ArticleBMC medical imaging2023
Prediction of lymph node status in patients with early-stage cervical cancer based on radiomic features of magnetic resonance imaging (MRI) images.
Article in BMC medical imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Noninvasive prediction of lymph-vascular space invasion in cervical cancer based on ultrasomics nomogram.BMC cancer · 2026Article
- Prediction Model of Lymph Node Metastasis in Cervical Cancer Based on MRI Habitat Radiomics.Cancers · 2025Article
- Recent advances in applications of machine learning in cervical cancer research: a focus on prediction models.Obstetrics & gynecology science · 2025Article
- Machine and Deep Learning for the Diagnosis, Prognosis, and Treatment of Cervical Cancer: A Scoping Review.Diagnostics (Basel, Switzerland) · 2025Review
- Prediction of clinical stages of cervical cancer via machine learning integrated with clinical features and ultrasound-based radiomics.Scientific reports · 2025Article
- Artificial intelligence radiomics in the diagnosis, treatment, and prognosis of gynecological cancer: a literature review.Translational cancer research · 2025Review
- A gene-based predictive model for lymph node metastasis in cervical cancer: superior performance over imaging techniques.Journal of translational medicine · 2025Article
- Fusion model combining ultrasound-based radiomics and deep transfer learning with clinical parameters for preoperative prediction of pelvic lymph node metastasis in cervical cancer.Frontiers in oncology · 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
- Magnetic Resonance Imaging Radiomics-Based Model for Prediction of Lymph Node Metastasis in Cervical Cancer.International journal of general medicine · 2025Article
- Predicting preoperative lymph node status in patients with cervical cancer: development of interpretable machine learning model and support for the biological plausibility.Frontiers in immunology · 2025Article
- Review
- An interpretable clinical ultrasound-radiomics combined model for diagnosis of stage I cervical cancer.Frontiers in oncology · 2024Article
- A bibliometric review of predictive modelling for cervical cancer risk.Frontiers in research metrics and analytics · 2024Review
- Hematological indicator-based machine learning models for preoperative prediction of lymph node metastasis in cervical cancer.Frontiers in oncology · 2024Article
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5 authors.
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Abstract
backgroundLymph node metastasis is an important factor affecting the treatment and prognosis of patients with cervical cancer. However, the comparison of different algorithms and features to predict lymph node metastasis is not well understood. This study aimed to construct a non-invasive model for predicting lymph node metastasis in patients with cervical cancer based on clinical features combined with the radiomic features of magnetic resonance imaging (MRI) images.
methodsA total of 180 cervical cancer patients were divided into the training set (n = 126) and testing set (n = 54). In this cross-sectional study, radiomic features of MRI images and clinical features of patients were collected. The least absolute shrinkage and selection operator (LASSO) regression was used to filter the features. Seven machine learning methods, including eXtreme Gradient Boosting (XGBoost), Logistic Regression, Multinomial Naive Bayes (MNB), Support Vector Machine (SVM), Decision Tree, Random Forest, and Gradient Boosting Decision Tree (GBDT) are used to build the models. Receiver operating characteristics (ROC) curve and area under the curve (AUC), accuracy, sensitivity, and specificity were calculated to assess the performance of the models.
resultsOf these 180 patients, 49 (27.22%) patients had lymph node metastases. Five of the 122 radiomic features and 3 clinical features were used to build predictive models. Compared with other models, the MNB model was the most robust, with its AUC, specificity, and accuracy on the testing set of 0.745 (95%CI: 0.740-0.750), 0.900 (95%CI: 0.807-0.993), and 0.778 (95%CI: 0.667-0.889), respectively. Furthermore, the AUCs of the MNB models with clinical features only, radiomic features only, and combined features were 0.698 (95%CI: 0.692-0.704), 0.632 (95%CI: 0.627-0.637), and 0.745 (95%CI: 0.740-0.750), respectively.
conclusionThe MNB model, which combines the radiomic features of MRI images with the clinical features of the patient, can be used as a non-invasive tool for the preoperative assessment of lymph node metastasis.
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