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Impact of Extranodal Extension on Risk Stratification in Papillary Thyroid Carcinoma.

ABSTRACT: Background: The current American Thyroid Association risk-stratification system for papillary thyroid carcinoma (PTC) incorporates the number and size of positive lymph nodes (LNs) but places less weight on extranodal extension (ENE). This study investigated how to incorporate ENE into the current system to predict recurrence better in PTC N1 patients. Methods: A total of 369 N1 PTC patients without distant metastasis were enrolled. The combination of number of positive LNs and LNs with ENE that had the highest C-index were identified with multivariable Cox proportional hazards models. ENE number was incorporated into the current system considering the recurrence rate and unadjusted and adjusted hazard ratios (HRs) of the subgroups. Kaplan-Meier curves for recurrence based on current and alternative systems were compared by log-rank test. Results: The recurrence rate for the subgroup with five or fewer positive LNs and one to three ENEs (7/61; 11.5%) was higher than that of the subgroup with five or fewer positive LNs without ENE (5/129; 3.9%; adjusted HR = 3.42 [confidence interval (CI) 0.99-11.75]; p = 0.050). In contrast, adjusted HRs of the subgroup with more than five positive LNs and one to three ENEs (2.33 [CI 0.52-10.35]) or with four or more ENEs (3.86 [CI 1.05-14.17]) were not higher than those of the subgroup with more than five LNs without ENE (4.47 [1.16-17.19]). Incorporating ENE into the current system as an intermediate-risk group yielded a lower log-rank p-value (0.05 vs. 0.01) than the current system. Conclusions: The presence of ENE in low volume LN metastasis confers an intermediate risk of recurrence. Incorporating ENE into the current system allows more accurate decisions regarding further management of PTC N1 patients.

SUBMITTER: Kim HI

PROVIDER: S-EPMC6648218 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Impact of Extranodal Extension on Risk Stratification in Papillary Thyroid Carcinoma.

Kim Hye In HI Hyeon Jiyeon J Park So Young SY Ahn Hyeon Seon HS Kim Kyunga K Han Ji Min JM Bae Ji Cheol JC Shin Jung Hee JH Kim Jee Soo JS Kim Sun Wook SW Chung Jae Hoon JH Kim Tae Hyuk TH Oh Young Lyun YL

Thyroid : official journal of the American Thyroid Association 20190530 7

Background: The current American Thyroid Association risk-stratification system for papillary thyroid carcinoma (PTC) incorporates the number and size of positive lymph nodes (LNs) but places less weight on extranodal extension (ENE). This study investigated how to incorporate ENE into the current system to predict recurrence better in PTC N1 patients. Methods: A total of 369 N1 PTC patients without distant metastasis were enrolled. The combination of number of positi ...[more]

PMID: 31025609

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Project description:BackgroundPapillary thyroid cancer (PTC) is one of the most common endocrine malignancies with different risk levels. However, preoperative risk assessment of PTC is still a challenge in the worldwide. Here, we first report a Preoperative Risk Assessment Classifier for PTC (PRAC-PTC) by multidimensional features including clinical indicators, immune indices, genetic feature, and proteomics.Materials and methodsThe 558 patients collected from June 2013 to November 2020 were allocated to three groups: discovery set (274 patients, 274 FFPE), retrospective test set (166 patients, 166 FFPE) and prospective test set (118 patients, 118 FNA). Proteomic profiling was conducted by formalin-fixed paraffin-embedded (FFPE) and fine-needle aspiration (FNA) tissues from the patients. Preoperative clinical information and blood immunological indices were collected. The BRAFV600E mutation were detected by the amplification refractory mutation system (ARMS).ResultsWe developed a machine learning model of 17 variables based on multidimensional features of 274 PTC patients from a retrospective cohort. The PRAC-PTC achieved areas under the curve (AUC) of 0.925 in the discovery set and validated externally by blinded analyses in a retrospective cohort of 166 PTC patients (0.787 AUC) and a prospective cohort of 118 PTC patients (0.799 AUC) from two independent clinical centres. Meanwhile, the preoperative predictive risk effectiveness of clinicians was improved with the assistance of PRAC-PTC, and the accuracies reached at 84.4% (95% CI 82.9-84.4) and 83.5% (95% CI 82.2-84.2) in the retrospective and prospective test sets, respectively.ConclusionThis study demonstrated that the PRAC-PTC that integrating clinical data, gene mutation information, immune indices, high-throughput proteomics and machine learning technology in multi-centre retrospective and prospective clinical cohorts can effectively stratify the preoperative risk of PTC and may decrease unnecessary surgery or overtreatment.

Dataset Information

Impact of Extranodal Extension on Risk Stratification in Papillary Thyroid Carcinoma.

Publications

Impact of Extranodal Extension on Risk Stratification in Papillary Thyroid Carcinoma.

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