Purpose. The aim of this study is to develop and compare performance of radiomics signatures using texture features extracted from noncontrast enhanced CT (NECT) and contrast enhanced CT (CECT) images for preoperative predicting risk categorization and clinical stage of thymomas. Materials and Methods. Between January 2010 and October 2018, 199 patients with surgical resection and histopathologically confirmed thymoma were enrolled in this retrospective study. We extracted 841 radiomics features separately from volume of interest (VOI) in NECT and CECT images. The features with poor reproducibility and highly redundancy were removed. Then a least absolute shrinkage and selection operator method (LASSO) logistic regression model with 10-fold cross validation was used for further feature selection and radiomics signatures build. The predictive performances of radiomics signatures were assessed by receiver operating characteristic (ROC) analysis. The areas under the receiver operating characteristic curve (AUC) between radiomics signatures were compared by using Delong test. Result. In differentiating high risk thymomas from low risk thymomas, the AUC, sensitivity, and specificity were 0.801(95% CI 0.740–0.863), 0.752 and 0.767 for radiomics signature based on NECT images, and 0.827 (95% CI 0.771 -0.884), 0.798, and 0.722 for radiomics signature based on CECT images. But there was no significant difference (p=0.365) between them. In differentiating advanced stage thymomas from early stage thymomas, the AUC, sensitivity, and specificity were 0.829 (95%CI 0.757-0.900), 0.712, and 0.806 for radiomics signature based on NECT images and 0.860 (95%CI 0.803-0.917), 0.699, and 0.889 for radiomics signature based on CECT images. There was no significant difference (p=0.069) between them. The accuracy was 0.819 for radiomics signature based on NECT images, 0.869 for radiomics signature based on CECT images, and 0.779 for radiologists. Both radiomics signatures had a better performance than radiologists. But there was significant difference (p = 0.025) only between CECT radiomics signature and radiologists. Conclusion. Radiomics signatures based on texture analysis from NECT and CECT images could be utilized as noninvasive biomarkers for differentiating high risk thymomas from low risk thymomas and advanced stage thymomas from early stage thymoma. As a quantitative method, radiomics signature can provide complementary diagnostic information and help to plan personalized treatment for patients with thymomas.

High-quality evidence suggests that TACE plus RFA improve the survival rates compared with RFA alone for patients with HCC larger than 3 cm.

Abstract.Computed tomography (CT)-guided percutaneous fine needle biopsy is a common method for lung biopsy. The objective of this study was to investigate factors affecting the accuracy and safety of CT-guided percutaneous lung biopsy of nodules ≤30 mm in diameter. Between January 2013 and March 2014, 155 patients underwent a CT-guided percutaneous biopsy procedure on an intrapulmonary solitary nodule measuring ≤30 mm in diameter. Prospectively collected data were retrospectively reviewed and examined for the influence of clinical and pathological characteristics (age, gender, smoking status, adhesion of nodule to the pleura, puncture depth, nodule size and time of biopsy) on the accuracy of biopsy and incidence of pneumothorax and hemorrhage. The accuracy of CT-guided biopsy was 90.3% (140/155). Biopsies predominantly contained lung adenocarcinoma (114/140; 81.4%) or squamous cell carcinoma of the lung (10/140; 7.1%). Accuracy was significantly dependent on nodule size, ranging in accuracy from 85 to 97% for patients with nodule diameters of ≤20 or 21-30 mm, respectively (P<0.05). Pleural adherence of the nodule significantly increased the accuracy of the biopsy (P<0.05). Patients with a nodule of 11-20 mm in diameter had a significantly higher incidence of pneumothorax compared with patients with a smaller nodule (P=0.013). In conclusion, the nodule size and adhesion to the pleura influenced the accuracy of CT-guided biopsy of intrapulmonary nodules that were ≤30 mm in diameter. Nodule size may also affect the incidence of severe complications. CT-guided percutaneous lung biopsy has a high accuracy and is easy and safe to conduct for intrapulmonary solitary nodules of ≤30 mm in diameter.

Our study indicates that TACE is feasible and potentially efficacious in HCC patients with PVTT, and identifies factors that may predict the prognosis of these patients.

This retrospective study investigated the clinical application of sequential therapy with transarterial chemoembolization (TACE) and CT-guided radiofrequency ablation (RFA) using a bipolar needle in treating hepatocellular carcinoma (HCC) tumors of different sizes. The study included patients (N = 46) with HCC from Shengjing Hospital of China Medical University who had received TACE and RFA from November 2012 to November 2013. Eligible patients had an Eastern Cooperative Oncology Group (ECOG) score of 0-1, a Child-Pugh grade of A-B, and no contradictions for TACE and/or RFA. Fifty one hepatic lesions of varying sizes were treated with TACE followed by RFA. Clinical response and 1- and 2-year survival rates were assessed. The frequency of complete and incomplete ablation following therapy was significantly different across the varying RFA pin numbers and the maximum diameter of the lesion (p ≤ 0.001). A greater percentage (97.3%) of lesions that were ≤3 cm in diameter were completely ablated compared with lesions that were 3-5 cm (88.9%) and >5 cm in diameter (20%). The median survival time of patients was 16.5 months, and the 1- and 2-year survival rates were 95.7% and 69.3%, respectively. There were only a limited number of complications, all of which were minor. These included hemothorax (4.3%), abdominal hemorrhage (10.9%), and abdominal hemorrhage with minor pneumothorax (2.2%). This study found that the sequential treatment with TACE and CT-guided RFA using a bipolar needle is effective and well tolerated in patients with HCC and that the effectiveness of treatment is dependent on tumor size.