Background. There is no radiological measurement to estimate the severity of pediatrics juvenile dermatomyositis (JDM) with interstitial lung disease (ILD). We validated the effectiveness of CT scoring assessment in JDM patients with ILD. Aim. To establish a CT scoring system and calculate CT scores in JDM patients with ILD and to determine its reliability and the correlation with Krebs von den Lungen-6 (KL-6). Methods. The study totally enrolled 46 JDM-ILD patients and 16 JDM without ILD (non-ILD, NILD) patients. The chest CT images (7.0 ± 3.6 years; 32 male and 30 female) were all analyzed. CT scores of six lung zones were retrospectively calculated, included image pattern score and distribution range score. Image pattern score was defined as follows: increased broncho-vascular bundle (1 point); ground glass opacity (GGO) (2 points); consolidation (3 points); GGO with bronchiectasis (4 points); consolidation with bronchiectasis (5 points); and honeycomb lung (6 points). Distribution range score was defined as no infiltrate (0 point); <30% (1 point); 30%–60% (2 points); and ≥60% (3 points). Two pediatric radiologists reviewed all CT images independently. The ROC curve was established, and the optimal cutoff score for severity discrimination was set. Results. The agreement between two observers was excellent, and the ICC was 0.930 (95% CI 0.882–0.959, $p<0.01$ ). CT score and KL-6 level had a positive linear correlation (r = 0.784, $p<0.01$ ). However, the correlation between CT scores of different lung zone and KL-6 level was different. The KL-6 cut off level suggested for JDM with ILD was 209.0 U/ml, with 73.9% sensitivity and 87.5% specificity, and the area under curve was (AUC) 0.864 ( $p<0.01$ ). Conclusion. The CT scoring system we established, as a semiquantitative method, can effectively evaluate ILD in JDM-PM patients and provide reliable evidence for treatment.

This paper aims to compare the performance of the classical machine learning (CML) model and the deep learning (DL) model, and to assess the effectiveness of utilizing fusion radiomics from both CML and DL in distinguishing encephalitis from glioma in atypical cases. We analysed the axial FLAIR images of preoperative MRI in 116 patients pathologically confirmed as gliomas and clinically diagnosed with encephalitis. The 3 CML models (logistic regression (LR), support vector machine (SVM) and multi-layer perceptron (MLP)), 3 DL models (DenseNet 121, ResNet 50 and ResNet 18) and a deep learning radiomic (DLR) model were established, respectively. The area under the receiver operating curve (AUC) and sensitivity, specificity, accuracy, negative predictive value (NPV) and positive predictive value (PPV) were calculated for the training and validation sets. In addition, a deep learning radiomic nomogram (DLRN) and a web calculator were designed as a tool to aid clinical decision-making. The best DL model (ResNet50) consistently outperformed the best CML model (LR). The DLR model had the best predictive performance, with AUC, sensitivity, specificity, accuracy, NPV and PPV of 0.879, 0.929, 0.800, 0.875, 0.867 and 0.889 in the validation sets, respectively. Calibration curve of DLR model shows good agreement between prediction and observation, and the decision curve analysis (DCA) indicated that the DLR model had higher overall net benefit than the other two models (ResNet50 and LR). Meanwhile, the DLRN and web calculator can provide dynamic assessments. Machine learning (ML) models have the potential to non-invasively differentiate between encephalitis and glioma in atypical cases. Furthermore, combining DL and CML techniques could enhance the performance of the ML models.

Background. There is no accurate radiological measurement to estimate the severity of pediatrics acute respiratory distress syndrome (PARDS). We validated the effectiveness of an adult radiographic assessment of lung edema (RALE) score in PARDS. Aim. To assess the severity and prognosis of PARDS based on a chest radiograph (CXR) RALE scoring method. Methods. Pediatric Acute Lung Injury Consensus Conference (PALICC) criteria were used to diagnose PARDS. General demographics, pulmonary complications, and 28‐day mortality of the patients were recorded. Subgroups were compared by prognosis (survive and death) and etiology (infection and noninfection). Two observers calculated RALE independently. Each quadrant of CXR was scored by consolidation scores 0 (none alveolar opacity), 1 (extent 75%) and density scores 1 (hazy), 2 (moderate), and 3 (dense). Quadrant score equals consolidation score times density score. Total score equals to the sum of four quadrants scores. The ROC curve and survival curve were established, and the optimal cutoff score for discrimination prognosis was set. Results. 116 PARDS (72 boys and 44 girls) and 463 CXRs were enrolled. The median age was 25 months (5 months, 60.8 months) and with a mortality of 37.9% (44/116). The agreement between two independent observers was excellent (ICC = 0.98, 95% CI: 0.97–0.99). Day 3 score was independently associated with better survival ( ). The area under the curve of ROC was 0.773 (95% CI: 0.709–0.838). The cutoff score was 21 (sensitivity 71.7%, specificity 76.5%), and the hazard ratio (HR) was 9.268 (95% CI: 1.257–68.320). The pulmonary complication showed an HR of 3.678 (95% CI: 1.174–11.521) for the discrimination. Conclusion. CXR RALE score can be used in PARDS for discriminating the prognosis and has a better agreement among radiologist and pediatrician. PARDS with pulmonary complications, day 3 score whether greater than 21 points, have a better predictive effectiveness.