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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.

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.

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BackgroundThe 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 presentationWe 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.ConclusionPatients 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.