Search

COVID-19 pneumonia is a disease that causes an existential health crisis in many people by directly affecting and damaging lung cells. The segmentation of infected areas from computed tomography (CT) images can be used to assist and provide useful information for COVID-19 diagnosis. Although several deep learning-based segmentation methods have been proposed for COVID-19 segmentation and have achieved state-of-the-art results, the segmentation accuracy is still not high enough (approximately 85%) due to the variations of COVID-19 infected areas (such as shape and size variations) and the similarities between COVID-19 and non-COVID-infected areas. To improve the segmentation accuracy of COVID-19 infected areas, we propose an interactive attention refinement network (Attention RefNet). The interactive attention refinement network can be connected with any segmentation network and trained with the segmentation network in an end-to-end fashion. We propose a skip connection attention module to improve the important features in both segmentation and refinement networks and a seed point module to enhance the important seeds (positions) for interactive refinement. The effectiveness of the proposed method was demonstrated on public datasets (COVID-19CTSeg and MICCAI) and our private multicenter dataset. The segmentation accuracy was improved to more than 90%. We also confirmed the generalizability of the proposed network on our multicenter dataset. The proposed method can still achieve high segmentation accuracy. [ABSTRACT FROM AUTHOR]

Objectives: To investigate the performance of radiomic-based quantitative analysis on CT images in predicting invasiveness of lung adenocarcinoma manifesting as pure ground-glass nodules (pGGNs). Methods: A total of 275 lung adenocarcinoma cases, with 322 pGGNs resected surgically and confirmed pathologically, from January 2015 to October 2017 were enrolled in this retrospective study. All nodules were split into training and test cohorts randomly with a ratio of 4:1 to establish models to predict between pGGN-like adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IVA). Radiomic feature extraction was performed using Pyradiomics with semi-automatically segmented tumor regions on CT scans that were contoured with an in-house plugin for 3D-Slicer. Random forest (RF) and support vector machine (SVM) were used for feature selection and predictive model building in the training cohort. Three different predictive models containing conventional, radiomic, and combined models were built on the basis of the selected clinical, radiological, and radiomic features. The predictive performance of each model was evaluated through the receiver operating characteristic curve (ROC) and the area under the curve (AUC). The predictive performance of two radiologists (A and B) and our radiomic predictive model were further investigated in the test cohort to see if radiomic predictive model could improve radiologists' performance in prediction between pGGN-like AIS/MIA and IVA. Results: Among 322 nodules, 48 (14.9%) were AIS and 102 (31.7%) were MIA with 172 (53.4%) for IVA. Age, diameter, density, and nine meaningful radiomic features were selected for model building in the training cohort. Three predictive models showed good performance in prediction between pGGN-like AIS/MIA and IVA (AUC > 0.8, P < 0.05) in both training and test cohorts. The AUC values in the test cohort were 0.824 (95% CI, 0.723–0.924), 0.833 (95% CI, 0.733–0.934), and 0.848 (95% CI, 0.750–0.946) for conventional, radiomic, and combined models, respectively. The predictive accuracy was 73.44 and 59.38% for radiologist A and radiologist B in the test cohort and was improved dramatically to 79.69 and 75.00% with the aid of our radiomic predictive model. Conclusion: The predictive models built in our study showed good predictive power with good accuracy and sensitivity, which provided a non-invasive, convenient, economic, and repeatable way for the prediction between IVA and AIS/MIA representing as pGGNs. The radiomic predictive model outperformed two radiologists in predicting pGGN-like AIS/MIA and IVA, and could significantly improve the predictive performance of the two radiologists, especially radiologist B with less experience in medical imaging diagnosis. The selected radiomic features in our research did not provide more useful information to improve the combined predictive model's performance. [ABSTRACT FROM AUTHOR]

Purpose: To discuss significant computed tomography (CT) findings that differentiate gastric leiomyomas (GLs) from small gastric stromal tumors (GSTs). Methods: One hundred sixty cases with pathologically proven GLs (n = 50) and GSTs (n = 110) with comprehensive CT images were enrolled in this retrospective study. Computed tomography findings (ie, size, location, contour, growth pattern, enhancement degree, necrosis, ulceration, calcification, and lymph nodes) were analyzed through the χ2 or Fisher exact test, independent T test, and multivariate (logistic regression) analysis. Sensitivity and specificity were also calculated. Results: Features of cardia location, endophytic growth, homogeneous gradual enhancement, absent of necrosis, long diameter less than 24 mm, short diameter less than 20 mm, unenhanced CT value larger than 35.2 Hounsfield units (HU), portal venous phase CT value larger than 67.4 HU, and enhancement degree of arterial and venous phase less than 16.2 HU and 32.4 HU were found to be statistically significant between GLs and small GSTs (P <.05). On multivariate analysis, cardia location, endophytic growth, and homogeneous gradual enhancement were independent predictive factors for GLs and small GSTs. Conclusion: These 10 CT criteria are very helpful to differentiate GLs from small GSTs. Especially cardia location, endophytic growth, and homogeneous gradual enhancement are of high value in differential diagnosis. [ABSTRACT FROM AUTHOR]