ArticleFrontiers in endocrinology2022
A critical analysis of the current TNM classification for differentiated thyroid carcinoma in young patients: Time for a change?
Article in Frontiers in endocrinology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed, 5 citations in OpenAlex.
- Recursive partitioning analysis improves risk stratification in node-negative differentiated thyroid cancer: a retrospective cohort study.Gland surgery · 2026Article
- Molecular alteration patterns predict tumor behavior in papillary thyroid carcinoma independent of tumor size: insights from an international multicenter retrospective study.Thyroid research · 2025Article
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Authors and funding
3 authors at 2 institutions in 1 country.
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Abstract
Background: The current TNM classification that simply classifies differentiated thyroid carcinoma (DTC) patients younger than 55 years into stage I and stage II based on the presence or absence of distant metastases has been questioned. In this study, we reexamined the impact of T status and N status on prognosis and then developed a new prediction model to improve the predictability of cancer-specific survival (CSS) in young patients. Materials and methods: Kaplan-Meier method was applied to calculate the CSS. Multivariable Cox proportional hazards models were used to assess the impact of T status and N status on CSS after adjustment for known covariates. The area under the receiver operating characteristic curve (AUC), C-index, Bayesian information criterion (BIC), and Akaike information criterion (AIC) were calculated to compare model performance. Results: A total of 9,242 DTC patients younger than 55 years were enrolled in the study. After adjusting for gender, age at diagnosis, race, pathology subtype, N stage, and M stage, T3 disease [hazard ratio (HR): 3.78, Conclusions: This is the first study to question the rationality of the current TNM staging system for patients younger than 55 years and successfully develop a new prognostic model, which improves prognostic stratification and guides individualized management.
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