| 摘要: | Abstract: Colorectal cancer is ranked third and fourth in terms of mortality and cancer incidence
in the world. While advances in treatment strategies have provided cancer patients with longer
survival, potentially harmful second primary cancers can occur. Therefore, second primary colorectal
cancer analysis is an important issue with regard to clinical management. In this study, a novel
predictive scheme was developed for predicting the risk factors associated with second colorectal
cancer in patients with colorectal cancer by integrating five machine learning classification techniques,
including support vector machine, random forest, multivariate adaptive regression splines, extreme
learning machine, and extreme gradient boosting. A total of 4287 patients in the datasets provided
by three hospital tumor registries were used. Our empirical results revealed that this proposed
predictive scheme provided promising classification results and the identification of important risk
factors for predicting second colorectal cancer based on accuracy, sensitivity, specificity, and area
under the curve metrics. Collectively, our clinical findings suggested that the most important risk
factors were the combined stage, age at diagnosis, BMI, surgical margins of the primary site, tumor
size, sex, regional lymph nodes positive, grade/differentiation, primary site, and drinking behavior.
Accordingly, these risk factors should be monitored for the early detection of second primary tumors
in order to improve treatment and intervention strategies. |
