Objective To describe some unenhanced spiral CT imaging signs that can clue to acute pulmonary embolism.Methods By retrospectively analyzing spiral CT imaging of acute pulmonary embolism proved by clinical treatment in 49 cases, some noticeable abnormal imaging signs were found.Results Among the 49 cases, 10 cases had abnormal attenuation changes in the pulmonary arteries, 6 of them had local high-attenuation centrally and 4 of them had local low-attenuation centrally.Conclusion The final diagnosis of acute pulmonary embolism depends on enhanced CT scan.But for cases that they could not use contrast media or cases that they only underwent unenhanced CT because of nonspecific heart-pulmonary symptom, abnormal attenuation changes of pulmonary arteries can clue to acute pulmonary embolism.

Objective To discuss the noticeable problems in the radiological diagnosis of thoracic sarcoidosis through retrospective analysis of misdiagnosis. Methods Imaging examinations of 32 misdiagnosed cases with thoracic sarcoidosis including chest radiography, CT, and their clinical data were reviewed. The final diagnosis was made by pathology (9 cases) and clinical therapy (23 cases). Results Enlarged thoracic lymph nodes were detected in all cases. 23 of them presented mediastinal lymph node enlargement associated with bilateral hilar lymph node enlargement, 5 of them had mediastinal lymph node enlargement and unilateral hilar lymph node enlargement, and 4 of them had mediastinal lymph node enlargement without hilar lymph node enlargement. In these cases, 24 had pulmonary abnormalities. 19 of them showed multiple pulmonary nodes, 4 of them had patchy pulmonary shadows, and another 1 had pulmonary fibrosis. Pleural lesions included 2 hydrothorax and 1 multiple pleural nodes, and all of pleural lesions were associated with multiple pulmonary nodes. Conclusion When the radiological findings of thoracic sarcoidosis are atypical, the diagnosis is difficult and must combine with the clinical findings, or the outcome of the treatment.

BACKGROUND:It remains controversial in term of efficacy for the lightweight mesh and heavyweight mesh in inguinal hernia repair.
OBJECTIVE:To compare the clinical therapeutic effects of lightweight mesh and heavyweight mesh in open methods of inguinal hernia repair with Meta-analysis.
METHODS:Comprehensive electronic search strategies were developed using the fol owing electronic databases:PubMed, Cochrane Library, EMBASE, Medline, Ovid, CNKI, VIP, Wanfang and FMJS. The Literature published before February 2013 was searched. The randomized control ed trials about comparing lightweight mesh and heavyweight mesh in open methods of inguinal hernia repair were included. A data-extraction sheet was developed based on the preset standards. The data from eligible studies were pooled using RevMan5.1 software through Meta-analysis.
RESULTS AND CONCLUSION:Eighteen trials with a total of 4 450 hernias met the inclusion criteria. The meta-analysis showed that there was a statistical difference between lightweight mesh group and the heavyweight mesh group on short-term pain [odd ratio (OR)=0.57, 95%confidence interval (CI) (0.43, 0.74), P<0.05] and a reduced risk of developing foreign body sensations [OR=0.49, 95%CI (0.35, 0.69), P<0.05]. No significant differences were found between the two groups in recurrence rate, testicular atrophy, seroma, hematoma, wound infection, urine retention (P>0.5). According to limited evidence, there are some findings as fol ows:the lightweight mesh is of feasibility, safety and effectiveness for inguinal hernia repair. Because of the limits of sample and quality, more large-sample and high-quality trials are required to make a definite clinical evidence to use lightweight mesh for inguinal hernia repair.

Objective:To explore the value of interpretable machine learning model in predicting the prognoses of patients with acute ischemic stroke..Methods:A total of 296 patients with acute ischemic stroke who received intravenous thrombolysis in Zhanjiang Central Hospital, Guangdong Medical University from March 2020 to October 2023 were selected. Prognosis was assessed 3 months after follow-up using modified Rankin scale (scores of 0-2: good prognosis; scores of 3-6: poor prognosis). Clinical data were collected and analyzed retrospectively, and independent influencing factors for prognoses were analyzed by multivariate Logistic regression. These patients were randomly divided into training dataset ( n=178) and test dataset ( n=118) in a 3:2 ratio; independent influencing factors were used as characteristic variables to train these 10 machine learning models, including Logistic regression, random forest, support vector machine, naive Bayesian model, linear discriminant analysis, mixture discriminant analysis, flexible discriminant analysis, gradient boosting machine, extreme gradient boosting, and category boosting. Prediction performance of these 10 machine learning models were evaluated using calibration curve, precise-recall curve, precision-recall gain curve and receiver operating characteristic (ROC) curve. Interpretation and visualization were added via Shapley Additive exPlanation (SHAP) to the machine learning models (including global interpretation and local interpretation). Results:Of the 296 patients, 72 had a poor prognosis. Age ( OR=1.039, 95% CI: 1.008-1.072, P=0.015), National Institute of Health Stroke Scale score ( OR=1.213, 95% CI: 1.000-1.337, P<0.001), Glasgow Coma Scale score ( OR=0.470, 95% CI: 0.289-0.765, P=0.002), Stroke Prognostic Instrument-Ⅱ score ( OR=1.257, 95% CI: 1.043-1.516, P=0.016,), C-reactive protein ( OR=1.709, 95% CI: 1.398-2.087, P<0.001) and platelet count ( OR=0.988, 95% CI: 0.978-0.998, P=0.016) were independent influencing factors for prognoses. Among the 10 machine learning algorithms, calibration curve (C-inder: 0.896), precise-recall curve (area under the curve [AUC]: 0.791), precision-recall gain curve (AUC: 0.363), and ROC curve (AUC: 0.856) in both the training and test sets confirmed that the XGBoost model has the highest performance in predicting prognoses. SHAP visualisation diagram indicated that order of importance was C-reactive protein, National Institutes of Health Stroke Scale, platelet count, Glasgow Coma Scale, Stroke Prediction Tool-II, and age. SHAP scatter plot visualized the contribution direction of these 6 characteristic variables, with bimodal distribution. SHAP dependence plot indicated dependence between values of 6 characteristic variables and SHAP values, with C-reactive protein enjoying the most significant trend. SHAP plot provided local interpretation for individual sample, making the extreme gradient enhancement model more transparent and interpretable. Conclusion:XGBoost model incorporating age, National Institute of Health Stroke Scale, Glasgow Coma Scale, Stroke Prognostic Instrument-Ⅱ, C-reactive protein, and platelet count can differentiate poor prognosis from good prognosis in patients with acute ischemic stroke with high accuracy; on this basis, the model interpretation and visualization combined with SHAP are helpful to understand the contribution and direction of each characteristic variable to the prediction results.