Article ; Online: Application and subgroup analysis of competing risks model based on different lymph node staging systems in differentiated thyroid cancer.
2024
Abstract: Differentiated thyroid cancer (DTC) is the most common endocrine malignancy, with a rising incidence worldwide. Accurate prognostic models are essential for effective patient management. This study evaluates the prognostic value of various lymph node ... ...
Abstract | Differentiated thyroid cancer (DTC) is the most common endocrine malignancy, with a rising incidence worldwide. Accurate prognostic models are essential for effective patient management. This study evaluates the prognostic value of various lymph node staging systems in DTC using a competing risks model. We used SEER database records (1998-2016) of 16,527 DTC patients, analyzing N stage, positive lymph node numbers (PLNNs), metastatic lymph node ratio (MLNR), log odds of positive lymph nodes (LODDS), and log odds of the negative lymph node (NLN)/T stage ratio (LONT). Univariate and multivariate analyses in a competing risks model were performed, along with subgroup analyses based on demographic and clinical characteristics. In this study of 16,527 patients with DTC, different lymph node staging systems showed different prognostic correlations in univariate and multivariate analyses. In particular, PLNNs showed significant prognostic correlations in several subgroups. Additionally, PLNNs were more suitable as a lymph node staging system for DTC than LODDS and MLNR in N1 stage subgroups, with an optimal cut-off of 13. Receiver operating characteristic curves, calibration curves and nomograms improved the clinical utility of the prognostic model based on PLNNs. Using competing risks model and subgroup analyses, we found that PLNNs had the best prognostic discriminatory efficacy for patients with DTC, especially those with N1 stage disease, and had an optimal cut-off value of 13. |
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Language | English |
Publishing date | 2024-05-01 |
Publishing country | Italy |
Document type | Journal Article |
ZDB-ID | 2572692-4 |
ISSN | 2038-3312 ; 2038-131X |
ISSN (online) | 2038-3312 |
ISSN | 2038-131X |
DOI | 10.1007/s13304-024-01851-1 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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