Qiu Binxu, Shen Zixiong, 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 May 24;13:1183072. doi: 10.3389/fonc.2023.1183072. eCollection 2023.
Metastasis in the lungs is common in patients with rectal cancer, and it can have severe consequences on their survival and quality of life. Therefore, it is essential to identify patients who may be at risk of developing lung metastasis from rectal cancer.
In this study, we utilized eight machine-learning methods to create a model for predicting the risk of lung metastasis in patients with rectal cancer. Our cohort consisted of 27,180 rectal cancer patients selected from the Surveillance, Epidemiology and End Results (SEER) database between 2010 and 2017 for model development. Additionally, we validated our models using 1118 rectal cancer patients from a Chinese hospital to evaluate model performance and generalizability. We assessed our models' performance using various metrics, including the area under the curve (AUC), the area under the precision-recall curve (AUPR), the Matthews Correlation Coefficient (MCC), decision curve analysis (DCA), and calibration curves. Finally, we applied the best model to develop a web-based calculator for predicting the risk of lung metastasis in patients with rectal cancer.
Our study employed tenfold cross-validation to assess the performance of eight machine-learning models for predicting the risk of lung metastasis in patients with rectal cancer. The AUC values ranged from 0.73 to 0.96 in the training set, with the extreme gradient boosting (XGB) model achieving the highest AUC value of 0.96. Moreover, the XGB model obtained the best AUPR and MCC in the training set, reaching 0.98 and 0.88, respectively. We found that the XGB model demonstrated the best predictive power, achieving an AUC of 0.87, an AUPR of 0.60, an accuracy of 0.92, and a sensitivity of 0.93 in the internal test set. Furthermore, the XGB model was evaluated in the external test set and achieved an AUC of 0.91, an AUPR of 0.63, an accuracy of 0.93, a sensitivity of 0.92, and a specificity of 0.93. The XGB model obtained the highest MCC in the internal test set and external validation set, with 0.61 and 0.68, respectively. Based on the DCA and calibration curve analysis, the XGB model had better clinical decision-making ability and predictive power than the other seven models. Lastly, we developed an online web calculator using the XGB model to assist doctors in making informed decisions and to facilitate the model's wider adoption (https://share.streamlit.io/woshiwz/rectal_cancer/main/lung.py).
In this study, we developed an XGB model based on clinicopathological information to predict the risk of lung metastasis in patients with rectal cancer, which may help physicians make clinical decisions.
肺癌转移在直肠癌患者中很常见，并且会对他们的生存和生活质量产生严重影响。因此，识别可能有直肠癌肺转移风险的患者至关重要。
在本研究中，我们使用了八种机器学习方法来创建一个预测直肠癌患者肺转移风险的模型。我们的队列由2010年至2017年间从监测、流行病学和最终结果（SEER）数据库中选取的27180例直肠癌患者组成，用于模型开发。此外，我们使用一家中国医院的1118例直肠癌患者对我们的模型进行验证，以评估模型性能和通用性。我们使用各种指标评估模型性能，包括曲线下面积（AUC）、精确召回曲线下面积（AUPR）、马修斯相关系数（MCC）、决策曲线分析（DCA）和校准曲线。最后，我们应用最佳模型开发了一个基于网络的计算器，用于预测直肠癌患者的肺转移风险。
我们的研究采用十折交叉验证来评估八种机器学习模型预测直肠癌患者肺转移风险的性能。训练集中的AUC值范围为0.73至0.96，极端梯度提升（XGB）模型的AUC值最高，为 0.96。此外，XGB模型在训练集中获得了最佳的AUPR和MCC，分别达到0.98和0.88。我们发现XGB模型表现出最佳的预测能力，在内部测试集中的AUC为0.87，AUPR为0.60，准确率为0.92，灵敏度为0.93。此外，XGB模型在外部测试集中进行了评估，AUC为0.91，AUPR为0.63，准确率为0.93，灵敏度为0.92，特异性为0.93。XGB模型在内部测试集和外部验证集中获得了最高的MCC，分别为0.61和0.68。基于DCA和校准曲线分析，XGB模型比其他七个模型具有更好的临床决策能力和预测能力。最后，我们使用XGB模型开发了一个在线网络计算器，以帮助医生做出明智的决策，并促进该模型的更广泛应用（https://share.streamlit.io/woshiwz/rectal_cancer/main/lung.py）。
在本研究中，我们基于临床病理信息开发了一个XGB模型，以预测直肠癌患者的肺转移风险，这可能有助于医生做出临床决策。
