ArticleBMC endocrine disorders2023
Comparison of the cox regression to machine learning in predicting the survival of anaplastic thyroid carcinoma.
Article in BMC endocrine disorders, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Deep learning survival analysis with time-varying covariates: extending PyCox to assess COVID-19 antiviral treatments and long-COVID associations.JAMIA open · 2026Article
- Article
- Machine Learning-Derived Risk Groups and Clinical Implementation of Survival Prediction in Lung Cancer: Evidence from a Kazakh National Cohort.Diagnostics (Basel, Switzerland) · 2026Article
- Prediction of First and Multiple Antiretroviral Therapy Interruptions in People Living With HIV: Comparative Survival Analysis Using Cox and Explainable Machine Learning Models.JMIR medical informatics · 2026Article
- Machine Learning Models for Disease-Free Survival Analysis after Liver Resection for Hepatocellular Carcinoma: A Multicentric French Collaborative Study.Liver cancer · 2026Article
- Dendrobine inhibits anaplastic thyroid cancer progression by targeting the JAK-STAT3 pathway.Frontiers in oncology · 2026Article
- Machine Learning Predictive Models for Survival in Patients with Brain Stroke.Health promotion perspectives · 2025Article
- Evaluation of Machine Learning and Traditional Statistical Models to Assess the Value of Stroke Genetic Liability for Prediction of Risk of Stroke Within the UK Biobank.Healthcare (Basel, Switzerland) · 2025Article
- Predicting overall survival in anaplastic thyroid cancer using machine learning approaches.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2025Article
- M2 macrophage infiltration drives tumor progression and identifies a multigene prognostic signature in esophageal cancer.Frontiers in immunology · 2025Article
- Prediction of Patients With Anaplastic Thyroid Carcinoma With Bone Metastasis: A Population-Based Study.International journal of endocrinology · 2025Article
- Movement Clearing Screens for Military Service Member Musculoskeletal Injury Risk Identification.Journal of athletic training · 2025Article
- Predictive study of machine learning combined with serum Neuregulin 4 levels for hyperthyroidism in type II diabetes mellitus.Frontiers in oncology · 2025Article
- Predicting Prognosis of Early-Stage Mycosis Fungoides with Utilization of Machine Learning.Life (Basel, Switzerland) · 2024Article
- An overview of the contemporary diagnosis and management approaches for anaplastic thyroid carcinoma.World journal of clinical oncology · 2024Article
- Update on current diagnosis and management of anaplastic thyroid carcinoma.World journal of clinical oncology · 2023Review
- Explainability of random survival forests in predicting conversion risk from mild cognitive impairment to Alzheimer's disease.Brain informatics · 2023Article
- Correction: Comparison of the cox regression to machine learning in predicting the survival of anaplastic thyroid carcinoma.BMC endocrine disorders · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
Abstract
backgroundTo compare the ability of the Cox regression and machine learning algorithms to predict the survival of patients with Anaplastic thyroid carcinoma (ATC).
methodsPatients diagnosed with ATC were extracted from the Surveillance, Epidemiology, and End Results database. The outcomes were overall survival (OS) and cancer-specific survival (CSS), divided into: (1) binary data: survival or not at 6 months and 1 year; (2): time-to-event data. The Cox regression method and machine learnings were used to construct models. Model performance was evaluated using the concordance index (C-index), brier score and calibration curves. The SHapley Additive exPlanations (SHAP) method was deployed to interpret the results of machine learning models.
resultsFor binary outcomes, the Logistic algorithm performed best in the prediction of 6-month OS, 12-month OS, 6-month CSS, and 12-month CSS (C-index = 0.790, 0.811, 0.775, 0.768). For time-event outcomes, traditional Cox regression exhibited good performances (OS: C-index = 0.713; CSS: C-index = 0.712). The DeepSurv algorithm performed the best in the training set (OS: C-index = 0.945; CSS: C-index = 0.834) but performs poorly in the verification set (OS: C-index = 0.658; CSS: C-index = 0.676). The brier score and calibration curve showed favorable consistency between the predicted and actual survival. The SHAP values was deployed to explain the best machine learning prediction model.
conclusionsCox regression and machine learning models combined with the SHAP method can predict the prognosis of ATC patients in clinical practice. However, due to the small sample size and lack of external validation, our findings should be interpreted with caution.
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