Multi-center verification of the influence of data ratio of training sets on test results of an AI system for detecting early gastric cancer based on the YOLO-v4 algorithm.

Objective: Convolutional Neural Network(CNN) is increasingly being applied in the diagnosis of gastric cancer. However, the impact of proportion of internal data in the training set on test results has not been sufficiently studied. Here, we constructed an artificial intelligence (AI) system called EGC-YOLOV4 using the YOLO-v4 algorithm to explore the optimal ratio of training set with the power to diagnose early gastric cancer. Design: A total of 22,0918 gastroscopic images from Yixing People's Hospital were collected. 7 training set models were established to identify 4 test sets. Respective sensitivity, specificity, Youden index, accuracy, and corresponding thresholds were tested, and ROC curves were plotted. Results: 1. The EGC-YOLOV4 system completes all tests at an average reading speed of about 15 ms/sheet; 2. The AUC values in training set 1 model were 0.8325, 0.8307, 0.8706, and 0.8279, in training set 2 model were 0.8674, 0.8635, 0.9056, and 0.9249, in training set 3 model were 0.8544, 0.8881, 0.9072, and 0.9237, in training set 4 model were 0.8271, 0.9020, 0.9102, and 0.9316, in training set 5 model were 0.8249, 0.8484, 0.8796, and 0.8931, in training set 6 model were 0.8235, 0.8539, 0.9002, and 0.9051, in training set 7 model were 0.7581, 0.8082, 0.8803, and 0.8763. Conclusion: EGC-YOLOV4 can quickly and accurately identify the early gastric cancer lesions in gastroscopic images, and has good generalization.The proportion of positive and negative samples in the training set will affect the overall diagnostic performance of AI.In this study, the optimal ratio of positive samples to negative samples in the training set is 1:1~ 1:2.

Role of SB203580 in the regulation of human esophageal cancer cells under the effection of Diosgenin.

In order to investigate the mechanism of human esophageal Eca109 cells induced by Diosgenin (Dio), the p38 specific inhibitor SB203580 was used to inhibit the expression of p38 and Western blot was employed to detect the effect of SB203580 in Eca109 cells. MTT experiments were executed to detect the proliferation of the cells. Western blot was also applied to find the expression of phosphorylated p38 (p-p38). It is found that SB203580 can inhibit the expression of p38 in human esophageal cell Eca109. After treated with 50 µg/mL of Dio and 10 µg/mL of SB203580, the proliferation of cells showed significantly increase and the apoptosis of cells showed significantly decrease compared with the proliferation in the cells treated with Dio only. Moreover, p-p38 protein level was significantly decreased after treated by the two drugs. It is concluded that Dio may regulate esophageal Eca109 cells through p-p38 pathway.