Machine learning algorithms to predict atypical metastasis of colorectal cancer patients after surgical resection - Yang, Xiaoyan; Yu, Wei; Yang, Feimin; Cai, Xiujun (Frontiers in Surgery, 2023)
Background: The prognosis of colorectal cancer with atypical metastasis is poor. However, atypical metastasis was less common and under-appreciated. Methods: In this study we attempted to present the first machine learning models to predict the risk of atypical metastasis in colorectal cancer patients. We evaluated the differences between metastasis and non-metastasis groups, assessed factors associated with atypical metastasis using univariate and multivariate logistic regression analyses, and preliminarily developed the multiple machine learning models to predict atypical metastasis. Results: 168 patients were included. Prognostic Nutritional Index (PNI) [OR= 0.998; P = 0.030], Cancer antigen 19-9 (CA19-9) [OR = 1.011; P = 0.043] and MR-Distance [-mid OR = 0.289; P = 0.009] [-high OR = 0.248; P = 0.021] were shown to be independent risk factors for the atypical metastasis via multivariate analysis. Furthermore, the machine learning model based on AdaBoost algorithm (AUC: 0736) has better predictive performance comparing to Logistic Regression (AUC: 0.671) and KNeighbors Classifier (AUC: 0.618) by area under the curve (AUC) in the validation cohorts. Conclusion: Machine-learning approaches containing PNI, CA19-9 and MR-Distance show great potentials in atypical metastasis prediction.