Anesthetics and long-term survival after cancer surgery-total intravenous versus volatile anesthesia: a retrospective study.

BACKGROUND: Intravenous anesthesia has been reported to have a favorable effect on the prognosis of cancer patients. This study was performed to analyze data regarding the relation between anesthetics and the prognosis of cancer patients in our hospital. METHODS: The medical records of patients who underwent surgical resection for gastric, lung, liver, colon, and breast cancer between January 2006 and December 2009 were reviewed. Depending on the type of anesthetic, it was divided into total intravenous anesthesia (TIVA) or volatile inhaled anesthesia (VIA) group. The 5-year overall survival outcomes were analyzed by log-rank test. Cox proportional hazards modeling was used for sensitivity. RESULTS: The number of patients finally included in the comparison after propensity matching came to 729 in each group. The number of surviving patients at 5 years came to 660 (90.5%) in the TIVA and 673 (92.3%) in the VIA. The type of anesthetic did not affect the 5-year survival rate according to the log-rank test (P = 0.21). Variables associated with a significant increase in the hazard of death after multivariable analysis were male sex and metastasis at surgery. CONCLUSIONS: There were no differences in 5-year overall survival between two groups in the cancer surgery. TRIAL REGISTRATION: Trial registration: CRIS KCT0004101. Retrospectively registered 28 June 2019.

Automated classification of gastric neoplasms in endoscopic images using a convolutional neural network.

BACKGROUND: Visual inspection, lesion detection, and differentiation between malignant and benign features are key aspects of an endoscopist's role. The use of machine learning for the recognition and differentiation of images has been increasingly adopted in clinical practice. This study aimed to establish convolutional neural network (CNN) models to automatically classify gastric neoplasms based on endoscopic images. METHODS: Endoscopic white-light images of pathologically confirmed gastric lesions were collected and classified into five categories: advanced gastric cancer, early gastric cancer, high grade dysplasia, low grade dysplasia, and non-neoplasm. Three pretrained CNN models were fine-tuned using a training dataset. The classifying performance of the models was evaluated using a test dataset and a prospective validation dataset. RESULTS: A total of 5017 images were collected from 1269 patients, among which 812 images from 212 patients were used as the test dataset. An additional 200 images from 200 patients were collected and used for prospective validation. For the five-category classification, the weighted average accuracy of the Inception-Resnet-v2 model reached 84.6 %. The mean area under the curve (AUC) of the model for differentiating gastric cancer and neoplasm was 0.877 and 0.927, respectively. In prospective validation, the Inception-Resnet-v2 model showed lower performance compared with the endoscopist with the best performance (five-category accuracy 76.4 % vs. 87.6 %; cancer 76.0 % vs. 97.5 %; neoplasm 73.5 % vs. 96.5 %; P  < 0.001). However, there was no statistical difference between the Inception-Resnet-v2 model and the endoscopist with the worst performance in the differentiation of gastric cancer (accuracy 76.0 % vs. 82.0 %) and neoplasm (AUC 0.776 vs. 0.865). CONCLUSION: The evaluated deep-learning models have the potential for clinical application in classifying gastric cancer or neoplasm on endoscopic white-light images.