DataSheet_1_The value of machine learning in preoperative identification of lymph node metastasis status in endometrial cancer: a systematic review and meta-analysis.docx

BackgroundThe early identification of lymph node metastasis status in endometrial cancer (EC) is a serious challenge in clinical practice. Some investigators have introduced machine learning into the early identification of lymph node metastasis in EC patients. However, the predictive value of machine learning is controversial due to the diversity of models and modeling variables. To this end, we carried out this systematic review and meta-analysis to systematically discuss the value of machine learning for the early identification of lymph node metastasis in EC patients.
MethodsA systematic search was conducted in Pubmed, Cochrane, Embase, and Web of Science until March 12, 2023. PROBAST was used to assess the risk of bias in the included studies. In the process of meta-analysis, subgroup analysis was performed according to modeling variables (clinical features, radiomic features, and radiomic features combined with clinical features) and different types of models in various variables.
ResultsThis systematic review included 50 primary studies with a total of 103,752 EC patients, 12,579 of whom had positive lymph node metastasis. Meta-analysis showed that among the machine learning models constructed by the three categories of modeling variables, the best model was constructed by combining radiomic features with clinical features, with a pooled c-index of 0.907 (95%CI: 0.886-0.928) in the training set and 0.823 (95%CI: 0.757-0.890) in the validation set, and good sensitivity and specificity. The c-index of the machine learning model constructed based on clinical features alone was not inferior to that based on radiomic features only. In addition, logistic regression was found to be the main modeling method and has ideal predictive performance with different categories of modeling variables.
ConclusionAlthough the model based on radiomic features combined with clinical features has the best predictive efficiency, there is no recognized specification for the application of radiomics at present. In addition, the logistic regression constructed by clinical features shows good sensitivity and specificity. In this context, large-sample studies covering different races are warranted to develop predictive nomograms based on clinical features, which can be widely applied in clinical practice.
Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO, identifier CRD42023420774.

【背景】子宫内膜癌（endometrial cancer, EC）患者淋巴结转移状态的早期识别是临床实践中的一大难题。已有研究者将机器学习（machine learning）应用于EC患者淋巴结转移的早期识别中。但由于模型类型及建模变量存在多样性，机器学习的预测价值尚存争议。为此，本研究开展此项系统评价与荟萃分析，系统探讨机器学习在EC患者淋巴结转移早期识别中的应用价值。
【方法】本研究于2023年3月12日前在PubMed、Cochrane、Embase及Web of Science数据库中进行系统性文献检索。采用PROBAST工具对纳入研究的偏倚风险进行评价。在荟萃分析过程中，根据建模变量（临床特征、放射组学特征、放射组学特征联合临床特征）及不同变量类型下的模型类型进行亚组分析。
【结果】本项系统评价共纳入50项原始研究，涉及103752例EC患者，其中12579例存在淋巴结转移阳性。荟萃分析结果显示，在三类建模变量构建的机器学习模型中，以放射组学特征联合临床特征构建的模型性能最优：训练集合并C指数为0.907（95%置信区间：0.886~0.928），验证集合并C指数为0.823（95%置信区间：0.757~0.890），且具有良好的灵敏度与特异度。仅基于临床特征构建的机器学习模型的C指数并不劣于仅基于放射组学特征的模型。此外，逻辑回归（logistic regression）是最常用的建模方法，在不同类别建模变量下均表现出理想的预测性能。
【结论】尽管基于放射组学特征联合临床特征的模型预测效能最优，但目前放射组学的应用尚无公认的规范。此外，基于临床特征构建的逻辑回归模型同样具备良好的灵敏度与特异度。在此背景下，亟需开展覆盖不同种族的大样本研究，以开发基于临床特征的预测列线图，从而实现其在临床实践中的广泛应用。
【系统评价注册】https://www.crd.york.ac.uk/PROSPERO，注册号：CRD42023420774。