ArticleJournal of clinical medicine2023
A Random Forest Model for Post-Treatment Survival Prediction in Patients with Non-Squamous Cell Carcinoma of the Head and Neck.
Article in Journal of clinical medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed, 2 syntheses or guidelines pooled it, 8 citations in OpenAlex.
- The application of random forest-based models in prognostication of gastrointestinal tract malignancies: a systematic review.Frontiers in artificial intelligence · 2025Pooled it
- Prognosing post-treatment outcomes of head and neck cancer using structured data and machine learning: A systematic review.PloS one · 2024Pooled it
- Machine learning-based prediction model for postoperative complications in gastric and colorectal cancer: A prospective nationwide multi-center study.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2025Article
- Explainable machine learning predicts survival of retroperitoneal liposarcoma: A study based on the SEER database and external validation in China.Cancer medicine · 2024Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
backgroundCompared to squamous cell carcinoma, head and neck non-squamous cell carcinoma (HNnSCC) is rarer. Integrated survival prediction tools are lacking.
methods4458 patients of HNnSCC were collected from the SEER database. The endpoints were overall survivals (OSs) and disease-specific survivals (DSSs) of 3 and 5 years. Cases were stratified-randomly divided into the train & validation (70%) and test cohorts (30%). Tenfold cross validation was used in establishment of the model. The performance was evaluated with the test cohort by the receiver operating characteristic, calibration, and decision curves.
resultsThe prognostic factors found with multivariate analyses were used to establish the prediction model. The area under the curve (AUC) is 0.866 (95%CI: 0.844-0.888) for 3-year OS, 0.862 (95%CI: 0.842-0.882) for 5-year OS, 0.902 (95%CI: 0.888-0.916) for 3-year DSS, and 0.903 (95%CI: 0.881-0.925) for 5-year DSS. The net benefit of this model is greater than that of the traditional prediction methods. Among predictors, pathology, involved cervical nodes level, and tumor size are found contributing the most variance to the prediction. The model was then deployed online for easy use.
conclusionsThe present study incorporated the clinical, pathological, and therapeutic features comprehensively and established a clinically effective survival prediction model for post-treatment HNnSCC patients.
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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.