Comprehensive Assessment of ERα, PR, Ki67, P53 to Predict the Risk of Lymph Node Metastasis in Low-Risk Endometrial Cancer

Huang, Yuzhen; Jiang, Peng; Kong, Wei; Tu, Yuan; Li, Ning; Wang, Jinyu; Qian, Zhou; Yuan, Rui

doi:10.1080/08941939.2022.2152508

Cited by 2 publications

(2 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then, after reading the titles or abstracts of the remaining literature, 62 primary studies were initially eligible, and their full texts were downloaded. After reading the full texts, 50 studies were finally included in this systematic review ( 19 – 68 ). The literature screening process is shown in Figure 1 .…”

Section: Resultsmentioning

confidence: 99%

“…The median number of cases is 342 (IQR: 200~661), and the median number of cases in training sets is 300 (IQR: 154~533). Only 29 studies ( 19 – 21 , 23 , 25 , 26 , 29 – 33 , 36 – 38 , 40 , 43 , 45 , 47 – 50 , 52 , 54 , 55 , 58 , 60 – 63 ) had independent validation sets. Among them, 11 studies ( 21 , 30 , 31 , 33 , 36 , 45 , 55 , 61 – 63 , 65 ) adopted the method of external validation, and the remaining studies carried out internal validation by random sampling.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

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

Ren,

Chen,

Hong

et al. 2023

Front. Oncol.

View full text Add to dashboard Cite

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.

show abstract

Section: Resultsmentioning

confidence: 99%