Background: Surgery is the only way to cure gastric adenocarcinoma (GAC), and chemotherapy is the basic adjuvant management for GAC. A significant prognostic nomogram for predicting the respective disease-specific survival (DSS) rates of GAC patients who receive surgery and chemotherapy has not been established. Objective: We were planning to establish a survival nomogram model for GAC patients who receive surgery and chemotherapy. Methods: We identified 5764 GAC patients who had received surgery and chemotherapy from the record of Surveillance, Epidemiology, and End Results (SEER) database. About 70% (n = 4034) of the chosen GAC patients were randomly assigned to the training set, and the rest of the included ones (n = 1729) were assigned to the external validation set. A prognostic nomogram was constructed by the training set and the predictive accuracy of it was validated by the validation set. Results: Based on the outcome of a multivariate analysis of candidate factors, a nomogram was developed that encompassed age at diagnosis, number of regional lymph nodes examined after surgery, number of positive regional lymph nodes, sex, race, grade, derived AJCC stage, summary stage, and radiotherapy status. The C-index (Harrell’s concordance index) of the nomogram model was some larger than that of the traditional seventh AJCC staging system (0.707 vs 0.661). Calibration plots of the constructed nomogram displayed that the probability of DSS commendably accord with the survival rate. Integrated discrimination improvement (IDI) revealed obvious increase and categorical net reclassification improvement (NRI) showed visible enhancement. IDI for 3-, 5- and 10- year DSS were 0.058, 0.059 and 0.058, respectively (P > 0.05), and NRI for 3-, 5- and 10- year DSS were 0.380 (95% CI = 0.316–0.470), 0.407 (95% CI = 0.350–0.505), and 0.413 (95% CI = 0.336–0.519), respectively. Decision curve analysis (DCA) proved that the constructed nomogram was preferable to the AJCC staging system. Conclusion: The constructed nomogram supplies more credible DSS predictions for GAC patients who receive surgery and chemotherapy in the general population. According to validation, the new nomogram will be beneficial in facilitating individualized survival predictions and useful when performing clinical decision-making for GAC patients who receive surgery and chemotherapy.

Objective This study aims to establish a machine learning (ML) model for predicting the risk of distant metastasis in colorectal cancer (CRC). Methods The pathological data of 104436 patients (2858 of them coming from China) diagnosed with colorectal cancer (denoted as CRC) in the United States of America between years 2010 and 2021 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database (built by the National Institute of Health of the United States of America). They were classified into two groups (the whole group (coded as G_w) covering all the 104436 patients and the branch group (coded as G_b) covering 2858 patients coming from China) and treated separately with 8 machine learning (ML) algorithms to establish the machine learning models for predicting the distant metastasis risk of CRC. The as-established ML models were evaluated based on accuracy, recall, and the area under the receiver operating characteristic (ROC) curve (AUC); and those possessing the best predicting performance for G_w and G_b were identified accordingly. Besides, the clinical-pathological features and their relationships with the target variables were analyzed by using the identified best models, while their performance and generalizability were externally validated by inputting the pathological data of 109 CRC patients from the Huaihe Hospital of Henan University (Kaifeng, China). Furthermore, a web-based calculator was developed with the identified best model to predict the distant metastasis risk of CRC patients in China. Results A total of 104436 patients from the general population (including 2858 patients from Chinese population) were included, of which 7864 cases (15.3%) were found to have a high risk of distant metastasis. Among the ML models developed for Chinese population, the Gradient Boosting model showed the best predictive performance, exhibiting an area under the receiver operating characteristic (ROC) curve (AUC) of 0.9571 in the internal test set. As to the models pointing to the general population, the XGBoost model performed best, exhibiting an AUC value of 0.9757 in the internal test set. In terms of the external validation of the as-established ML models, the model built with the pathological data of 109 CRC patients from the Huaihe Hospital of Henan University (Kaifeng, China) outperformed the one built with the pathological data of G_w. Namely, they provided accuracy rates of 0.9083 and 0.8716, precision rates of 0.9060 and 0.8680, recall rates of 0.9083 and 0.8716, and F1 scores of 0.9067 and 0.8694, respectively. Conclusion This study developed a Gradient Boosting-based model for predicting the risk of distant metastasis in colorectal cancer, providing an effective clinical decision support tool for physicians.