Abstract
The study focused on the performance of Convolutional Neural Network- (CNN-) based lymph node recognition model as well as the effects of different rehabilitation nursing methods on patients with esophageal cancer. Specifically, the activation function and loss function were optimized by CNN, to establish a U-Net lymph node recognition model. It was compared with Mean Shift and Fuzzy C-means (FCM) algorithm for the loss value, the mean pixel accuracy (mPA), and intersection over union (IOU). 158 patients with esophageal cancer undergoing radical resection were selected as research subjects. With pathological diagnosis results as the gold standard, the role of CT imaging was evaluated in the diagnosis of esophageal cancer lymph nodes. All subjects were divided into control group (routine nursing) and intervention group (routine nursing + rehabilitation nursing) according to different nursing methods, with 79 cases in each. The two groups were compared in terms of the time in bed, hospital stay, indwelling chest tube time, and VAS scores. It was found that the loss value of the U-Net model was close to 0 when it was stable, and its IOU value and mPA value were significantly higher than those of the Mean Shift and FCM algorithms. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the U-Net model were 84.37%, 80.74%, 88.65%, 85.02%, and 87.16%, respectively. When it came to lymph node metastasis number of 1-2, there were notable differences between CT results and postoperative pathology results, and the difference was statistically significant ( P < 0.05 ). As for their identifying lymph node metastasis area, there was no statistically significant difference ( P > 0.05 ). The intervention group exhibited lower postoperative VAS score, shorter time in bed, and shorter hospital stay and indwelling chest tube time versus the control group ( P < 0.01 ). It suggested that the U-Net model optimized by CNN has high diagnostic efficiency for lymph nodes, and the rehabilitation nursing intervention significantly mitigates postoperative pain and accelerates postoperative recovery.
Highlights
Esophageal carcinoma is one of the common malignant tumors originating in the digestive tract
An abundance of research results reveals that lymph node metastasis is one of the most important factors affecting the prognosis of esophageal carcinoma patients, and the number of lymph node metastases is a key factor for the prognosis of esophageal carcinoma patients [3]
CT is characteristic of non-invasiveness and high accuracy, and it is superior to other methods in the diagnosis of esophageal carcinoma, the identification of foci, and the judgment of lymph node metastasis [4]
Summary
Esophageal carcinoma is one of the common malignant tumors originating in the digestive tract It mainly develops in the glands or mucosal epithelium of the esophagus and is one of the top ten malignant tumors with high incidence. An abundance of research results reveals that lymph node metastasis is one of the most important factors affecting the prognosis of esophageal carcinoma patients, and the number of lymph node metastases is a key factor for the prognosis of esophageal carcinoma patients [3]. CT is characteristic of non-invasiveness and high accuracy, and it is superior to other methods in the diagnosis of esophageal carcinoma, the identification of foci, and the judgment of lymph node metastasis [4]. CNN has found broad applications in the segmentation of medical images, most research focuses on the MRI-based human brain image segmentation. ere is little research on CT image segmentation, let alone esophageal CT image segmentation
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