Nutritional status of patients undergoing upper gastrointestinal cancer surgery: A cross-sectional study at a single centre

Malnutrition is closely related to the outcome of disease treatment, especially in digestive cancer surgery. The aim of this study was to assess the nutritional condition of pre-operative patients with upper digestive cancers (including stomach and oesophagus) at the Department of General Surgery, Bach Mai Hospital in 2016. We conducted a cross-sectional descriptive analysis of 76 malignancies of the upper gastrointestinal tract with surgical treatments. The results revealed that the weight loss rate of hospitalized patients with gastric cancer and esophageal cancer was 76.6% and 66.7%, respectively. The rate of weight loss above 10% of body weight was 19.7%. The prevalence of chronic energy deficit was 29.9%. The risk of malnutrition according to SGA was 77.6%, of which mild to moderate and severe was 67.2% and 10.4%, respectively. The rate of low blood albumin level (less than 35 g/L) was 36.5%. The average net nutritional value was 1146.3 ± 592.7 Kcal (range 246.7 – 3653.5), which equals to 55.7% of the necessary daily intake. Protein, lipid, and glucid contents reached 73.4%, 57.8%, and 52.1% of the recommended levels, respectively. Conclusion: malnutrition was still prevalent among patients undergoing upper gastrointestinal cancer surgery, and pre-operative nutritional status does not achieve recommended levels.

Utilization of Ultrasonic Image Characteristics Combined with Endoscopic Detection on the Basis of Artificial Intelligence Algorithm in Diagnosis of Early Upper Gastrointestinal Cancer

The aim of this study was to evaluate the diagnostic value of artificial intelligence algorithm combined with ultrasound endoscopy in early esophageal cancer and precancerous lesions by comparing the examination of conventional endoscopy and artificial intelligence algorithm combined with ultrasound endoscopy, and by comparing the real-time diagnosis of endoscopy and the ultrasonic image characteristics of artificial intelligence algorithm combined with endoscopic detection and pathological results. 120 cases were selected. According to the inclusion and exclusion criteria, 80 patients who met the criteria were selected and randomly divided into two groups: endoscopic examination combined with ultrasound imaging based on intelligent algorithm processing (cascade region-convolutional neural network (Cascade RCNN) model algorithm group) and simple use of endoscopy group (control group). This study shows that the ultrasonic image of artificial intelligence algorithm is effective, and the detection performance is better than that of endoscopic detection. The results are close to the gold standard of doctor recognition, and the detection time is greatly shortened, and the recognition time is shortened by 71 frames per second. Compared with the traditional convolutional neural network (CNN) algorithm, the accuracy and recall of image analysis and segmentation using feature pyramid network are increased. The detection rates of CNN model, Cascade RCNN model, and endoscopic detection alone in early esophageal cancer and precancerous lesions are 56.3% (45/80), 88.8% (71/80), and 44.1% (35/80), respectively. The detection rate of Cascade RCNN model and CNN model was higher than that of endoscopy alone, and the difference was statistically significant ( P < 0.05 ). The sensitivity, specificity, positive predictive value, and negative predictive value of Cascade RCNN model were higher than those of CNN model, which was close to the gold standard for physician identification. This provided a reference basis for endoscopic ultrasound identification of early upper gastrointestinal cancer or other gastrointestinal cancers.