RESUMO
Background: Using blood-derived circular RNAs (circRNAs) may be an efficient tool for noninvasive fluid biopsy in diagnosing non-small cell lung cancer (NSCLC). However, no relevant systemic meta-analysis has been conducted so far to support the diagnostic value of using blood-derived circRNAs in NSCLC clinically. The aim of this study is to clarify the issue through a meta-analysis. Methods: A systematic search strategy was used to search relevant literature in the databases of PubMed, Web of Science, and Cochrane Library from 2017 to 2022. The relationship between the diagnostic accuracy of circRNAs and NSCLC was analyzed. For the purpose of evaluating the quality of the literature, Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) was used. Statistical analyses were assessed using Stata software (version 17.0) and META-DISC (version 1.4). Results: The meta-analysis included 1,093 patients with NSCLC and 959 controls. Results are as follows: pooled sensitivity, 0.78 (95% CI = 0.71-0.83, I2 = 71.86); pooled specificity, 0.76 (95% CI = 0.70-0.82, I2 = 70.12); pooled positive likelihood ratio (PLR), 3.3 (95% CI = 2.6-4.2, I2 = 37.56); pooled negative likelihood ratio (NLR), 0.29 (95% CI = 0.23-0.37, I2 = 64.67); diagnostic odds ratio (DOR), 11.42 (95% CI = 7.88-16.56, I2 = 99.05); area under the receiver operating characteristic curve (AUC), 0.84 (95% CI = 0.80-0.87). Based on the subgroup analysis, it appears that the heterogeneity is primarily caused by the NSCLC subgroup. Conclusion: circRNAs are highly useful diagnostic biomarkers for NSCLC in China. Further prospective studies on the diagnostic value of circRNAs should be conducted in multiple countries. Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42022323804.
RESUMO
BACKGROUND: To evaluate the role of radiomics based on magnetic resonance imaging (MRI) in the biological activity of hepatic alveolar echinococcosis (HAE). METHODS: In this study, 90 active and 46 inactive cases of HAE patients were analyzed retrospectively. All the subjects underwent MRI and positron emission tomography computed tomography (PET-CT) before surgery. A total of 1409 three-dimensional radiomics features were extracted from the T2-weighted MR images (T2WI). The inactive group in the training cohort was balanced via the synthetic minority oversampling technique (SMOTE) method. The least absolute shrinkage and selection operator (LASSO) regression method was used for feature selection. The machine learning (ML) classifiers were logistic regression (LR), multilayer perceptron (MLP), and support vector machine (SVM). We used a fivefold cross-validation strategy in the training cohorts. The classification performance of the radiomics signature was evaluated using receiver operating characteristic curve (ROC) analysis in the training and test cohorts. RESULTS: The radiomics features were significantly associated with the biological activity, and 10 features were selected to construct the radiomics model. The best performance of the radiomics model for the biological activity prediction was obtained by MLP (AUC = 0.830 ± 0.053; accuracy = 0.817; sensitivity = 0.822; specificity = 0.811). CONCLUSIONS: We developed and validated a radiomics model as an adjunct tool to predict the HAE biological activity by combining T2WI images, which achieved results nearly equal to the PET-CT findings.
