ArticleTechnology in cancer research & treatment
Evaluation of Prognosis in Nasopharyngeal Cancer Using Machine Learning.
Article in Technology in cancer research & treatment. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of predictive models for hospital-acquired pressure injury using machine learning.Nursing open · 2023Pooled it
- Research progress of machine learning applications in gastric cancer diagnosis and therapy.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Early and late mortality among patients with T1-T3 head and neck squamous cell carcinoma: a machine learning analysis using a SEER population-based cohort study.Scientific reports · 2026Article
- A prognostic nomogram integrating Epstein-Barr virus DNA and specific inflammatory indices for early recurrent or metastatic nasopharyngeal carcinoma after first-line nedaplatin-based therapy.Discover oncology · 2026Article
- Development of machine learning models for survival prediction in nasopharyngeal carcinoma using population-based data.Discover oncology · 2025Article
- Article
- Machine learning model for predicting recurrence following intensity-modulated radiation therapy in nasopharyngeal carcinoma.World journal of surgical oncology · 2025Article
- Prospective virtual screening combined with bio-molecular simulation enabled identification of new inhibitors for the KRAS drug target.BMC chemistry · 2024Article
- Prediction of Cervical Cancer Patients' Survival Period with Machine Learning Techniques.Healthcare informatics research · 2024Article
- Machine learning explainability in nasopharyngeal cancer survival using LIME and SHAP.Scientific reports · 2023Article
- Iterated cross validation method for prediction of survival in diffuse large B-cell lymphoma for small size dataset.Scientific reports · 2023Article
- Observational
- Progression-Free Survival Prediction in Patients with Nasopharyngeal Carcinoma after Intensity-Modulated Radiotherapy: Machine Learning vs. Traditional Statistics.Journal of personalized medicine · 2021Article
- Emerging landscape of circHIPK3 and its role in cancer and other diseases (Review).Molecular medicine reports · 2021Review
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
aimAlthough the prognosis of nasopharyngeal cancer largely depends on a classification based on the tumor-lymph node metastasis staging system, patients at the same stage may have different clinical outcomes. This study aimed to evaluate the survival prognosis of nasopharyngeal cancer using machine learning. SETTINGS AND
designOriginal, retrospective. MATERIALS AND
methodsA total of 72 patients with a diagnosis of nasopharyngeal cancer who received radiotherapy ± chemotherapy were included in the study. The contribution of patient, tumor, and treatment characteristics to the survival prognosis was evaluated by machine learning using the following techniques: logistic regression, artificial neural network, XGBoost, support-vector clustering, random forest, and Gaussian Naive Bayes.
resultsIn the analysis of the data set, correlation analysis, and binary logistic regression analyses were applied. Of the 18 independent variables, 10 were found to be effective in predicting nasopharyngeal cancer-related mortality: age, weight loss, initial neutrophil/lymphocyte ratio, initial lactate dehydrogenase, initial hemoglobin, radiotherapy duration, tumor diameter, number of concurrent chemotherapy cycles, and T and N stages. Gaussian Naive Bayes was determined as the best algorithm to evaluate the prognosis of machine learning techniques (accuracy rate: 88%, area under the curve score: 0.91, confidence interval: 0.68-1, sensitivity: 75%, specificity: 100%).
conclusionMany factors affect prognosis in cancer, and machine learning algorithms can be used to determine which factors have a greater effect on survival prognosis, which then allows further research into these factors. In the current study, Gaussian Naive Bayes was identified as the best algorithm for the evaluation of prognosis of nasopharyngeal cancer.
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