[Clinical diagnosis and treatment of gastrointestinal stromal tumor: matching technological breakthrough with patient care]

Quantitative Detection of Gastrointestinal Tumor Markers Using a Machine Learning Algorithm and Multicolor Quantum Dot Biosensor

This work was to explore the application value of gastrointestinal tumor markers based on gene feature selection model of principal component analysis (PCA) algorithm and multicolor quantum dots (QDs) immunobiosensor in the detection of gastrointestinal tumors. Based on the PCA method, the neighborhood rough set algorithm was introduced to improve it, and the tumor gene feature selection model (OPCA) was established to analyze its classification accuracy and accuracy. Four kinds of coupled biosensors were fabricated based on QDs, namely, 525 nm Cd Se/Zn S QDs-carbohydrate antigen 125 (QDs525-CA125 McAb), 605 nm Cd Se/Zn S QDs-cancer antigen 19-9 (QDs605-CA19-9 McAb), 645 nm Cd Se/Zn S QDs-anticancer embryonic antigen (QDs 645-CEA McAb), and 565 nm Cd Se/Zn S QDs-anti-alpha-fetoprotein (QDs565-AFP McAb). The quantum dot-antibody conjugates were identified and quantified by fluorescence spectroscopy and ultraviolet absorption spectroscopy. The results showed that the classification precision of OPCA model in colon tumor and gastric cancer datasets was 99.52% and 99.03%, respectively, and the classification accuracy was 94.86% and 94.2%, respectively, which were significantly higher than those of other algorithms. The fluorescence values of AFP McAb, CEA McAb, CA19-9 McAb, and CA125 McAb reached the maximum when the conjugation concentrations were 25 µg/mL, 20 µg/mL, 30 µg/mL, and 30 µg/m, respectively. The highest recovery rate of AFP was 98.51%, and its fluorescence intensity was 35.78 ± 2.99, which was significantly higher than that of other antigens (P < 0.001). In summary, the OPCA model based on PCA algorithm can obtain fewer feature gene sets and improve the accuracy of sample classification. Intelligent immunobiosensors based on machine learning algorithms and QDs have potential application value in gastrointestinal gene feature selection and tumor marker detection, which provides a new idea for clinical diagnosis of gastrointestinal tumors.

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

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.