Application of machine learning techniques in real-world research to predict the risk of liver metastasis in rectal cancer.

Background: The liver is the most common site of distant metastasis in rectal cancer, and liver metastasis dramatically affects the treatment strategy of patients. This study aimed to develop and validate a clinical prediction model based on machine learning algorithms to predict the risk of liver metastasis in patients with rectal cancer. Methods: We integrated two rectal cancer cohorts from Surveillance, Epidemiology, and End Results (SEER) and Chinese multicenter hospitals from 2010-2017. We also built and validated liver metastasis prediction models for rectal cancer using six machine learning algorithms, including random forest (RF), light gradient boosting (LGBM), extreme gradient boosting (XGB), multilayer perceptron (MLP), logistic regression (LR), and K-nearest neighbor (KNN). The models were evaluated by combining several metrics, such as the area under the curve (AUC), accuracy score, sensitivity, specificity and F1 score. Finally, we created a network calculator using the best model. Results: The study cohort consisted of 19,958 patients from the SEER database and 924 patients from two hospitals in China. The AUC values of the six prediction models ranged from 0.70 to 0.95. The XGB model showed the best predictive power, with the following metrics assessed in the internal test set: AUC (0.918), accuracy (0.884), sensitivity (0.721), and specificity (0.787). The XGB model was assessed in the outer test set with the following metrics: AUC (0.926), accuracy (0.919), sensitivity (0.740), and specificity (0.765). The XGB algorithm also shows a good fit on the calibration decision curves for both the internal test set and the external validation set. Finally, we constructed an online web calculator using the XGB model to help generalize the model and to assist physicians in their decision-making better. Conclusion: We successfully developed an XGB-based machine learning model to predict liver metastasis from rectal cancer, which was also validated with a real-world dataset. Finally, we developed a web-based predictor to guide clinical diagnosis and treatment strategies better.

Isolation and identification of bovine nasopharyngeal mucosal epithelial cells and establishment of cell models of acute infection by foot-and-mouth disease virus.

Foot-and-mouth disease (FMD) commonly occurs via the respiratory tract, and bovine nasopharyngeal mucosal epithelial cells are the primary infection cells in cattle. The aim of the present study was to isolate and culture epithelial cells from the bovine nasopharyngeal mucosa in vitro using a mechanical separation method. The cells were expanded, established in continuous cell culture, and used for immunofluorescence cytochemistry and establishment of infection models. We detected pan-cytokeratin markers of bovine nasopharyngeal mucosal epithelial cells by immunofluorescence. Bovine nasopharyngeal mucosal epithelial cells were then infected with foot-and-mouth disease virus (FMDV) serum type O. RT-PCR demonstrated the successful establishment of acute FMDV infection in the cell models. This infection model provides the basis for clarification of the interaction between FMDV and host bovine nasopharyngeal mucosal epithelial cells in vitro.