinical-radiological features. Six machine learning classifiers, including LR, support vector machine (SVM), k-nearest neighbors (KNN), Light Gradient Boosting Machine (LightGBM), Adaptive Boosting (AdaBoost), and Multilayer Perceptron (MLP), were used to construct Radiomics and Clinico-radiologic models. The selected features from the Radiomics and Clinico-radiologic models were fused to build a Combined model. Receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA) were used to evaluate the discrimination, calibration, and clinical utility of the models, respectively. The Delong test was used to compare the AUC between different models. K-means clustering was used to subdivide the lesions of thymomas or thymic cysts into subregions, and traditional radiomics methods were used to extract features and compare the ability of Radiomics and DTL models to reflect intratumoral heterogeneity using correlation analysis. RESULTS: The Densenet 169 based on DTL performed the best, with AUC of 0.933 (95% CI: 0.875-0.991) in the internal validation cohort and 0.962 (95% CI: 0.923-1.000) in the external validation cohort. The AdaBoost classifier achieved AUC of 0.965 (95% CI: 0.923-1.000) and 0.959 (95% CI: 0.919-1.000) in the internal and external validation cohorts, respectively, for the Radiomics model. The LightGBM classifier achieved AUC of 0.805 (95% CI: 0.690-0.920) and 0.839 (95% CI: 0.736-0.943) in the Clinico-radiologic model. The AUC of the Combined model in the internal and external validation cohorts was 0.933 (95% CI: 0.866-1.000) and 0.945 (95% CI: 0.897-0.994), respectively. The results of the Delong test showed that the Radiomics model, DTL model, and Combined model outperformed the Clinico-radiologic model in both internal and external validation cohorts (p-values were 0.002, 0.004, and 0.033 in the internal validation cohort, while in the external validation cohort, the p-values were 0.014, 0.006, and 0.015, respectively). But there was no statistical difference in performance among the three models (all p-values <0.05). Correlation analysis showed that radiomics performed better than DTL in quantifying intratumoral heterogeneity differences between thymoma and thymic cysts. CONCLUSION: The developed DTL model and the Combined model based on radiomics and clinical-radiologic features achieved excellent diagnostic performance in differentiating thymic cysts from thymoma. They can serve as potential tools to assist clinical decision-making, particularly when endoscopic biopsy carries a high risk.

Development and validation of a radiomic nomogram based on pretherapy dual-energy CT for distinguishing adenocarcinoma from squamous cell carcinoma of the lung.

Objective: Based on pretherapy dual-energy computed tomography (DECT) images, we developed and validated a nomogram combined with clinical parameters and radiomic features to predict the pathologic subtypes of non-small cell lung cancer (NSCLC) - adenocarcinoma (ADC) and squamous cell carcinoma (SCC). Methods: A total of 129 pathologically confirmed NSCLC patients treated at the Second Affiliated Hospital of Nanchang University from October 2017 to October 2021 were retrospectively analyzed. Patients were randomly divided in a ratio of 7:3 (n=90) into training and validation cohorts (n=39). Patients' pretherapy clinical parameters were recorded. Radiomics features of the primary lesion were extracted from two sets of monoenergetic images (40 keV and 100 keV) in arterial phases (AP) and venous phases (VP). Features were selected successively through the intra-class correlation coefficient (ICC) and the least absolute shrinkage and selection operator (LASSO). Multivariate logistic regression analysis was then performed to establish predictive models. The prediction performance between models was evaluated and compared using the receiver operating characteristic (ROC) curve, DeLong test, and Akaike information criterion (AIC). A nomogram was developed based on the model with the best predictive performance to evaluate its calibration and clinical utility. Results: A total of 87 ADC and 42 SCC patients were enrolled in this study. Among the five constructed models, the integrative model (AUC: Model 4 = 0.92, Model 5 = 0.93) combining clinical parameters and radiomic features had a higher AUC than the individual clinical models or radiomic models (AUC: Model 1 = 0.84, Model 2 = 0.79, Model 3 = 0.84). The combined clinical-venous phase radiomics model had the best predictive performance, goodness of fit, and parsimony; the area under the ROC curve (AUC) of the training and validation cohorts was 0.93 and 0.90, respectively, and the AIC value was 60.16. Then, this model was visualized as a nomogram. The calibration curves demonstrated it's good calibration, and decision curve analysis (DCA) proved its clinical utility. Conclusion: The combined clinical-radiomics model based on pretherapy DECT showed good performance in distinguishing ADC and SCC of the lung. The nomogram constructed based on the best-performing combined clinical-venous phase radiomics model provides a relatively accurate, convenient and noninvasive method for predicting the pathological subtypes of ADC and SCC in NSCLC.

Development and Validation of a Comprehensive Model for Predicting Distant Metastasis of Solid Lung Adenocarcinoma: 3D Radiomics, 2D Radiomics and Clinical Features.

Objective: To develop and validate models for predicting distant metastases in patients with solid lung adenocarcinomas using 3D radiomic features, 2D radiomic features, clinical features, and their combinations. Methods: This retrospective study included 253 eligible patients with solid adenocarcinoma of the lung diagnosed at our hospital between August 2018 and August 2021. 3D and 2D regions of interest were segmented from computed tomography-enhanced thin-slice images of the venous phase, and 851 radiomic features were extracted in each region. The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm was used to select radiomic features and calculate radiomic scores, and logistic regression was used to develop the model. Development of a 3D radiomics model (model 1), a 2D radiomics model (model 2), a combined 3D radiomics and 2D radiomics model (model 3), a clinical model (model 4), and a comprehensive model (model 5) for the prediction of distant metastases in patients with solid lung adenocarcinomas. Nomograms were drawn to illustrate model 5, and receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used for model evaluation. Results: The AUC (area under the curve) of model 1, model 2, model 3, model 4, and model 5 in the test set was 0.711, 0.769, 0.775, 0.829, and 0.892, respectively. The Delong test showed that AUC values were statistically different between model 5 and model 1 (p=0.001), and there was no statistical difference in AUC between the other models. Based on a comprehensive review of DCA, ROC curve, and Akaike information criterion (AIC), Model 5 is demonstrated to have better clinical utility, goodness of fit, and parsimony. Conclusion: A comprehensive model based on 3D radiomic features, 2D radiomic features, and clinical features has the potential to predict distant metastasis in patients with solid lung adenocarcinomas.

Primary pulmonary T-cell lymphoma after operation for small intestinal stromal tumor: A case report.

Background: The risk of gastrointestinal stromal tumor (GIST) in combination with other primary malignancies is high, which occurs before and after the diagnosis of GIST. Primary pulmonary T-cell lymphoma is a rare type of non-Hodgkin lymphoma. Case presentation: We report a 53-year-old male patient who was admitted to our hospital with fever, cough, and expectoration for 2 weeks. Chest computed tomography (CT) showed a cavitary mass in the left lower lobe with multiple nodules in the upper lobes of both lungs. The patient had a history of surgery for small intestinal stromal tumors and was treated with oral imatinib after surgery. Lung biopsy was diagnosed as lymphomatoid granulomatosis, tending to grade 3. The pathological diagnosis was corrected by surgery and genetic testing for lung non-Hodgkin CD8-positive cytotoxic T-cell lymphoma with Epstein-Barr virus (EBV) infection in some cells. After multiple chemotherapies, the CT scan showed a better improvement than before. The patient is still under follow-up, and no tumor recurrence has been found. Conclusion: Patients with a history of GIST should be monitored for other malignancies. The clinical symptoms and imaging examinations of primary pulmonary T-cell lymphoma are not characteristic, and the definite diagnosis still depends on pathological examination. The patient was treated with the CHOP chemotherapy regimen after the operation, the curative effect was good.

Identification of pulmonary adenocarcinoma and benign lesions in isolated solid lung nodules based on a nomogram of intranodal and perinodal CT radiomic features.

To develop and validate a predictive model based on clinical radiology and radiomics to enhance the ability to distinguish between benign and malignant solitary solid pulmonary nodules. In this study, we retrospectively collected computed tomography (CT) images and clinical data of 286 patients with isolated solid pulmonary nodules diagnosed by surgical pathology, including 155 peripheral adenocarcinomas and 131 benign nodules. They were randomly divided into a training set and verification set at a 7:3 ratio, and 851 radiomic features were extracted from thin-layer enhanced venous phase CT images by outlining intranodal and perinodal regions of interest. We conducted preprocessing measures of image resampling and eigenvalue normalization. The minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (lasso) methods were used to downscale and select features. At the same time, univariate and multifactorial analyses were performed to screen clinical radiology features. Finally, we constructed a nomogram based on clinical radiology, intranodular, and perinodular radiomics features. Model performance was assessed by calculating the area under the receiver operating characteristic curve (AUC), and the clinical decision curve (DCA) was used to evaluate the clinical practicability of the models. Univariate and multivariate analyses showed that the two clinical factors of sex and age were statistically significant. Lasso screened four intranodal and four perinodal radiomic features. The nomogram based on clinical radiology, intranodular, and perinodular radiomics features showed the best predictive performance (AUC=0.95, accuracy=0.89, sensitivity=0.83, specificity=0.96), which was superior to other independent models. A nomogram based on clinical radiology, intranodular, and perinodular radiomics features is helpful to improve the ability to predict benign and malignant solitary pulmonary nodules.

Analysis of the Nursing Effect of Respiratory Critical Illness Based on Refined Nursing Management.

In order to improve the nursing effect of respiratory critical illness, this paper combines the refined nursing method to explore the nursing plan of respiratory critical illness. Moreover, this paper uses the variable control method to explore the effects of nursing management, combines the hospital patient samples to conduct a controlled trial analysis, and conducts sample grouping according to the random grouping method. The patients in the control group are managed by traditional nursing management methods, the patients in the test group are managed by refined nursing management methods, and other conditions are basically the same. In addition, the experiment process variable control is carried out according to the mathematical statistics method, and the reasonable statistics and data processing are carried out. Through the comparison method, we can see that the refined management method proposed in this paper has a good effect in the nursing of respiratory critical illness.

Investigating the role of circulating CXCR5-expressing CD8+ T-cells as a biomarker for bacterial infection in subjects with pneumonia.

BACKGROUND: Recently, lymphoid follicle-confined and circulating CD8+ T-cells expressing the C-X-C chemokine receptor type 5 (CXCR5) were described, which was involved in anti-virus immune response. However, the dynamics and role of circulating CXCR5-expressing CD8+ T-cells during bacterial infection is unknown. So, we asked whether CXCR5+ CD8+ T cells were also generated during bacterial infections in lower respiratory tract. METHODS: The clinical data of 65 pneumonia patients were analyzed. The patients were divided into groups as tuberculosis, bronchiectasis and community or hospital acquired pneumonia (CAP, HAP). The sputum/bronchial secretion or bronchoalveolar lavage fluid (BALF) samples were taken for microbiological examination. The procalcitonin (PCT) was used to evaluate disease severity of these groups and compared among patients. We characterized the number and phenotype (PD-1 and CD103) of CXCR5 + CD8+ T cells in the peripheral circulation by flow cytometry in all individuals and analyzed their association with the serum PCT level and disease severity. RESULTS: Patients were mainly infected with Escherichia coli, Acinetobacter baumannii, Klebsiella pneumonia (K.p), Pseudomonas aeruginosa, and Staphylococcus aureus. Of note is the finding that PCT was weakly correlated with severity of respiratory infections. Furthermore, it was revealed an increase of CXCR5-expressing CD8+ T cells in peripheral blood of un-controlled CAP and progressive HAP compared controlled CAP and HAP, respectively (P < 0.05). Strikingly, the circulating CXCR5-expressing CD8+ T-cells in K.p-infected group was higher than that non-K.p-infected group (P < 0.05). Meanwhile, the ratio of CXCR5 + CD8+/CD8 was positively correlated with PCT level (P < 0.05). In clinic, the determination of CXCR5-expressing CD8+ T-cells showed better results compared to PCT and can be useful for the prediction of exacerbation of CAP or HAP. Phenotypically, CXCR5+ CD8 + T cell expressed comparable level of inhibitory molecules PD-1 and lower CD103 compared to their CXCR5- counterparts. CONCLUSION: The circulating CXCR5-expressing CD8+ T-cell has diagnostic value for current pneumonia severity, and could act as a biomarker for identifying a bacteria-associated exacerbation. These cells may provide novel insight for the pathogenesis of pneumonia.