PloS one · 2025
A machine learning-based model for predicting survival in patients with Rectosigmoid Cancer.
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Wang Yifei, Chen Bingbing, Yu Jinhai
Аннотация
BACKGROUND: The unique anatomical characteristics and blood supply of the rectosigmoid junction confer particular significance to its physiological functions and clinical surgeries. However, research on the prognosis of rectosigmoid junction cancer (RSC) is scarce, and reliable clinical prediction models are lacking.
METHODS: This retrospective study included 524 patients diagnosed with RSC who were admitted to the Department of Gastrointestinal and Colorectal Surgery at the First Hospital of Jilin University between January 1, 2017, and June 1, 2019. Univariate and multivariate Cox regression analyses were conducted in this study to identify independent risk factors impacting the survival of RSC patients. Subsequently, models were constructed using six different machine learning algorithms. Finally, the discrimination, calibration, and clinical applicability of each model were evaluated to determine the optimal model.
RESULTS: Through univariate and multivariate Cox regression analyses, we identified seven independent risk factors associated with the survival of RSC patients: age (HR = 1.9, 95% CI: 1.3-2.8, P = 0.001), gender (HR = 0.6, 95% CI: 0.4-0.9, P = 0.013), diabetes (HR = 2.0, 95% CI: 1.3-3.1, P = 0.002), tumor differentiation (HR = 2.1, 95% CI: 1.4-3.1, P < 0.001), tumor N stage (HR = 2.02, 95% CI: 1.2-3.4, P = 0.009), distant metastasis (HR = 4.2, 95% CI: 2.7-6.7, P < 0.001), and anastomotic leakage (HR = 2.4, 95% CI: 1.1-5.3, P = 0.034). After evaluating each model, the prediction model based on XGBoost was determined to be the optimal model, with AUC of 0.7856, 0.8484, and 0.796 at 1, 3, and 5 years. It also had the lowest Brier scores at all time points, and decision curve analysis (DCA) demonstrated the best clinical decision benefits compared to other models.
CONCLUSION: We developed a prediction model based on the optimal machine learning, XGBoost, which can assist clinical decision-making and potentially extend the survival of patients with rectosigmoid junction cancer.
METHODS: This retrospective study included 524 patients diagnosed with RSC who were admitted to the Department of Gastrointestinal and Colorectal Surgery at the First Hospital of Jilin University between January 1, 2017, and June 1, 2019. Univariate and multivariate Cox regression analyses were conducted in this study to identify independent risk factors impacting the survival of RSC patients. Subsequently, models were constructed using six different machine learning algorithms. Finally, the discrimination, calibration, and clinical applicability of each model were evaluated to determine the optimal model.
RESULTS: Through univariate and multivariate Cox regression analyses, we identified seven independent risk factors associated with the survival of RSC patients: age (HR = 1.9, 95% CI: 1.3-2.8, P = 0.001), gender (HR = 0.6, 95% CI: 0.4-0.9, P = 0.013), diabetes (HR = 2.0, 95% CI: 1.3-3.1, P = 0.002), tumor differentiation (HR = 2.1, 95% CI: 1.4-3.1, P < 0.001), tumor N stage (HR = 2.02, 95% CI: 1.2-3.4, P = 0.009), distant metastasis (HR = 4.2, 95% CI: 2.7-6.7, P < 0.001), and anastomotic leakage (HR = 2.4, 95% CI: 1.1-5.3, P = 0.034). After evaluating each model, the prediction model based on XGBoost was determined to be the optimal model, with AUC of 0.7856, 0.8484, and 0.796 at 1, 3, and 5 years. It also had the lowest Brier scores at all time points, and decision curve analysis (DCA) demonstrated the best clinical decision benefits compared to other models.
CONCLUSION: We developed a prediction model based on the optimal machine learning, XGBoost, which can assist clinical decision-making and potentially extend the survival of patients with rectosigmoid junction cancer.