Qiu Binxu, Shen Zixiong, Wu Song, Qin Xinxin, Yang Dongliang, Wang Quan
Department of Gastric and Colorectal Surgery, General Surgery Center, The First Hospital of Jilin University, Changchun, China.
Department of Thoracic Surgery, The First Hospital of Jilin University, Changchun, China.
Front Oncol. 2023 Aug 15;13:1235121. doi: 10.3389/fonc.2023.1235121. eCollection 2023.
Distant metastasis from rectal cancer usually results in poorer survival and quality of life, so early identification of patients at high risk of distant metastasis from rectal cancer is essential.
The study used eight machine-learning algorithms to construct a machine-learning model for the risk of distant metastasis from rectal cancer. We developed the models using 23867 patients with rectal cancer from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2017. Meanwhile, 1178 rectal cancer patients from Chinese hospitals were selected to validate the model performance and extrapolation. We tuned the hyperparameters by random search and tenfold cross-validation to construct the machine-learning models. We evaluated the models using the area under the receiver operating characteristic curves (AUC), the area under the precision-recall curve (AUPRC), decision curve analysis, calibration curves, and the precision and accuracy of the internal test set and external validation cohorts. In addition, Shapley's Additive explanations (SHAP) were used to interpret the machine-learning models. Finally, the best model was applied to develop a web calculator for predicting the risk of distant metastasis in rectal cancer.
The study included 23,867 rectal cancer patients and 2,840 patients with distant metastasis. Multiple logistic regression analysis showed that age, differentiation grade, T-stage, N-stage, preoperative carcinoembryonic antigen (CEA), tumor deposits, perineural invasion, tumor size, radiation, and chemotherapy were-independent risk factors for distant metastasis in rectal cancer. The mean AUC value of the extreme gradient boosting (XGB) model in ten-fold cross-validation in the training set was 0.859. The XGB model performed best in the internal test set and external validation set. The XGB model in the internal test set had an AUC was 0.855, AUPRC was 0.510, accuracy was 0.900, and precision was 0.880. The metric AUC for the external validation set of the XGB model was 0.814, AUPRC was 0.609, accuracy was 0.800, and precision was 0.810. Finally, we constructed a web calculator using the XGB model for distant metastasis of rectal cancer.
The study developed and validated an XGB model based on clinicopathological information for predicting the risk of distant metastasis in patients with rectal cancer, which may help physicians make clinical decisions. rectal cancer, distant metastasis, web calculator, machine learning algorithm, external validation.
直肠癌远处转移通常会导致较差的生存率和生活质量，因此早期识别直肠癌远处转移高危患者至关重要。
本研究使用八种机器学习算法构建直肠癌远处转移风险的机器学习模型。我们利用2010年至2017年间监测、流行病学和最终结果（SEER）数据库中的23867例直肠癌患者开发模型。同时，选取中国医院的1178例直肠癌患者来验证模型性能和外推性。我们通过随机搜索和十折交叉验证调整超参数以构建机器学习模型。我们使用受试者工作特征曲线下面积（AUC）、精确召回率曲线下面积（AUPRC）、决策曲线分析、校准曲线以及内部测试集和外部验证队列的精度和准确性来评估模型。此外，使用夏普利加性解释（SHAP）来解释机器学习模型。最后，应用最佳模型开发了一个用于预测直肠癌远处转移风险的网络计算器。
该研究纳入了23867例直肠癌患者和2840例有远处转移的患者。多因素逻辑回归分析表明，年龄、分化程度、T分期、N分期、术前癌胚抗原（CEA）、肿瘤沉积、神经侵犯、肿瘤大小、放疗和化疗是直肠癌远处转移的独立危险因素。训练集中极端梯度提升（XGB）模型在十折交叉验证中的平均AUC值为0.859。XGB模型在内部测试集和外部验证集中表现最佳。内部测试集中XGB模型的AUC为0.855，AUPRC为0.510，准确率为0.900，精确率为0.880。XGB模型外部验证集的指标AUC为0.814，AUPRC为0.609，准确率为0.800，精确率为0.810。最后，我们使用XGB模型构建了一个用于直肠癌远处转移的网络计算器。
本研究开发并验证了一种基于临床病理信息的XGB模型，用于预测直肠癌患者远处转移风险，这可能有助于医生做出临床决策。直肠癌、远处转移、网络计算器、机器学习算法、外部验证。
