ArticleJournal of gynecologic oncology2024
A machine learning-based prediction model of pelvic lymph node metastasis in women with early-stage cervical cancer.
Article in Journal of gynecologic oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.
- Progress and current trends in prediction models for the occurrence and prognosis of cancer and cancer-related complications: a bibliometric and visualization analysis.Frontiers in oncology · 2025Pooled it
- Comprehensive Analysis of Cuproptosis-Related Genes According to Cancer Stage and Their Prognostic Value in Cervical Cancer.International journal of molecular sciences · 2026Article
- Machine learning-based prognostic model for local control prediction in lung cancer patients undergoing stereotactic body radiation therapy: a preliminary study.European journal of medical research · 2026Article
- The differences of pelvic lymph node metastasis between squamous cell carcinoma and adenocarcinoma in early-stage cervical cancer patients undergoing radical surgery and adjuvant radiotherapy: a large cohort study.Therapeutic advances in medical oncology · 2026Article
- Exploiting artificial intelligence in precision oncology: an updated comprehensive review.Journal of translational medicine · 2025Review
- Machine and Deep Learning for the Diagnosis, Prognosis, and Treatment of Cervical Cancer: A Scoping Review.Diagnostics (Basel, Switzerland) · 2025Review
- Machine Learning Based Assessment of Inguinal Lymph Node Metastasis in Patients with Squamous Cell Carcinoma of the Vulva.Journal of clinical medicine · 2025Article
- Artificial intelligence radiomics in the diagnosis, treatment, and prognosis of gynecological cancer: a literature review.Translational cancer research · 2025Review
- Prediction of lymph node metastasis in cervical cancer patients using AdaBoost machine learning model: analysis of risk factors.American journal of cancer research · 2025Article
- Development and validation of a machine learning model to predict the risk of lymph node metastasis in early-stage supraglottic laryngeal cancer.Frontiers in oncology · 2025Article
- Enhancing cervical cancer diagnosis with ensemble learning and shark optimization algorithm: comparative study of CT and MRI in cervical cancer diagnosis.Frontiers in oncology · 2025Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo develop a novel machine learning-based preoperative prediction model for pelvic lymph node metastasis (PLNM) in early-stage cervical cancer by combining the clinical findings and preoperative computerized tomography (CT) of the whole abdomen and pelvis.
methodsPatients diagnosed with International Federation of Gynecology and Obstetrics stage IA2-IIA1 squamous cell carcinoma, adenocarcinoma, and adenosquamous carcinoma of the cervix who had primary radical surgery with bilateral pelvic lymphadenectomy from January 1, 2003 to December 31, 2020, were included. Seven supervised machine learning algorithms, including logistic regression, random forest, support vector machine, adaptive boosting, gradient boosting, extreme gradient boosting, and category boosting, were used to evaluate the risk of PLNM.
resultsPLNM was found in 199 (23.9%) of 832 patients included. Younger age, larger tumor size, higher stage, no prior conization, tumor appearance, adenosquamous histology, and vaginal metastasis as well as the CT findings of larger tumor size, parametrial metastasis, pelvic lymph node enlargement, and vaginal metastasis, were significantly associated with PLNM. The models' predictive performance, including accuracy (89.1%-90.6%), area under the receiver operating characteristics curve (86.9%-91.0%), sensitivity (77.4%-82.4%), specificity (92.1%-94.3%), positive predictive value (77.0%-81.7%), and negative predictive value (93.0%-94.4%), appeared satisfactory and comparable among all the algorithms. After optimizing the model's decision threshold to enhance the sensitivity to at least 95%, the 'highly sensitive' model was obtained with a 2.5%-4.4% false-negative rate of PLNM prediction.
conclusionWe developed prediction models for PLNM in early-stage cervical cancer with promising prediction performance in our setting. Further external validation in other populations is needed with potential clinical applications.
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