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.

Background To evaluate the application of Arterial Enhancement Fraction (AEF) texture features in predicting the tumor response in Hepatocellular Carcinoma (HCC) treated with Transarterial Chemoembolization (TACE) by means of texture analysis. Methods HCC patients treated with TACE in Shengjing Hospital of China Medical University from June 2018 to December 2019 were retrospectively enrolled in this study. Pre-TACE Contrast Enhanced Computed Tomography (CECT) and imaging follow-up within 6 months were both acquired. The tumor responses were categorized according to the modified RECIST (mRECIST) criteria. Based on the CECT images, Region of Interest (ROI) of HCC lesion was drawn, the AEF calculation and texture analysis upon AEF values in the ROI were performed using CT-Kinetics (C.K., GE Healthcare, China). A total of 32 AEF texture features were extracted and compared between different tumor response groups. Multi-variate logistic regression was performed using certain AEF features to build the differential models to predict the tumor response. The Receiver Operator Characteristic (ROC) analysis was implemented to assess the discriminative performance of these models. Results Forty-five patients were finally enrolled in the study. Eight AEF texture features showed significant distinction between Improved and Un-improved patients (p < 0.05). In multi-variate logistic regression, 9 AEF texture features were applied into modeling to predict “Improved” outcome, and 4 AEF texture features were applied into modeling to predict “Un-worsened” outcome. The Area Under Curve (AUC), diagnostic accuracy, sensitivity, and specificity of the two models were 0.941, 0.911, 1.000, 0.826, and 0.824, 0.711, 0.581, 1.000, respectively. Conclusions Certain AEF heterogeneous features of HCC could possibly be utilized to predict the tumor response to TACE treatment.

No abstract

This study aims to explore the imaging-clinic relationship and an optional imaging biomarker of hepatocellular carcinoma (HCC) by using texture analysis on arterial enhancement fraction (AEF). Materials and Methods: The HCC patients treated in No. 2 Interventional Ward, ShengJing Hospital of China Medical University from June 2018 to June 2019 were enrolled, for whom tri-phasic enhanced CT scans were acquired. Perfusion analysis and texture analysis were then performed on the tri-phasic enhanced CT images. After the region of interest (ROI) of viable HCC was drawn, 13 AEF textures describing the values distribution were conducted. A between-groups comparison of AEF textures was made where the cases had grouping properties, a correlation analysis was made between AEF textures and alpha-fetoprotein (AFP) as well as other clinical data which were digital, and regression analysis was made when a significant correlation was found. SPSS 19.0 (IBM) was utilized for statistical analysis; a significant difference was considered when P < 0.05. Results: Twenty-five HCC patients were enrolled. Several AEF textures were found to have a correlation with clinical features, including previous surgery history, age, glutamic oxaloacetylase, indirect bilirubin, creatinine, and AFP. The majority of AEF textures (up to 9/13) were found to have a correlation with AFP (SD, variance, uniformity, energy, entropy, inertia, correlation, inverse difference moment, and cluster prominence), while six or seven textures have a linear or cubic relationship with AFP (SD, variance, uniformity, inertia, correlation, cluster prominence, plus inverse difference moment). Conclusion: The AEF textures of HCC are strongly correlated with and are impacted by AFP, which may enable AEF to act as an optional imaging biomarker of HCC.