Table_1_Machine learning-based dynamic prediction of lateral lymph node metastasis in patients with papillary thyroid cancer.docx

ObjectiveTo develop a web-based machine learning server to predict lateral lymph node metastasis (LLNM) in papillary thyroid cancer (PTC) patients.
MethodsClinical data for PTC patients who underwent primary thyroidectomy at our hospital between January 2015 and December 2020, with pathologically confirmed presence or absence of any LLNM finding, were retrospectively reviewed. We built all models from a training set (80%) and assessed them in a test set (20%), using algorithms including decision tree, XGBoost, random forest, support vector machine, neural network, and K-nearest neighbor algorithm. Their performance was measured against a previously established nomogram using area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA), precision, recall, accuracy, F1 score, specificity, and sensitivity. Interpretable machine learning was used for identifying potential relationships between variables and LLNM, and a web-based tool was created for use by clinicians.
ResultsA total of 1135 (62.53%) out of 1815 PTC patients enrolled in this study experienced LLNM episodes. In predicting LLNM, the best algorithm was random forest. In determining feature importance, the AUC reached 0.80, with an accuracy of 0.74, sensitivity of 0.89, and F1 score of 0.81. In addition, DCA showed that random forest held a higher clinical net benefit. Random forest identified tumor size, lymph node microcalcification, age, lymph node size, and tumor location as the most influentials in predicting LLNM. And the website tool is freely accessible at http://43.138.62.202/.
ConclusionThe results showed that machine learning can be used to enable accurate prediction for LLNM in PTC patients, and that the web tool allowed for LLNM risk assessment at the individual level.

研究目的：开发一款基于网页的机器学习服务器，用于预测乳头状甲状腺癌（papillary thyroid cancer, PTC）患者的侧颈淋巴结转移（lateral lymph node metastasis, LLNM）。
方法：本研究回顾性分析2015年1月至2020年12月于本院行原发性甲状腺切除术的乳头状甲状腺癌患者的临床资料，所有患者均经病理证实存在或不存在侧颈淋巴结转移。本研究以80%的数据作为训练集构建模型，剩余20%作为测试集开展模型评估，所采用的算法包括决策树、XGBoost、随机森林、支持向量机、神经网络以及K近邻算法。以受试者工作特征曲线下面积（area under the receiver operating characteristic curve, AUC）、决策曲线分析（decision curve analysis, DCA）、精确率、召回率、准确率、F1得分、特异度以及灵敏度作为模型性能评估指标。同时采用可解释机器学习方法挖掘变量与侧颈淋巴结转移间的潜在关联，并开发一款面向临床医师的网页工具。
结果：本研究共纳入1815例乳头状甲状腺癌患者，其中1135例（62.53%）发生侧颈淋巴结转移。在侧颈淋巴结转移预测任务中，随机森林算法表现最优。在特征重要性分析中，该模型的受试者工作特征曲线下面积达0.80，准确率为0.74，灵敏度为0.89，F1得分为0.81。此外，决策曲线分析结果显示，随机森林模型具有更高的临床净获益。随机森林算法筛选出肿瘤大小、淋巴结微钙化、年龄、淋巴结大小以及肿瘤部位为预测侧颈淋巴结转移的关键影响因素。本研究开发的网页工具可通过http://43.138.62.202/免费访问。
结论：本研究结果表明，机器学习可用于准确预测乳头状甲状腺癌患者的侧颈淋巴结转移，且所开发的网页工具可实现个体化的侧颈淋巴结转移风险评估。