Utilizing machine learning algorithms for predicting risk factors for bone metastasis from right-sided colon carcinoma after complete mesocolic excision: a 10-year retrospective multicenter study

Abstract

Background: Bone metastasis (BM) occurs when colon cancer cells disseminate from the primary tumor site to the skeletal system via the bloodstream or lymphatic system. The emergence of such bone metastases typically heralds a significantly poor prognosis for the patient. This study's primary aim is to develop a machine learning model to identify patients at elevated risk of bone metastasis among those with right-sided colon cancer undergoing complete mesocolonectomy (CME).

Patients and methods: The study cohort comprised 1,151 individuals diagnosed with right-sided colon cancer, with a subset of 73 patients presenting with bone metastases originating from the colon. We used univariate and multivariate regression analyses as well as four machine learning algorithms to screen variables for 38 characteristic variables such as patient demographic characteristics and surgical information. The study employed four distinct machine learning algorithms, namely, extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and k-nearest neighbor algorithm (KNN), to develop the predictive model. Additionally, the model was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA), while Shapley additive explanation (SHAP) was utilized to visualize and analyze the model.

Results: The XGBoost algorithm performed the best performance among the four prediction models. In the training set, the XGBoost algorithm had an area under curve (AUC) value of 0.973 (0.953-0.994), an accuracy of 0.925 (0.913-0.936), a sensitivity of 0.921 (0.902-0.940), and a specificity of 0.908 (0.894-0.922). In the validation set, the XGBoost algorithm had an AUC value of 0.922 (0.833-0.995), an accuracy of 0.908 (0.889-0.926), a sensitivity of 0.924 (0.873-0.975), and a specificity of 0.883 (0.810-0.956). Furthermore, the AUC value of 0.83 for the external validation set suggests that the XGBoost prediction model possesses strong extrapolation capabilities. The results of SHAP analysis identified alkaline phosphatase (ALP) levels, tumor size, invasion depth, lymph node metastasis, lung metastasis, and postoperative neutrophil-to-lymphocyte ratio (NLR) levels as significant risk factors for BM from right-sided colon cancer subsequent to CME.

Conclusion: The prediction model for BM from right-sided colon cancer developed using the XGBoost machine learning algorithm in this study is both highly precise and clinically valuable.

Keywords: Bone metastasis; Colonic neoplasms; Machine learning; Prognosis; Risk factor.

Fig. 1

Flow diagram of patients included in the study

Fig. 2

The variable ranking plots of the four models. A Variable importance ranking diagram of the XGBoost model. B Variable importance ranking diagram of the RF model. C Variable importance ranking diagram of the SVM model. D Variable importance ranking diagram of the KNN model

Fig. 3

Evaluation of the four models for predicting BM. A ROC curves for the training set of the four models. B ROC curves for the validation set of the four models. C Calibration plots of the four models. The 45° dotted line on each graph represents the perfect match between the observed (y-axis) and predicted (x-axis) complication probabilities. A closer distance between two curves indicates greater accuracy. D DCA curves of the four models. The intersection of the red curve and the all curve is the starting point, and the intersection of the red curve and the None curve is the node within which the corresponding patients can benefit

Fig. 4

Internal validation of the XGBoost model. A ROC curve of the XGBoost model for the training set. B ROC curve of the XGBoost model for the validation set. C ROC curve of the XGBoost model for the test set. D External validation of the XGBoost model

Fig. 5

SHAP summary plot. Risk factors are arranged along the y-axis based on their importance, which is given by the mean of their absolute Shapley values. The higher the risk factor is positioned in the plot, the more important it is for the model

Fig. 6

SHAP force plot. The contributing variables are arranged in the horizontal line, sorted by the absolute value of their impact. Blue represents features that have a negative effect on disease prediction, with a decrease in SHAP values; red represents features that have a positive effect on disease prediction, with an increase in SHAP values. A Predictive Analysis of Patient I. B Predictive Analysis of Patient II. C Predictive Analysis of Patient III. D Predictive Analysis of Patient IV