机器学习在子宫内膜癌淋巴结转移术前识别中的价值：系统评价与荟萃分析的数据表1

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• 源站标题：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
• DOI：10.3389/fonc.2023.1289050.s001
• 发布日期：2023-12-20
• 文件数：1 个
• 文件总体积：12.92 MB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/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/24872898

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curl -L -o "DataSheet_1_The value of machine learning in preoperative identification of lymph node metastasis status in endometrial cancer: a systematic review an.docx" "https://ndownloader.figshare.com/files/43764777"

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• DataSheet_1_The value of machine learning in preoperative identification of lymph node metastasis status in endometrial cancer: a systematic review an.docx（12.92 MB）

文件总体积 12.92 MB（源站 API 实测）。

数据说明

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

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CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fonc.2023.1289050.s001（source: https://frontiersin.figshare.com/articles/dataset/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/24872898）。