Radiomics analysis for prediction and classification of submucosal tumors based on gastrointestinal endoscopic ultrasonography.

Objectives: To identify and classify submucosal tumors by building and validating a radiomics model with gastrointestinal endoscopic ultrasonography (EUS) images. Methods: A total of 144 patients diagnosed with submucosal tumors through gastrointestinal EUS were collected between January 2019 and October 2020. There are 1952 radiomic features extracted from each patient's EUS images. The statistical test and the customized least absolute shrinkage and selection operator regression were used for feature selection. Subsequently, an extremely randomized trees algorithm was utilized to construct a robust radiomics classification model specifically tailored for gastrointestinal EUS images. The performance of the model was measured by evaluating the area under the receiver operating characteristic curve. Results: The radiomics model comprised 30 selected features that showed good discrimination performance in the validation cohorts. During validation, the area under the receiver operating characteristic curve was calculated as 0.9203 and the mean value after 10-fold cross-validation was 0.9260, indicating excellent stability and calibration. These results confirm the clinical utility of the model. Conclusions: Utilizing the dataset provided curated from gastrointestinal EUS examinations at our collaborating hospital, we have developed a well-performing radiomics model. It can be used for personalized and non-invasive prediction of the type of submucosal tumors, providing physicians with aid for early treatment and management of tumor progression.

Apolipoprotein E E3/E4 genotype is associated with an increased risk of type 2 diabetes mellitus complicated with coronary artery disease.

OBJECTIVE: Dyslipidemia is a co-existing problem in patients with diabetes mellitus (DM) and coronary artery disease (CAD), and apolipoprotein E (APOE) plays an important role in lipid metabolism. However, the relationship between the APOE gene polymorphisms and the risk of developing CAD in type 2 DM (T2DM) patients remains controversial. The aim of this study was to assess this relationship and provide a reference for further risk assessment of CAD in T2DM patients. METHODS: The study included 378 patients with T2DM complicated with CAD (T2DM + CAD) and 431 patients with T2DM alone in the case group, and 351 individuals without DM and CAD were set as controls. The APOE rs429358 and rs7412 polymorphisms were genotyped by polymerase chain reaction (PCR) - microarray. Differences in APOE genotypes and alleles between patients and controls were compared. Multiple logistic regression analysis was performed after adjusting for age, gender, body mass index (BMI), history of smoking, and history of drinking to access the relationship between APOE genotypes and T2DM + CAD risk. RESULTS: The frequencies of the APOE ɛ3/ɛ4 genotype and ε4 allele were higher in the T2DM + CAD patients, and the frequencies of the APOE ɛ3/ɛ3 genotype and ε3 allele were lower than those in the controls (all p < 0.05). The T2DM + CAD patients with ɛ4 allele had higher level in low-density lipoprotein cholesterol (LDL-C) than those in patients with ɛ2 and ɛ3 allele (p < 0.05). The results of logistic regression analysis showed that age ≥ 60 years old, and BMI ≥ 24.0 kg/m2 were independent risk factors for T2DM and T2DM + CAD, and APOE ɛ3/ɛ4 genotype (adjusted odds ratio (OR) = 1.93, 95% confidence interval (CI) = 1.18-3.14, p = 0.008) and ɛ4 allele (adjusted OR = 1.97, 95% CI = 1.23-3.17) were independent risk factors for T2DM + CAD. However, the APOE genotypes and alleles were not found to have relationship with the risk of T2DM. CONCLUSIONS: APOE ε3/ε4 genotype and ε4 allele were independent risk factors for T2DM complicated with CAD, but not for T2DM.

Multifeature Fusion Classification Method for Adaptive Endoscopic Ultrasonography Tumor Image.

Endoscopic ultrasonography (EUS) has been found to be of great advantage in the diagnosis of digestive tract submucosal tumors. However, EUS-based diagnosis is limited by variability in subjective interpretation on the part of doctors. Tumor classification of ultrasound images with the computer-aided diagnosis system can significantly improve the diagnostic efficiency and accuracy of doctors. In this study, we proposed a multifeature fusion classification method for adaptive EUS tumor images. First, for different ultrasound tumor images, we selected the region of interest based on prior information to facilitate the estimation in the subsequent works. Second, we proposed a method based on image gray histogram feature extraction with principal component analysis dimensionality reduction, which learns the gray distribution of different tumor images effectively. Third, we fused the reduced grayscale features with the improved local binary pattern features and gray-level co-occurrence matrix features, and then used the multiclassification support vector machine. Finally, in the experiment, we selected the 431 ultrasound images of 109 patients in the hospital and compared the experimental effects of different features and different classifiers. The results revealed that the proposed method performed best, with the highest accuracy of 96.18% and an area under the curve of 99%. It is evident that the method proposed in this study can efficiently contribute to the classification of EUS tumor images.