scholarly journals CT Image Examination Based on Virtual Reality Analysis in Clinical Diagnosis of Gastrointestinal Stromal Tumors

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zhiying Wang ◽  
Qiaoyan Qu ◽  
Ke Cai ◽  
Ting Xu
Keyword(s):  
Virtual Reality ◽  
Lymph Nodes ◽  
Clinical Diagnosis ◽  
Gastrointestinal Stromal Tumors ◽  
Ct Images ◽  
Ct Imaging ◽  
Ct Image ◽  
Average Difference ◽  
Stromal Tumors ◽  
Imaging Examination

With the advancement and development of medical equipment, CT images have become a common lung examination tool. This article mainly studies the application of CT imaging examination based on virtual reality analysis in the clinical diagnosis of gastrointestinal stromal tumors. Before extracting suspected lymph nodes from a CT image of the stomach, the CT image sequence is preprocessed first, which can reduce the cumbersomeness of subsequent extraction of suspected lymph nodes and speed up the subsequent processing. According to medical knowledge, CT images of the stomach show that lymph nodes mainly exist in the adipose tissue around the gastric wall, but there are no lymph nodes in the subcutaneous fat outside the chest. The most basic gray value in the image and the neighborhood average difference feature related to gray level are used as the primary features of visual attention detection. When extracting the neighborhood average difference feature, we use a 3 ∗ 3 sliding window method to traverse each point of the pixel matrix in the image, thereby calculating the feature value of each pixel in the image. After the feature extraction is completed, it is necessary to calibrate the data and make a training data set. The SP immunohistochemical staining method was used. The specimens were fixed with 10% formaldehyde, routinely embedded in paraffin, sectioned, and stained with HE. The tumor tissue was determined by immunohistochemistry, and the reagents were products of Maixin Company. All patients were followed up by regular outpatient review, letters, and visits or phone calls. The data showed that immunohistochemical tumor cells showed positive staining for CD117 (14/15, 93.3%) and CD34 (10/15, 66.7%). The results show that the application of virtual reality technology to CT imaging examination can significantly improve the diagnostic accuracy of gastrointestinal stromal tumors.

scholarly journals Discrimination of mediastinal metastatic lymph nodes in NSCLC based on radiomic features in different phases of CT imaging

2019 ◽  
Author(s):  
Xue Sha ◽  
Guan Zhong Gong ◽  
Qing Tao Qiu ◽  
Jing Hao Duan ◽  
Deng Wang Li ◽  
...  
Keyword(s):  
Lymph Nodes ◽  
Predictive Value ◽  
Characteristic Curve ◽  
Multivariate Logistic Regression Analysis ◽  
Ct Images ◽  
Ct Imaging ◽  
Arterial Phase ◽  
Metastatic Lymph Nodes ◽  
Metastatic Lymph ◽  
Venous Phase

Abstract Background: We aimed to develop radiomic models based on different phases of computed tomography (CT) imaging and to investigate the efficacy of models for diagnosing mediastinal metastatic lymph nodes (LNs) in non-small cell lung cancer (NSCLC). Methods: We selected 231 mediastinal LNs confirmed by pathology results as the subjects, which were divided into training (n=163) and validation cohorts (n=68). The regions of interest (ROIs) were delineated on CT scans in the plain phase, arterial phase and venous phase, respectively. Radiomic features were extracted from the CT images in each phase. A least absolute shrinkage and selection operator (LASSO) algorithm was used to select features, and multivariate logistic regression analysis was used to build models. We constructed six models (orders 1-6) based on the radiomic features of the single- and dual-phase CT images. The performance of the radiomic model was evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV). Results: A total of 846 features were extracted from each ROI, and 10, 9, 5, 2, 2, and 9 features were chosen to develop models 1-6, respectively. All of the models showed excellent discrimination, with AUCs greater than 0.8. The plain CT radiomic model, model 1, yielded the highest AUC, specificity, accuracy and PPV, which were 0.926 and 0.925; 0.860 and 0.769; 0.871 and 0.882; and 0.906 and 0.870 in the training and validation sets, respectively. When the plain and venous phase CT radiomic features were combined with the arterial phase CT images, the sensitivity increased from 0.879 and 0.919 to 0.949 and 0979 and the NPV increased from 0.821 and 0.789 to 0.878 and 0.900 in the training group, respectively. Conclusions: All of the CT radiomic models based on different phases all showed high accuracy and precision for the diagnosis of LN metastasis (LNM) in NSCLC patients. When combined with arterial phase CT, the sensitivity and NPV of the model was be further improved.

A systematic review on the clinical diagnosis of gastrointestinal stromal tumors

2008 ◽  
Vol 98 (5) ◽  
pp. 384-392 ◽  
Author(s):  
Marco Scarpa ◽  
Matteo Bertin ◽  
Cesare Ruffolo ◽  
Lino Polese ◽  
Davide F. D'Amico ◽  
...  
Keyword(s):  
Systematic Review ◽  
Clinical Diagnosis ◽  
Gastrointestinal Stromal Tumors ◽  
Stromal Tumors

scholarly journals Differentiation of mediastinal metastatic lymph nodes in NSCLC based on radiomic features of different phase CT imaging

2019 ◽  
Author(s):  
Xue Sha ◽  
Guan Zhong Gong ◽  
Qing Tao Qiu ◽  
Jing Hao Duan ◽  
Deng Wang Li ◽  
...  
Keyword(s):  
Lymph Nodes ◽  
Predictive Value ◽  
Characteristic Curve ◽  
Multivariate Logistic Regression Analysis ◽  
Ct Images ◽  
Ct Imaging ◽  
Arterial Phase ◽  
Metastatic Lymph Nodes ◽  
Metastatic Lymph ◽  
Venous Phase

Abstract Background To develop radiomic models based on different phases of computed tomography (CT) imaging and investigate the efficacy of models to diagnose mediastinal metastatic lymph nodes in non-small cell lung cancer (NSCLC).Methods We selected 231 mediastinal lymph nodes confirmed by pathology results as the subjects, which were divided into training (n=163) and validation cohorts (n=68). The regions of interest (ROIs) were delineated on CT scans of the plain phase, arterial phase and venous phase, respectively. Radiomic features were extracted from the CT images of each phase. Least absolute shrinkage and selection operator (LASSO) was used to select features, and multivariate logistic regression analysis was used to build models. We constructed six models (orders of 1-6) based on radiomic features of the single- and dual-phase CT images. The performance of the radiomic model was evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV).Results A total of 846 features were extracted from each ROI, and 10, 9, 5, 2, 2, and 9 features were chosen to develop models 1-6. All of the models showed superior differentiation, with AUCs greater than 0.8. The plain CT radiomic model, model 1, yielded the highest AUC, specificity, accuracy and PPV, which were 0.926 VS 0.925, 0.860 VS 0.769, 0.871 VS 0.882 and 0.906 VS 0.870 in the training and validation sets, respectively. When the plain and venous phase CT radiomic features were combined with the arterial phase CT images, the sensitivity increased from 0.879, 0.919 to 0.949, 0979 and the NPV increased from 0.821, 0.789 to 0.878, 0.900 in the training group, respectively.Conclusion CT radiomic models based on different phases all showed high accuracy and precision in the diagnosis of LNM in NSCLC patients. When combined with arterial phase CT, the sensitivity and NPV of the model can be further improved.

scholarly journals Multislice CT imaging of gastrointestinal stromal tumors (GISTs)

2011 ◽  
Vol 42 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Said Abdel-Monem ◽  
Moanes M. Enaba ◽  
Tamir A. Hassan ◽  
Mohamed A. Attya
Keyword(s):  
Gastrointestinal Stromal Tumors ◽  
Multislice Ct ◽  
Ct Imaging ◽  
Stromal Tumors

scholarly journals Optimized Backprojection Filtration Algorithm for Postoperative Reduction and Analysis of Respiratory Infection-Related Factors of Pelvic Fractures by CT Imaging

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Aihua Pu ◽  
Hua Wang ◽  
Jichong Ying
Keyword(s):  
Pelvic Fracture ◽  
Ct Images ◽  
Ct Imaging ◽  
Pelvic Fractures ◽  
Conventional Group ◽  
Ct Image ◽  
Diagnosis Group ◽  
Imaging Characteristics ◽  
Conventional Ct ◽  
Projection Filtering

To explore the computed tomography (CT) imaging characteristics and BPF algorithm fine lung CT image efficiency for the diagnosis of pelvic fracture patients and assist clinicians to carry out the disease care and treatment, CT images based on optimized back-projection filtering (BPF) algorithm were utilized to diagnose postoperative reduction of pelvic fractures and penetrating lung infection caused by long-term bed rest. A total of 100 patients with pelvic fracture were selected and all of them underwent pelvic fracture surgery and were rolled into conventional CT diagnosis group (conventional group) and BPF algorithm optimized CT image diagnosis group (BPF group). One group used conventional CT images to guide pelvic reduction and detect lung infections, and the other used BPF algorithm to optimize the images. The results showed that the BPF group was superior to the conventional CT group in both image clarity and shadow area, and the peak signal-to-noise ratio (PSNR) was significantly better than that of the conventional group ( P < 0.05 ). Nine more cases were detected in the algorithm group than in the conventional group, and the incidence of complications was 48% in the conventional group and 28% in the BPF group, with a statistical difference of 20% between the two groups ( P < 0.05 ). In addition, the satisfaction of returning patients was 96% in the BPF group and 77% in the conventional group ( P < 0.05 ). The diagnosis of pulmonary infection was more obvious in the BPF group, indicating that BPF optimization of the CT image was suitable for clinical diagnosis and had a practical application value.

scholarly journals Preoperative CT-Based Deep Learning Model for Predicting Risk Stratification in Patients With Gastrointestinal Stromal Tumors

2021 ◽  
Vol 11 ◽  
Author(s):  
Bing Kang ◽  
Xianshun Yuan ◽  
Hexiang Wang ◽  
Songnan Qin ◽  
Xuelin Song ◽  
...  
Keyword(s):  
Deep Learning ◽  
Risk Stratification ◽  
Gastrointestinal Stromal Tumors ◽  
External Validation ◽  
Learning Model ◽  
Ct Images ◽  
Stromal Tumors ◽  
Contrast Enhanced Ct ◽  
Malignant Gists ◽  
Deep Learning Model

ObjectiveTo develop and evaluate a deep learning model (DLM) for predicting the risk stratification of gastrointestinal stromal tumors (GISTs).MethodsPreoperative contrast-enhanced CT images of 733 patients with GISTs were retrospectively obtained from two centers between January 2011 and June 2020. The datasets were split into training (n = 241), testing (n = 104), and external validation cohorts (n = 388). A DLM for predicting the risk stratification of GISTs was developed using a convolutional neural network and evaluated in the testing and external validation cohorts. The performance of the DLM was compared with that of radiomics model by using the area under the receiver operating characteristic curves (AUROCs) and the Obuchowski index. The attention area of the DLM was visualized as a heatmap by gradient-weighted class activation mapping.ResultsIn the testing cohort, the DLM had AUROCs of 0.90 (95% confidence interval [CI]: 0.84, 0.96), 0.80 (95% CI: 0.72, 0.88), and 0.89 (95% CI: 0.83, 0.95) for low-malignant, intermediate-malignant, and high-malignant GISTs, respectively. In the external validation cohort, the AUROCs of the DLM were 0.87 (95% CI: 0.83, 0.91), 0.64 (95% CI: 0.60, 0.68), and 0.85 (95% CI: 0.81, 0.89) for low-malignant, intermediate-malignant, and high-malignant GISTs, respectively. The DLM (Obuchowski index: training, 0.84; external validation, 0.79) outperformed the radiomics model (Obuchowski index: training, 0.77; external validation, 0.77) for predicting risk stratification of GISTs. The relevant subregions were successfully highlighted with attention heatmap on the CT images for further clinical review.ConclusionThe DLM showed good performance for predicting the risk stratification of GISTs using CT images and achieved better performance than that of radiomics model.

scholarly journals Value of contrast-enhanced CT based radiomic machine learning algorithm in differentiating gastrointestinal stromal tumors with KIT exon 11 mutation: a two-center study

2020 ◽  
Author(s):  
Bo Liu ◽  
Hexiang Wang ◽  
Shunli Liu ◽  
Shifeng Yang ◽  
Yancheng Song ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gastrointestinal Stromal Tumors ◽  
Ct Images ◽  
Arterial Phase ◽  
Training Set ◽  
Clinical Model ◽  
Stromal Tumors ◽  
Contrast Enhanced ◽  
Validation Set ◽  
Exon 11

Abstract Background Knowing the genetic phenotype of gastrointestinal stromal tumors (GISTs) is essential for patients who receive therapy with tyrosine kinase inhibitors.Methods We enrolled 106 patients (80 in the training set, 26 in the validation set) with clinicopathologically confirmed GISTs from two centers. Preoperative and postoperative clinical characteristics were selected and analyzed to construct the clinical model. Arterial phase (A-phase), venous phase (V-phase), delayed phase (D-phase), and combined radiomics algorithms were generated from the training set based on contrast-enhanced computed tomography (CE-CT) images. Various radiomics feature selection methods were used, namely least absolute shrinkage and selection operator (LASSO); minimum redundancy maximum relevance (mRMR); and generalized linear model (GLM) as a machine-learning classifier. Independent predictive factors were determined to construct preoperative and postoperative radiomics nomograms by multivariate logistic regression analysis. The performances of the clinical model, radiomics algorithm, and radiomics nomogram in distinguishing GISTs with the KIT exon 11 mutation were evaluated by area under the curve (AUC) of the receiver operating characteristic (ROC).Results The combined radiomics algorithm was found to be the best prediction model for differentiating the expression status of the KIT exon 11 mutation (AUC = 0.836; 95% confidence interval (CI), 0.640–0.951) in the validation set. The clinical model, and preoperative and postoperative radiomics nomograms had AUCs of 0.606 (95% CI, 0.397–0.790), 0.715 (95% CI, 0.506–0.873), and 0.679 (95% CI, 0.468–0.847), respectively, with the validation set.Conclusion The radiomics algorithm could distinguish GISTs with the KIT exon 11 mutation based on CE-CT images and could potentially be used for selective genetic analysis to support the precision medicine of GISTs.

scholarly journals Gastrointestinal stromal tumors: associations between contrast-enhanced CT images and KIT exon 11 gene mutation

2021 ◽  
Vol 0 (0) ◽  
pp. 0-0
Author(s):  
Xijiao Liu ◽  
Yuan Yin ◽  
Xiaozhou Wang ◽  
Caiwei Yang ◽  
Shang Wan ◽  
...  
Keyword(s):  
Gene Mutation ◽  
Gastrointestinal Stromal Tumors ◽  
Ct Images ◽  
Stromal Tumors ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
Exon 11

scholarly journals Association between CT imaging features and KIT mutations in small intestinal gastrointestinal stromal tumors

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Yi-qiong Yin ◽  
Chun-juan Liu ◽  
Bo Zhang ◽  
Yue Wen ◽  
Yuan Yin
Keyword(s):  
Gastrointestinal Stromal Tumors ◽  
Ct Imaging ◽  
Imaging Features ◽  
Stromal Tumors ◽  
Small Intestinal
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