ABSTRACT
Objective:To propose a model using the maximum intensity projection (MIP) of lung field computed tomography (CT) images and deep convolution neural network (CNN) and explore its value in identifying chronic obstructive pulmonary disease (COPD).Methods:A total of 201 subjects were selected from the Second Hospital of Dalian Medical University from January 2010 to May 2021. All subjects were included according to the inclusion criteria and were divided into COPD group (101 cases) and healthy controls group (100 cases). Each patient underwent a high-resolution CT scan of the chest and pulmonary function test. First, the lung field was extracted from CT images and the intrapulmonary MIP images were acquired. Second, with these MIP images as input, the model for identifying COPD was constructed based on a modified residual network (ResNet). Finally, the influence of the number of residual blocks on the performance of the models was investigated. Accuracy, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to evaluate the identification efficiency.Results:The accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) of ResNet26 was 76.1%, 76.2%, 76.0%, 76.2%, and 76.0%, respectively; and the AUC of the test was 0.855 (95% CI: 0.799-0.901). The accuracy, sensitivity, specificity, PPV, NPV of ResNet50 was 77.6%, 76.2%, 79.0%, 78.6%, and 76.7%, respectively; and the AUC of the test was 0.854 (95% CI: 0.797-0.900). The accuracy, sensitivity, specificity, PPV, NPV of ResNet26d was 82.1%, 83.2%, 81.0%, 81.6%, and 82.7%, respectively; and the AUC of the test was 0.885 (95% CI: 0.830-0.926). Conclusions:The COPD identification model via MIP images from CT images within the lung and deep CNN is successfully constructed and achieves accurate COPD identification. And it can provide an effective tool for COPD screening.
ABSTRACT
Objective:To investigate the value of net water uptake (NWU) in predicting malignant edema (ME) in large hemispheric infarction (LHI).Methods:Fifty-six patients suffering from LHI in the General Hospital of Northern Theater Command from September 2017 to July 2018 were retrospectively analyzed, and their NWU was calculated separately. Patients were divided into two groups according to the occurrence of ME, which was defined as space-occupying infarct requiring decompressive craniectomy or death resulting from cerebral hernia in seven days from onset. The clinical characteristics were analyzed, and receiver operating characteristic (ROC) curve and respective area under curve (AUC) were used to assess the value of NWU and other factors.Results:After adjusting for atrial fibrillation, National Institutes of Health Stroke Scale scores at admission, and time from onset to imaging, multivariable analysis showed that NWU was an independent predictor of ME ( OR=1.226，95% CI 1.040-1.446, P=0.015). According to the ROC curve, NWU≥13.08% identified ME with great predictive power (AUC=0.813;sensitivity 0.64, specificity 0.94). Conclusions:NWU is an important predictor of ME in patients with LHI. It can help identify patients at risk of ME.