Radioiodine refractoriness score: A multivariable prediction model for postoperative radioiodine‐refractory differentiated thyroid carcinomas.

Autor: Li, Genpeng, Lei, Jianyong, Song, Linlin, Jiang, Ke, Wei, Tao, Li, Zhihui, Gong, Rixiang, Zhu, Jingqiang
Předmět:
Zdroj: Cancer Medicine; Nov2018, Vol. 7 Issue 11, p5448-5456, 9p
Abstrakt: Objective: The purpose of the present study was to evaluate the clinical features of patients with radioiodine refractory (RAIR) differentiated thyroid carcinoma (DTC) and establish an effective risk score for postoperative radioiodine refractoriness. Subjects and methods: Data were retrospectively collected from 5163 patients admitted to our center after thyroid surgery. Radioiodine refractoriness was defined according to criteria used in the 2015 American Thyroid Association guidelines. The scoring system was established by independent risk factors identified by univariate and multivariate analyses. The optimal index points for predicting the prevalence of radioiodine refractoriness and the model discriminatory power were assessed by receiver operating characteristic (ROC) curves. Results: One hundred and twelve (2.2%) patients developed RAIR DTC. Smoking, tumor type (follicular thyroid cancer), extrathyroid extension, lymph node metastasis number (≥4), lymph node metastasis rate (≥53%), and pN stage (N1) were highly positively correlated with the prevalence of RAIR DTC. The cutoff value of seven points was found to be the best for predicting the prevalence of RAIR DTC, and the scoring system presented better discrimination than other single independent predictors. Conclusions: Based on our multivariable prediction model, patients with ≥7 index points may need to undergo more active surveillance or aggressive treatment due to the high risk of RAIR DTC. The radioiodine refractory differentiated thyroid carcinoma is associated with adverse prognosis and responsible for a large number of deaths. A multivariable prediction model was established, aiming to identify the potential patients, to optimize their management in the early stage. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index
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