ArticleAmerican journal of translational research2023
Construction of prediction model of lymph node metastasis of early cervical cancer based on machine learning algorithm and its application: experience of 204 cases in a single center.
Article in American journal of translational research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it, 3 citations in OpenAlex.
- PET-CT radiomics and artificial intelligence for predicting lymph node metastasis in cervical cancer: a systematic review and meta-analysis.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Pooled it
- Machine Learning Based Assessment of Inguinal Lymph Node Metastasis in Patients with Squamous Cell Carcinoma of the Vulva.Journal of clinical medicine · 2025Article
- Prediction of lymph node metastasis in cervical cancer patients using AdaBoost machine learning model: analysis of risk factors.American journal of cancer research · 2025Article
- Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThe prediction model of para-aortic lymph node metastasis (LNM) in patients with early cervical cancer was constructed based on the logistic regression (LR) and random forest (RF) algorithms in the machine learning algorithm. The prediction efficiencies of the two models were compared.
methodsThe clinical data of 204 patients with early cervical cancer in the First Affiliated Hospital of Guangxi Medical University were retrospectively collected. The 204 patients were randomly divided into a training set and a verification set according to a ratio of 3:1. The training set was used to build the model. The verification set was used to evaluate model effectiveness. The para-aortic LNM prediction model of early cervical cancer was established by LR and RF. Receiver operating characteristic curve (ROC), sensitivity, and specificity were used to evaluate the prediction performances of the two models.
resultsLR analysis showed that tumor diameter > 4 cm, choroidal aneurysm embolism, pelvic lymph node metastasis, and high preoperative squamous cell carcinoma antigen (SCC-Ag) level were risk factors for para-aortic LNM in patients with early cervical cancer (
conclusionsBoth LR and RF models based on machine learning algorithm have great predictive value in predicting early cervical cancer para-aortic lymph node metastasis.
Indexed as
Identifiers
37056833PMC10086876W4365478827What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.