Department of General Surgery, Wuxi People's Hospital Affiliated to Nanjing Medical University, Wuxi, China.
Department of General Practice, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong Province, China.
PLoS One. 2023 Aug 11;18(8):e0289621. doi: 10.1371/journal.pone.0289621. eCollection 2023.
Colon cancer recurrence is a common adverse outcome for patients after complete mesocolic excision (CME) and greatly affects the near-term and long-term prognosis of patients. This study aimed to develop a machine learning model that can identify high-risk factors before, during, and after surgery, and predict the occurrence of postoperative colon cancer recurrence.
The study included 1187 patients with colon cancer, including 110 patients who had recurrent colon cancer. The researchers collected 44 characteristic variables, including patient demographic characteristics, basic medical history, preoperative examination information, type of surgery, and intraoperative information. Four machine learning algorithms, namely extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and k-nearest neighbor algorithm (KNN), were used to construct the model. The researchers evaluated the model using the k-fold cross-validation method, ROC curve, calibration curve, decision curve analysis (DCA), and external validation.
Among the four prediction models, the XGBoost algorithm performed the best. The ROC curve results showed that the AUC value of XGBoost was 0.962 in the training set and 0.952 in the validation set, indicating high prediction accuracy. The XGBoost model was stable during internal validation using the k-fold cross-validation method. The calibration curve demonstrated high predictive ability of the XGBoost model. The DCA curve showed that patients who received interventional treatment had a higher benefit rate under the XGBoost model. The external validation set's AUC value was 0.91, indicating good extrapolation of the XGBoost prediction model.
The XGBoost machine learning algorithm-based prediction model for colon cancer recurrence has high prediction accuracy and clinical utility.
结肠癌患者在接受完整结肠系膜切除术（CME）后，癌症复发是一种常见的不良预后，极大地影响了患者的近期和远期预后。本研究旨在开发一种机器学习模型，能够识别手术前后的高危因素，并预测术后结肠癌复发的发生。
本研究纳入了 1187 例结肠癌患者，其中 110 例患者发生了结肠癌复发。研究人员收集了 44 个特征变量，包括患者的人口统计学特征、基本病史、术前检查信息、手术类型和术中信息。使用了四种机器学习算法，即极端梯度提升（XGBoost）、随机森林（RF）、支持向量机（SVM）和 K 近邻算法（KNN）来构建模型。研究人员使用 K 折交叉验证法、ROC 曲线、校准曲线、决策曲线分析（DCA）和外部验证来评估模型。
在四个预测模型中，XGBoost 算法表现最好。ROC 曲线结果表明，XGBoost 在训练集和验证集的 AUC 值分别为 0.962 和 0.952，表明具有较高的预测准确性。XGBoost 模型在 K 折交叉验证方法的内部验证中表现稳定。校准曲线表明 XGBoost 模型具有较高的预测能力。DCA 曲线表明，在 XGBoost 模型下，接受干预治疗的患者具有更高的获益率。外部验证集的 AUC 值为 0.91，表明 XGBoost 预测模型具有良好的外推能力。
基于 XGBoost 机器学习算法的结肠癌复发预测模型具有较高的预测准确性和临床实用性。
