Detection Of Gastric Cancer Lesions Research Articles

GCLDNet: Gastric cancer lesion detection network combining level feature aggregation and attention feature fusion.

BackgroundAnalysis of histopathological slices of gastric cancer is the gold standard for diagnosing gastric cancer, while manual identification is time-consuming and highly relies on the experience of pathologists. Artificial intelligence methods, particularly deep learning, can assist pathologists in finding cancerous tissues and realizing automated detection. However, due to the variety of shapes and sizes of gastric cancer lesions, as well as many interfering factors, GCHIs have a high level of complexity and difficulty in accurately finding the lesion region. Traditional deep learning methods cannot effectively extract discriminative features because of their simple decoding method so they cannot detect lesions accurately, and there is less research dedicated to detecting gastric cancer lesions.MethodsWe propose a gastric cancer lesion detection network (GCLDNet). At first, GCLDNet designs a level feature aggregation structure in decoder, which can effectively fuse deep and shallow features of GCHIs. Second, an attention feature fusion module is introduced to accurately locate the lesion area, which merges attention features of different scales and obtains rich discriminative information focusing on lesion. Finally, focal Tversky loss (FTL) is employed as a loss function to depress false-negative predictions and mine difficult samples.ResultsExperimental results on two GCHI datasets of SEED and BOT show that DSCs of the GCLDNet are 0.8265 and 0.8991, ACCs are 0.8827 and 0.8949, JIs are 0.7092 and 0.8182, and PREs are 0.7820 and 0.8763, respectively.ConclusionsExperimental results demonstrate the effectiveness of GCLDNet in the detection of gastric cancer lesions. Compared with other state-of-the-art (SOTA) detection methods, the GCLDNet obtains a more satisfactory performance. This research can provide good auxiliary support for pathologists in clinical diagnosis.

Support Vector Machine-Based Backprojection Algorithm for Detection of Gastric Cancer Lesions with Abdominal Endoscope Using Magnetic Resonance Imaging Images

This study was to analyze the diagnostic value of magnetic resonance imaging (MRI) for gastric cancer (GC) lesions and the treatment effect of complete laparoscopic radical resection (CLSRR). A malignant tumor recognition algorithm was constructed in this study based on the backprojection (BP) and support vector machine (SVM), which was named BPS. 78 GC patients were divided into an experimental group (received CLSRR) and a control group (received assisted laparoscopic radical resection (ALSRR)), with 39 cases in each group. It was found that the BPS algorithm showed lower relative mean square error (MSE) in axle x (OMSE, x) and axle y (OMSE, x), but the classification accuracy (CA) was the opposite ( P < 0.05 ). The postoperative hospital stay, analgesia duration, first exhaust time (FET), and first off-bed activity time (FOBA) for patients in the experimental group were less ( P < 0.05 ). The operation time of the experimental group (270.56 ± 90.55 min) was significantly longer than that of the control group (228.07 ± 75.26 min) ( P < 0.05 ). There were 3 cases of anastomotic fistula, 1 case of acute peritonitis, and 2 cases of lung infections in the experimental group, which were greatly less than those in the control group (7 cases, 4 cases, and 3 cases) ( P < 0.05 ). In short, the BPS algorithm was superior in processing MRI images and could improve the diagnostic effect of MRI images. The CLSRR could reduce the length of hospital stay and the probability of complications in GC patients, so it could be used as a surgical plan for the clinical treatment of advanced GC.

Clinical applications of 5-aminolevulinic acid-mediated fluorescence for gastric cancer.

5-aminolevulinic acid (ALA) is a naturally occurring amino acid that is a protoporphyrin IX (PpIX) precursor and a next-generation photosensitive substance. After exogenous administration of ALA, PpIX specifically accumulates in cancer cells owing to the impaired metabolism of ALA to PpIX in mitochondria, which results in a red fluorescence following irradiation with blue light and the formation of singlet oxygen. Fluorescence navigation by photodynamic diagnosis (PDD) using ALA provides good visualization and detection of gastric cancer lesions and is a potentially valuable diagnostic tool for gastric cancer for evaluating both the surgical resection margins and extension of the lesion. Furthermore, PDD using ALA might be used to detect peritoneal metastases during preoperative staging laparoscopy, where it could provide useful information for the selection of a therapeutic approach. Another promising application for this modality is in the evaluation of lymph node metastases. Photodynamic therapy (PDT) using ALA to cause selective damage based on the accumulation of a photosensitizer in malignant tissue is expected to be a non-invasive endoscopic treatment for superficial early gastric cancer. ALA has the potential to be used not only as a diagnostic agent but also as a therapeutic drug, resulting in a new strategy for cancer diagnosis and therapy. Here, we review the current use of PDD and PDT in gastric cancer and evaluate its future potential beyond conventional modalities combined with a light energy upconverter, a light-emitting diode and near-infrared rays as light sources.

Photodynamic diagnosis using 5‐aminolevulinic acid during gastrectomy for gastric cancer

Five-aminolevulinic acid (ALA) has received much attention recently as a new-generation photosensitive substance for photodynamic diagnosis (PDD). This study aimed to investigate the feasibility of intraoperative PDD using ALA to identify gastric cancer. Intraoperative PDD was carried out in 26 lesions of 21 patients with gastric cancer. Before surgery, 1.0 g of ALA solution was given orally through a stomach tube. After tumor resection, resected specimens were investigated by an endoscopic PDD system, and red fluorescence-positive lesions were compared with the pathological result. Red fluorescence was detected in 15 lesions of 11 patients. The sensitivity, specificity, and accuracy of PDD using ALA in detecting gastric cancer were 57.7, 100, and 66.7%, respectively. The incidence of laparoscopic surgery was significantly lower in the PDD-positive group than in PDD-negative cases (18.2% vs. 60.0%; P = 0.049), while that of intestinal type tumor was significantly higher in PDD-positive compared to PDD-negative patients (93.3% vs. 27.3%; P < 0.001). Fluorescence navigation by PDD provides good visualization and detection of gastric cancer lesions, and might be particularly useful for intestinal type gastric cancer. Thus, PDD using ALA seems to be a promising diagnostic tool for gastric cancer.