The Diagnostic Efficacy of the American College of Radiology (ACR) Thyroid Imaging Report and Data System (TI-RADS) and the American Thyroid Association (ATA) Risk Stratification Systems for Thyroid Nodules

Background. This study is aimed at evaluating the diagnostic efficacy of ultrasound-based risk stratification for thyroid nodules in the American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) and the American Thyroid Association (ATA) risk stratification systems. Methods. 286 patients with thyroid cancer were included in the tumor group, with 259 nontumor cases included in the nontumor group. The ACR TI-RADS and ATA risk stratification systems assessed all thyroid nodules for malignant risks. The diagnostic effect of ACR and ATA risk stratification system for thyroid nodules was evaluated by receiver operating characteristic (ROC) analysis using postoperative pathological diagnosis as the gold standard. Results. The distributions and mean scores of ACR and ATA rating risk stratification were significantly different between the tumor and nontumor groups. The lesion diameter > 1 cm subgroup had higher malignant ultrasound feature rates detected and ACR and ATA scores. A significant difference was not found in the ACR and ATA scores between patients with or without Hashimoto’s disease. The area under the receiver operating curve (AUC) for the ACR TI-RADS and the ATA systems was 0.891 and 0.896, respectively. The ACR had better specificity (0.90) while the ATA system had higher sensitivity (0.92), with both scenarios having almost the same overall diagnostic accuracy (0.84). Conclusion. Both the ACR TI-RADS and the ATA risk stratification systems provide a clinically feasible thyroid malignant risk classification, with high thyroid nodule malignant risk diagnostic efficacy.

Circulating Exosomal circRNAs Contribute to Potential Diagnostic Value of Large Artery Atherosclerotic Stroke

Large artery atherosclerotic (LAA) stroke is closely associated with atherosclerosis, characterized by the accumulation of immune cells. Early recognition of LAA stroke is crucial. Circulating exosomal circRNAs profiling represents a promising, noninvasive approach for the detection of LAA stroke. Exosomal circRNA sequencing was used to identify differentially expressed circRNAs between LAA stroke and normal controls. From a further validation stage, the results were validated using RT-qPCR. We then built logistic regression models of exosomal circRNAs based on a large replication stage, and receiver operating characteristic (ROC) curves were constructed to assess the diagnostic efficacy. Using exosomal circRNA sequencing, large sample validation, and diagnostic model construction revealed that exosomal circ_0043837 and circ_ 0001801were independent predictive factors for LAA stroke, and had better diagnostic efficacy than plasma circRNAs. In the atherosclerotic group (AS), we developed a nomogram for clinical use that integrated the two-circRNA-based risk factors to predict which patients might have the risk of plaque rupture. Circulating exosomal circRNAs profiling identifies novel predictive biomarkers for the LAA stroke and plaque rupture, with superior diagnostic value than plasma circRNAs. It might facilitate the prevention and better management of this disease.

Delaying Intraoral Radiographs during the COVID-19 Pandemic: A Conundrum

Background. The COVID-19 pandemic has made dentists very assiduous about cross-infection during dental treatment, thereby delaying dental radiographs for treatment. However, patients needing dental emergency treatment in the ongoing pandemic require relevant intra/extraoral dental radiography for adequate diagnosis and treatment planning. Methods. This article is aimed at adding to the hot debate: Is delay for intraoral radiographs justified or a possible proxy? As a narrative review, it provides an insight into the reasons for delaying intra-oral dental radiographs during in the pandemic and options of the nontraditional radiographic techniques available until the pandemic subsides. Discussion and Conclusion. Cross-contamination concerns through respiratory droplets grow while using intraoral film holders that stimulate gag reflex, coughing, saliva secretion, and if proper disinfection protocols are not applied. Since the patients’ acquiring emergency dental treatment cannot be neglected, the return-to-work guidelines by the health regulatory bodies urge to prioritize extraoral radiographic imaging techniques to curb the infection, offering the best diagnostic efficacy. The dental professionals can consider cone-beam computed tomography (CBCT) scans and sectional dental panoramic radiographs (SDPRs), followed by a risk assessment for COVID-19, a safer modality in reducing cross-contamination and assuring an innocuous environment for both patient and coworkers.

Comprehensive Analysis of Hub Genes Associated With Competing Endogenous RNA Networks in Stroke Using Bioinformatics Analysis

Background: Acute ischemic stroke (AIS) is the second leading cause of death and the third leading cause of disability worldwide. Long noncoding RNAs (lncRNAs) are promising biomarkers for the early diagnosis of AIS and closely participate in the mechanism of stroke onset. However, studies focusing on lncRNAs functioning as microRNA (miRNA) sponges to regulate the mRNA expression are rare and superficial.Methods: In this study, we systematically analyzed the expression profiles of lncRNA, mRNA (GSE58294), and miRNA (GSE110993) from the GEO database. Gene ontology (GO) analysis was performed to reveal the functions of differentially expressed genes (DEGs), and we used weighted gene co-expression network analysis (WGCNA) to investigate the relationships between clinical features and expression profiles and the co-expression of miRNA and lncRNA. Finally, we constructed a lncRNA–miRNA–mRNA competing endogenous RNA (ceRNA) network with selected DEGs using bioinformatics methods and obtained ROC curves to assess the diagnostic efficacy of differentially expressed lncRNAs (DElncRNAs) and differentially expressed mRNAs (DEmRNAs) in our network. The GSE22255 dataset was used to confirm the diagnostic value of candidate genes.Results: In total, 199 DElncRNAs, 2068 DEmRNAs, and 96 differentially expressed miRNAs were detected. The GO analysis revealed that DEmRNAs primarily participate in neutrophil activation, neutrophil degranulation, vacuolar transport, and lysosomal transport. WGCNA screened out 16 lncRNAs and 195 mRNAs from DEGs, and only eight DElncRNAs maintained an area under the curve higher than 0.9. By investigating the relationships between lncRNAs and mRNAs, a ceRNA network containing three lncRNAs, three miRNAs, and seven mRNAs was constructed. GSE22255 confirmed that RP1-193H18.2 is more advantageous for diagnosing stroke, whereas no mRNA showed realistic diagnostic efficacy.Conclusion: The ceRNA network may broaden our understanding of AIS pathology, and the candidate lncRNA from the ceRNA network is assumed to be a promising therapeutic target and diagnostic biomarker for AIS.

Identification of pathological subtypes of early lung adenocarcinoma based on artificial intelligence parameters and CT signs

Objective: To explore the value of quantitative parameters of artificial intelligence and computed tomography (CT) signs in identifying pathological subtypes of lung adenocarcinoma appearing as ground-glass nodules (GGNs). Methods: CT images of 224 GGNs from 210 individuals were collected retrospectively and pathologically classified into atypical adenomatous hyperplasia (AAH)/adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) groups. Artificial intelligence was used to identify GGNs and to obtain quantitative parameters, and CT signs were recognized manually. The mixed predictive model based on logistic multivariate regression was evaluated. Results: Of the 224 GGNs, 55, 93, and 76 were AAH/AIS, MIA, IAC, respectively. In terms of artificial intelligence parameters, from AAH/AIS to MIA, and IAC, there was a gradual increase in two-dimensional mean diameter, three-dimensional mean diameter, mean CT value, maximum CT value, and volume of GGNs (all P < 0.0001). Except for the CT signs of the location, and the tumor-lung interface, there were significant differences among the three groups in the density type, shape, vacuole signs, air bronchogram, lobulation, spiculation, pleural indentation, and vascular convergence signs (all P < 0.05). The areas under the curve (AUC) of predictive model 1 for identifying the AAH/AIS and MIA and model 2 for identifying MIA and IAC were 0.779 and 0.918, respectively, which were greater than the quantitative parameters independently (all P < 0.05). Conclusion: Artificial intelligence parameters are valuable for identifying subtypes of early lung adenocarcinoma, and when combined with CT signs to improve its diagnostic efficacy.

Invasive Prediction of Ground Glass Nodule Based on Clinical Characteristics and Radiomics Feature

Objective: To explore the diagnostic value of CT radiographic images and radiomics features for invasive classification of lung adenocarcinoma manifesting as ground-glass nodules (GGNs) in computer tomography (CT).Methods: A total of 312 GGNs were enrolled in this retrospective study. All GGNs were randomly divided into training set (n = 219) and test set (n = 93). Univariate and multivariate logistic regressions were used to establish a clinical model, while the minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) algorithm were used to select the radiomics features and construct the radiomics model. A combined model was finally built by combining these two models. The performance of these models was assessed in both training and test set. A combined nomogram was developed based on the combined model and evaluated with its calibration curves and C-index.Results: Diameter [odds ratio (OR), 1.159; p ＜ 0.001], lobulation (OR, 2.953; p = 0.002), and vascular changes (OR, 3.431; p ＜ 0.001) were retained as independent predictors of the invasive adenocarcinoma (IAC) group. Eleven radiomics features were selected by mRMR and LASSO method to established radiomics model. The clinical model and radiomics mode showed good predictive ability in both training set and test set. When two models were combined, the diagnostic area under the curve (AUC) value was higher than the single clinical or radiomics model (training set: 0.86 vs. 0.83 vs. 0.82; test set: 0.80 vs. 0.78 vs. 0.79). The constructed combined nomogram could effectively quantify the risk degree of 3 image features and Rad score with a C-index of 0.855 (95%: 0.805∼0.905).Conclusion: Radiographic and radiomics features show high accuracy in the invasive diagnosis of GGNs, and their combined analysis can improve the diagnostic efficacy of IAC manifesting as GGNs. The nomogram, serving as a noninvasive and accurate predictive tool, can help judge the invasiveness of GGNs prior to surgery and assist clinicians in creating personalized treatment strategies.

Diagnostic Efficacy of Ultrasound, Cytology, and BRAFV600E Mutation Analysis and Their Combined Use in Thyroid Nodule Screening for Papillary Thyroid Microcarcinoma

BackgroundUltrasound, cytology, and BRAFV600E mutation analysis were applied as valuable tools in the differential diagnosis of thyroid nodules. The aim of the present study was to evaluate the diagnostic efficiency of the three methods and their combined use in screening for papillary thyroid microcarcinoma (PTMC).MethodsA total of 1,081 patients with 1,157 thyroid nodules (0.5–1 cm in maximum diameter) classified as thyroid imaging reporting and data system (TIRADS) 4–5 were recruited. All patients underwent ultrasound, fine-needle aspiration (FNA) examination, and an additional BRAFV600E mutation test. TIRADS and Bethesda System for Reporting Thyroid Cytopathology (BSRTC) were adopted to judge the ultrasound and cytological results. The receiver operating characteristic (ROC) curve was established to assess the diagnostic values of different methods.ResultsOf the 1,157 nodules, 587 were benign and 570 were PTMCs. BRAFV600E mutation test had highest sensitivity (85.4%), specificity (97.1%), accuracy (91.4%), and area under the ROC curve (Az) value (0.913) among the three methods. The combination of BSRTC and BRAFV600E mutation analysis yielded a considerably high sensitivity (96.0%), accuracy (94.3%), and negative predictive value (95.9%) than either BSRTC or BRAFV600E mutation alone (P < 0.0001 for all comparisons). Of all the methods, the combined use of the three methods produced the best diagnostic performance (Az = 0.967), which was significantly higher than that (Az = 0.943) for the combination of BSRTC and BRAFV600E mutation (P < 0.0001). The diagnostic accuracy of the molecular method in the 121 nodules with indeterminate cytology was 90.1% (109/121), which was significantly higher than that of TIRADS classification, 74.4% (90/121) (P = 0.002).ConclusionThe combined use of ultrasound, cytology, and BRAFV600E mutation analysis is the most efficient and objective method for diagnosing PTMC. Both BRAFV600E mutation and TIRADS classification are potentially useful adjuncts to differentiate thyroid nodules, especially indeterminate samples classified as BSRTC III.