RESUMO
Background: Anxiety-driven clinical interventions have been queried due to the nondeterminacy of pure ground-glass nodules (pGGNs). Although radiomics and radiogenomics aid diagnosis, standardization and reproducibility challenges persist. We aimed to assess a risk score system for invasive adenocarcinoma in pGGNs. Methods: In a retrospective, multi-center study, 772 pGGNs from 707 individuals in The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital were grouped into training (509 patients with 558 observations) and validation (198 patients with 214 observations) sets consecutively from January 2017 to November 2021. An additional test set of 143 observations in Hainan Cancer Hospital was analyzed in the same period. Computed tomography (CT) signs and clinical features were manually collected, and the quantitative parameters were achieved by artificial intelligence (AI). The positive cutoff score was ≥3. Risk scores system 3 combined carcinoma history, chronic obstructive pulmonary disease (COPD), maximum diameters, nodule volume, mean CT values, type II or III vascular supply signs, and other radiographic characteristics. The evaluation included the area under the curves (AUCs), accuracy, sensitivity, specificity, positive predictive values (PPV), and negative predictive values (NPV) for both the risk score systems 1, 2, 3 and the AI model. Results: The risk score system 3 [AUC, 0.840; 95% confidence interval (CI): 0.789-0.890] outperformed the AI model (AUC, 0.553; 95% CI: 0.487-0.619), risk score system 1 (AUC, 0.802; 95% CI: 0.754-0.851), and risk score system 2 (AUC, 0.816; 95% CI: 0.766-0.867), with 88.0% (0.850-0.904) accuracy, 95.6% (0.932-0.972) PPV, 0.620 (0.535-0.702) NPV, 89.6% (0.864-0.920) sensitivity, and 80.6% (0.717-0.872) specificity in the training sets. In the validation and test sets, risk score system 3 performed best with AUCs of 0.769 (0.678-0.860) and 0.801 (0.669-0.933). Conclusions: An AI-based risk scoring system using quantitative image parameters, clinical features, and radiographic characteristics effectively predicts invasive adenocarcinoma in pulmonary pGGNs.
RESUMO
Background: With the increasing incidence of gynecological ovarian tumors, the differential diagnosis of benign and malignant ovarian tumors is of great significance for subsequent treatment. Currently, ovarian examinations commonly use computed tomography (CT) or magnetic resonance imaging (MRI). This study sought to compare the value of CT and MRI in differentiating between benign and malignant ovarian tumors. Methods: The PubMed, Cochrane Central Register of Controlled Trials, Embase, Web of Science, China National Knowledge Infrastructure, Wanfang, and Weipu databases were searched for published articles using the following terms "CT" or "Computed Tomography" or "MRI" or "Magnetic Resonance imaging" and "ovarian cancer" or "ovarian tumor" or "ovarian neoplasm" or "adnexal mass" or "adnexal lesion". The articles were screened and the data were extracted based on the inclusion and exclusion criteria. The Quality Assessment of Diagnostic Accuracy Studies-2 recommended by the Cochrane Collaboration was used to assess the methodological quality of the included studies, and the network meta-analysis was performed by Stata 15.0. Results: The results showed that the overall sensitivity and specificity of CT were 0.79 [95% confidence intervals (CI): 0.70-0.87] and 0.87 (95% CI: 0.80-0.92), respectively. The overall sensitivity and specificity of MRI were 0.94 (95% CI: 0.91-0.95) and 0.91 (95% CI: 0.90-0.93), respectively. The area under the curve of the CT and MRI summary receiver operating characteristics were 0.9016 and 0.9764, respectively. The positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio of CT were 5.26 (95% CI: 2.78-9.93), 0.26 (95% CI: 0.13-0.50), and 22.19 (95% CI: 7.54-65.30), respectively. The positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio of MRI were 8.69 (95% CI: 5.06-14.92), 0.07 (95% CI: 0.04-0.13), and 146.19 (95% CI: 68.88-310.24), respectively. Conclusions: Compared to CT, MRI has a stronger ability to differentiate between benign and malignant ovarian tumors. It's a promising non-radiological imaging technique and a more favorable choice for patients with ovarian tumors. However, in the future, large-sample, multi-center prospective studies need to be conducted to compare the performance of MRI and CT in distinguishing between benign and malignant ovarian tumors.
