ArticleJournal of personalized medicine2021
Progression-Free Survival Prediction in Patients with Nasopharyngeal Carcinoma after Intensity-Modulated Radiotherapy: Machine Learning vs. Traditional Statistics.
Article in Journal of personalized medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prognosing post-treatment outcomes of head and neck cancer using structured data and machine learning: A systematic review.PloS one · 2024Pooled it
- Development of machine learning models for survival prediction in nasopharyngeal carcinoma using population-based data.Discover oncology · 2025Article
- Machine learning model for predicting recurrence following intensity-modulated radiation therapy in nasopharyngeal carcinoma.World journal of surgical oncology · 2025Article
- Machine learning explainability in nasopharyngeal cancer survival using LIME and SHAP.Scientific reports · 2023Article
- Predicting the survival of patients with pancreatic neuroendocrine neoplasms using deep learning: A study based on Surveillance, Epidemiology, and End Results database.Cancer medicine · 2023Article
- Survival rate prediction of nasopharyngeal carcinoma patients based on MRI and gene expression using a deep neural network.Cancer science · 2023Article
- Roundabout Guidance Receptor 1 Is an Emerging Prognostic Biomarker for Nasopharyngeal Carcinoma.Clinical Medicine Insights. Oncology · 2022Article
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Authors and funding
12 authors.
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Abstract
backgroundThe Cox proportional hazards (CPH) model is the most commonly used statistical method for nasopharyngeal carcinoma (NPC) prognostication. Recently, machine learning (ML) models are increasingly adopted for this purpose. However, only a few studies have compared the performances between CPH and ML models. This study aimed at comparing CPH with two state-of-the-art ML algorithms, namely, conditional survival forest (CSF) and DeepSurv for disease progression prediction in NPC.
methodsFrom January 2010 to March 2013, 412 eligible NPC patients were reviewed. The entire dataset was split into training cohort and testing cohort in a ratio of 90%:10%. Ten features from patient-related, disease-related, and treatment-related data were used to train the models for progression-free survival (PFS) prediction. The model performance was compared using the concordance index (c-index), Brier score, and log-rank test based on the risk stratification results.
resultsDeepSurv (c-index = 0.68, Brier score = 0.13, log-rank test
conclusionsBoth CSF and DeepSurv outperformed CPH in our relatively small dataset. ML-based survival prediction may guide physicians in choosing the most suitable treatment strategy for NPC patients.
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