PDF(Pubmed)
Abstract：
UNASSIGNED: The purpose of this study was to develop and validate a nomogram for estimating the risk of distant metastases (DM) in the early postoperative phase of medullary thyroid cancer (MTC).
UNASSIGNED: We retrospectively reviewed cases of patients diagnosed with MTC from the Surveillance, Epidemiology, and End Results (SEER) database from 2007 to 2017. In addition, we gathered data on patients who diagnosed as MTC at Department of Thyroid Surgery in the First Hospital of Jilin University between 2009 and 2021. Four machine learning algorithms were used for modeling, including random forest classifier (RFC), gradient boosting decision tree (GBDT), logistic regression (LR), and support vector machine (SVM). The optimal model was selected based on accuracy, recall, specificity, receiver operating characteristic curve (ROC), and area under curve (AUC). After that, the Hosmer-Lemeshow goodness-of-fit test, the brier score (BS) and calibration curve were used for validation of the best model, which allowed us to measure the discrepancy between the projected value and the actual value.
UNASSIGNED: Through feature selection, we finally clarified that the following four features are associated with distant metastases of MTC, which are age, surgery, primary tumor (T) and nodes (N). The AUC values of the four models in the internal test set were as follows: random forest: 0.8786 (95% CI, 0.8070-0.9503), GBDT: 0.8402 (95% CI, 0.7606-0.9199), logistic regression: 0.8670(95%CI,0.7927-0.9413), and SVM: 0.8673 (95% CI, 0.7931-0.9415). As can be shown, there was no statistically significant difference in their AUC values. The highest AUC value of the four models were chosen as the best model since. The model was evaluated on the internal test set, and the accuracy was 0.84, recall was 0.76, and specificity was 0.87. The ROC curve was drawn, and the AUC was 0.8786 (95% CI, 0.8070-0.9503), which was higher than the other three models. The model was visualized using the nomogram and its net benefit was shown in both the Decision Curve Analysis (DCA) and Clinical Impact Curve (CIC).
UNASSIGNED: Proposed model had good discrimination ability and could preliminarily screen high-risk patients for DM in the early postoperative period.
UNASSIGNED: We retrospectively reviewed cases of patients diagnosed with MTC from the Surveillance, Epidemiology, and End Results (SEER) database from 2007 to 2017. In addition, we gathered data on patients who diagnosed as MTC at Department of Thyroid Surgery in the First Hospital of Jilin University between 2009 and 2021. Four machine learning algorithms were used for modeling, including random forest classifier (RFC), gradient boosting decision tree (GBDT), logistic regression (LR), and support vector machine (SVM). The optimal model was selected based on accuracy, recall, specificity, receiver operating characteristic curve (ROC), and area under curve (AUC). After that, the Hosmer-Lemeshow goodness-of-fit test, the brier score (BS) and calibration curve were used for validation of the best model, which allowed us to measure the discrepancy between the projected value and the actual value.
UNASSIGNED: Through feature selection, we finally clarified that the following four features are associated with distant metastases of MTC, which are age, surgery, primary tumor (T) and nodes (N). The AUC values of the four models in the internal test set were as follows: random forest: 0.8786 (95% CI, 0.8070-0.9503), GBDT: 0.8402 (95% CI, 0.7606-0.9199), logistic regression: 0.8670(95%CI,0.7927-0.9413), and SVM: 0.8673 (95% CI, 0.7931-0.9415). As can be shown, there was no statistically significant difference in their AUC values. The highest AUC value of the four models were chosen as the best model since. The model was evaluated on the internal test set, and the accuracy was 0.84, recall was 0.76, and specificity was 0.87. The ROC curve was drawn, and the AUC was 0.8786 (95% CI, 0.8070-0.9503), which was higher than the other three models. The model was visualized using the nomogram and its net benefit was shown in both the Decision Curve Analysis (DCA) and Clinical Impact Curve (CIC).
UNASSIGNED: Proposed model had good discrimination ability and could preliminarily screen high-risk patients for DM in the early postoperative period.
摘要：
■本研究的目的是开发和验证用于估计甲状腺髓样癌（MTC）术后早期远处转移（DM）风险的列线图。
■我们回顾性回顾了从监测中诊断为MTC的患者病例，流行病学,和2007年至2017年的最终结果(SEER)数据库。此外,我们收集了2009年至2021年在吉林大学第一医院甲状腺外科诊断为MTC的患者的数据.四种机器学习算法用于建模，包括随机森林分类器(RFC)，梯度增强决策树(GBDT)，逻辑回归(LR)，和支持向量机(SVM)。基于精度选择了最优模型，召回，特异性，接收机工作特性曲线(ROC)，和曲线下面积(AUC)。之后,Hosmer-Lemeshow拟合优度测试,Brier评分（BS）和校准曲线用于验证最佳模型，这使得我们能够衡量预测值和实际值之间的差异。
■通过功能选择，我们最终澄清了以下四个特征与MTC的远处转移有关，这是年龄，手术,原发肿瘤（T）和淋巴结（N）。内部测试集中四个模型的AUC值如下：随机森林：0.8786（95％CI，0.8070-0.9503），GBDT：0.8402（95%CI，0.7606-0.9199），Logistic回归：0.8670（95CI，0.7927-0.9413），和SVM：0.8673（95%CI，0.7931-0.9415）。可以看出,其AUC值无统计学差异。选择四个模型的最高AUC值作为此后的最佳模型。该模型在内部测试集上进行了评估，准确率为0.84，召回率为0.76，特异性为0.87。绘制ROC曲线,AUC为0.8786（95%CI，0.8070-0.9503），高于其他三种型号。使用列线图对模型进行可视化，其净效益显示在决策曲线分析（DCA）和临床影响曲线（CIC）中。
■提出的模型具有良好的辨别能力，可以初步筛查术后早期的DM高危患者。
■我们回顾性回顾了从监测中诊断为MTC的患者病例，流行病学,和2007年至2017年的最终结果(SEER)数据库。此外,我们收集了2009年至2021年在吉林大学第一医院甲状腺外科诊断为MTC的患者的数据.四种机器学习算法用于建模，包括随机森林分类器(RFC)，梯度增强决策树(GBDT)，逻辑回归(LR)，和支持向量机(SVM)。基于精度选择了最优模型，召回，特异性，接收机工作特性曲线(ROC)，和曲线下面积(AUC)。之后,Hosmer-Lemeshow拟合优度测试,Brier评分（BS）和校准曲线用于验证最佳模型，这使得我们能够衡量预测值和实际值之间的差异。
■通过功能选择，我们最终澄清了以下四个特征与MTC的远处转移有关，这是年龄，手术,原发肿瘤（T）和淋巴结（N）。内部测试集中四个模型的AUC值如下：随机森林：0.8786（95％CI，0.8070-0.9503），GBDT：0.8402（95%CI，0.7606-0.9199），Logistic回归：0.8670（95CI，0.7927-0.9413），和SVM：0.8673（95%CI，0.7931-0.9415）。可以看出,其AUC值无统计学差异。选择四个模型的最高AUC值作为此后的最佳模型。该模型在内部测试集上进行了评估，准确率为0.84，召回率为0.76，特异性为0.87。绘制ROC曲线,AUC为0.8786（95%CI，0.8070-0.9503），高于其他三种型号。使用列线图对模型进行可视化，其净效益显示在决策曲线分析（DCA）和临床影响曲线（CIC）中。
■提出的模型具有良好的辨别能力，可以初步筛查术后早期的DM高危患者。
