Quantification of Myocardial Dosimetry and Glucose Metabolism Using a 17-Segment Model of the Left Ventricle in Esophageal Cancer Patients Receiving Radiotherapy

Objective Previous studies have shown that increased cardiac uptake of(18)F-fluorodeoxyglucose (FDG) on positron emission tomography (PET) may be an indicator of myocardial injury after radiotherapy (RT). The primary objective of this study was to quantify cardiac subvolume dosimetry and(18)F-FDG uptake on oncologic PET using a 17-segment model of the left ventricle (LV) and to identify dose limits related to changes in cardiac(18)F-FDG uptake after RT. Methods Twenty-four esophageal cancer (EC) patients who underwent consecutive oncologic(18)F-FDG PET/CT scans at baseline and post-RT were enrolled in this study. The radiation dose and the(18)F-FDG uptake were quantitatively analyzed based on a 17-segment model. The(18)F-FDG uptake and doses to the basal, middle and apical regions, and the changes in the(18)F-FDG uptake for different dose ranges were analyzed. Results A heterogeneous dose distribution was observed, and the basal region received a higher median mean dose (18.36 Gy) than the middle and apical regions (5.30 and 2.21 Gy, respectively). Segments 1, 2, 3, and 4 received the highest doses, all of which were greater than 10 Gy. Three patterns were observed for the myocardial(18)F-FDG uptake in relation to the radiation dose before and after RT: an increase (5 patients), a decrease (13 patients), and no change (6 patients). In a pairing analysis, the(18)F-FDG uptake after RT decreased by 28.93 and 12.12% in the low-dose segments (0-10 Gy and 10-20 Gy, respectively) and increased by 7.24% in the high-dose segments (20-30 Gy). Conclusion The RT dose varies substantially within LV segments in patients receiving thoracic EC RT. Increased(18)F-FDG uptake in the myocardium after RT was observed for doses above 20 Gy.</div

NLRP3 Inflammasome Activation-Mediated Pyroptosis Aggravates Myocardial Ischemia/Reperfusion Injury in Diabetic Rats

The reactive oxygen species- (ROS-) induced nod-like receptor protein-3 (NLRP3) inflammasome triggers sterile inflammatory responses and pyroptosis, which is a proinflammatory form of programmed cell death initiated by the activation of inflammatory caspases. NLRP3 inflammasome activation plays an important role in myocardial ischemia/reperfusion (MI/R) injury. Our present study investigated whether diabetes aggravated MI/R injury through NLRP3 inflammasome-mediated pyroptosis. Type 1 diabetic rat model was established by intraperitoneal injection of streptozotocin (60 mg/kg). MI/R was induced by ligating the left anterior descending artery (LAD) for 30 minutes followed by 2 h reperfusion. H9C2 cardiomyocytes were exposed to high glucose (HG, 30 mM) conditions and hypoxia/reoxygenation (H/R) stimulation. The myocardial infarct size, CK-MB, and LDH release in the diabetic rats subjected to MI/R were significantly higher than those in the nondiabetic rats, accompanied with increased NLRP3 inflammasome activation and increased pyroptosis. Inhibition of inflammasome activation with BAY11-7082 significantly decreased the MI/R injury. In vitro studies showed similar effects, as BAY11-7082 or the ROS scavenger N-acetylcysteine, attenuated HG and H/R-induced H9C2 cell injury. In conclusion, hyperglycaemia-induced NLRP3 inflammasome activation may be a ROS-dependent process in pyroptotic cell death, and NLRP3 inflammasome-induced pyroptosis aggravates MI/R injury in diabetic rats

The Feasibility Study of Megavoltage Computed Tomographic (MVCT) Image for Texture Feature Analysis

Purpose: To determine whether radiomics texture features can be reproducibly obtained from megavoltage computed tomographic (MVCT) images acquired by Helical TomoTherapy (HT) with different imaging conditions.Methods: For each of the 195 textures enrolled, the mean intrapatient difference, which is considered to be the benchmark for reproducibility, was calculated from the MVCT images of 22 patients with early-stage non-small-cell lung cancer. Test–retest MVCT images of an in-house designed phantom were acquired to determine the concordance correlation coefficient (CCC) for these 195 texture features. Features with high reproducibility (CCC &gt; 0.9) in the phantom test–retest set were investigated for sensitivities to different imaging protocols, scatter levels, and motion frequencies using a wood phantom and in-vitro animal tissues.Results: Of the 195 features, 165 (85%) features had CCC &gt; 0.9. For the wood phantom, 124 features were reproducible in two kinds of scatter materials, and further investigations were performed on these features. For animal tissues, 108 features passed the criteria for reproducibility when one layer of scatter was covered, while 106 and 108 features of in-vitro liver and bone passed with two layers of scatter, respectively. Considering the effect of differing acquisition pitch (AcP), 97 features extracted from wood passed, while 103 and 59 features extracted from in-vitro liver and bone passed, respectively. Different reconstruction intervals (RI) had a small effect on the stability of the feature value. When AcP and RI were held consistent without motion, all 124 features calculated from wood passed, and a majority (122 of 124) of the features passed when imaging with a “fine” AcP with different RIs. However, only 55 and 40 features passed with motion frequencies of 20 and 25 beats per minute, respectively.Conclusion: Motion frequency has a significant impact on MVCT texture features, and features from MVCT were more reproducibility in different scatter conditions than those from CBCT. Considering the effects of AcP and RI, the scanning protocols should be kept consistent when MVCT images are used for feature analysis. Some radiomics features from HT MVCT images are reproducible and could be used for creating clinical prediction models in the future

Random Violation Risk Degree Based Service Channel Routing Mechanism in Smart Grid

Smart gird, integrated power network with communication network, has brought an innovation of traditional power for future green energy. Optical fiber technology and synchronous digital hierarchy (SDH) technology is widely used in smart grid communication transmission network. It is a challenge to reduce impact of the availability of smart grid communication services caused by random failures and random time to repair. Firstly, we create a service channel violation risk degree (SCVRD) model to precisely track the violation risk change of communication service channel. It is denoted by the probability of service channel cumulative failure duration exceeding the prescribed duration. Secondly, a service channel violation risk degree routing mechanism is proposed to improve the availability of communication service. At last, the simulation is implemented with MATLAB and network data in one province are used as data instance. The simulation results show that the average service channel failure rate of availability-aware routing based on statistics (AAR-OS) algorithm and risk-aware provisioning algorithm are reduced by 15% and 6%, respectively

Research on multi-model imaging machine learning for distinguishing early hepatocellular carcinoma

Abstract Objective To investigate the value of differential diagnosis of hepatocellular carcinoma (HCC) and non-hepatocellular carcinoma (non-HCC) based on CT and MR multiphase radiomics combined with different machine learning models and compare the diagnostic efficacy between different radiomics models. Background Primary liver cancer is one of the most common clinical malignancies, hepatocellular carcinoma (HCC) is the most common subtype of primary liver cancer, accounting for approximately 90% of cases. A clear diagnosis of HCC is important for the individualized treatment of patients with HCC. However, more sophisticated diagnostic modalities need to be explored. Methods This retrospective study included 211 patients with liver lesions: 97 HCC and 124 non-hepatocellular carcinoma (non-HCC) who underwent CT and MRI. Imaging data were used to obtain imaging features of lesions and radiomics regions of interest (ROI). The extracted imaging features were combined to construct different radiomics models. The clinical data and imaging features were then combined with radiomics features to construct the combined models. Support Vector Machine (SVM), K-nearest Neighbor (KNN), RandomForest (RF), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Multilayer Perceptron (MLP) six machine learning models were used for training. Five-fold cross-validation was used to train the models, and ROC curves were used to analyze the diagnostic efficacy of each model and calculate the accuracy rate. Model training and efficacy test were performed as before. Results Statistical analysis showed that some clinical data (gender and concomitant cirrhosis) and imaging features (presence of envelope, marked enhancement in the arterial phase, rapid contouring in the portal phase, uniform density/signal and concomitant steatosis) were statistical differences (P < 0.001). The results of machine learning models showed that KNN had the best diagnostic efficacy. The results of the combined model showed that SVM had the best diagnostic efficacy, indicating that the combined model (accuracy 0.824) had better diagnostic efficacy than the radiomics-only model. Conclusions Our results demonstrate that the radiomic features of CT and MRI combined with machine learning models enable differential diagnosis of HCC and non-HCC (malignant, benign). The diagnostic model with dual radiomic had better diagnostic efficacy. The combined model was superior to the radiomic model alone

Prediction of distant metastasis in esophageal cancer using a radiomics–clinical model

Abstract Background Distant metastasis, which occurs at a rate of 25% in patients with esophageal cancer (EC), has a poor prognosis, with previous studies reporting an overall survival of only 3–10 months. However, few studies have been conducted to predict distant metastasis in EC, owing to a dearth of reliable biomarkers. The purpose of this study was to develop and validate an accurate model for predicting distant metastasis in patients with EC. Methods A total of 299 EC patients were enrolled and randomly assigned to a training cohort (n = 207) and a validation cohort (n = 92). Logistic univariate and multivariate regression analyses were used to identify clinical independent predictors and create a clinical nomogram. Radiomic features were extracted from contrast-enhanced computed tomography (CT) images taken prior to treatment, and least absolute shrinkage and selection operator (Lasso) regression was used to screen the associated features, which were then used to develop a radiomic signature. Based on the screened features, four machine learning algorithms were used to build radiomics models. The joint nomogram with radiomic signature and clinically independent risk factors was developed using the logical regression algorithm. All models were validated and compared by discrimination, calibration, reclassification, and clinical benefit. Results Multivariable analyses revealed that age, N stage, and degree of pathological differentiation were independent predictors of distant metastasis, and a clinical nomogram incorporating these factors was established. A radiomic signature was developed by a set of sixteen features chosen from 851 radiomic features. The joint nomogram incorporating clinical factors and radiomic signature performed better [AUC(95% CI) 0.827(0.742–0.912)] than the clinical nomogram [AUC(95% CI) 0.731(0.626–0.836)] and radiomics predictive models [AUC(95% CI) 0.754(0.652–0.855), LR algorithms]. Calibration and decision curve analyses revealed that the radiomics–clinical nomogram outperformed the other models. In comparison with the clinical nomogram, the joint nomogram's NRI was 0.114 (95% CI 0.075–0.345), and its IDI was 0.071 (95% CI 0.030–0.112), P = 0.001. Conclusions We developed and validated the first radiomics–clinical nomogram for distant metastasis in EC which may aid clinicians in identifying patients at high risk of distant metastasis

The impact of b-values on diffusion-weighted imaging radiomic features and a retrospective study to characterize the hepatic cirrhosis

DWI RADIOMICS OF HEPATIC CIRRHOSIS-PEER

Study of dynamic CT radiomic features to assist the accurate diagnosis of HCC

<p><b>Figure 1 </b><a></a><a>Delineation of an HCC in each of the five phases on CT images<b> (</b>A: plain scan phase (PSP), B: arterial phase (AP), C: portal venous phase (PVP), D: </a><a>venous</a> <a>phase</a> (VP), E: delayed phase (DP))<b></b></p> <p><b>Figure 2</b> The variation trend of the GTV radiomic features throughout the 5 phases</p> <p><b>Figure 3</b> <a></a><a>The comparison of dynamic trends of the CT radiomic features of the GTV and normal liver tissue</a></p> <p><b>Figure 4</b> ROC curves of 4 radiomic features in the five phases <a></a><a>(A: plain scan phase, B: arterial phase, C: portal venous phase, D: </a><a>venous</a> <a>phase</a>, E: delayed phase）</p> <p><b>Table 1</b> The radiomic feature change rate differences between normal liver tissue and the GTV in the different time phases</p> <p><b>Table 2</b> The AUC values and Youden index of 4 radiomic features in the different phases</p> <p><a><b>Table S1</b></a> <a></a><a>The extracted radiomic features in this study</a></p