Impact of Multifocality on the Recurrence of Papillary Thyroid Carcinoma

Woo, Joo Hyun; Kim, Hyeonkyeong; Kwon, Hyungju

doi:10.3390/jcm10215144

Cited by 22 publications

(23 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…All of the 23 studies were hospital‐based studies. Two articles 18,31 only reported papillary thyroid microcarcinomas (in which the maximal diameters were less than 1 cm) and the rest reported PTC patients 14,22,32–49 . The eligible studies were retrospective cohorts.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Clinical outcomes of multifocal papillary thyroid cancer: A systematic review and meta‐analysis

Cui

Feng

Zhu

et al. 2022

Laryngoscope Investig Oto

View full text Add to dashboard Cite

Objective: Papillary thyroid cancer (PTC) is the most common endocrine malignancy with a steadily increasing incidence. Researches have reported that tumor multifocality occurs in an extensive number of cases. Nevertheless, the clinical characteristics and prognostic value remained controversial. This study was performed to investigate the relationship between multifocal PTC and adverse clinicopathologic features and the prognosis.Methods: A systematic review and meta-analysis were conducted based on three electronic databases up to December 31, 2021. Parameters of interest included five clinical features (extrathyroidal extension, lymphovascular invasion, central lymph node metastasis, lateral lymph node metastasis, distant metastasis) and were pooled into risk ratios (RRs). Time-to-event data (recurrence-free survival and all-cause mortality) were evaluated using hazard ratios (HRs). Publication bias was examined using funnel plots and Egger's test.Results: A total of 23 articles were included according to the inclusion criteria; all of the studies were retrospective cohorts. In comparison with unifocality, multifocality showed an increased risk of extrathyroidal extension (

show abstract

Section: Resultsmentioning

confidence: 99%

“…We used a fixed effect model to evaluate the risk of multifocality for lateral lymph node metastasis (LLNM) among four studies 33,40,48,49 . The analysis showed that there was a significant association between tumor multifocality and LLNM (RR 1.86, 95% CI 1.62–2.14, p < .001) (Figures 2 and 4B).…”

Section: Resultsmentioning

confidence: 99%

Clinical outcomes of multifocal papillary thyroid cancer: A systematic review and meta‐analysis

Cui

Feng

Zhu

et al. 2022

Laryngoscope Investig Oto

View full text Add to dashboard Cite

show abstract

“…In the present study, compared with the thyroid lobectomy group, the total thyroidectomy group was also found to have a larger tumor size, a higher rate of microscopic ETE, and an increased risk of LN metastasis. As these high-risk features could affect the development of recurrence, propensity score matching was performed to minimize potential biases [ 21 ]. After adjusting for possible confounding factors, including tumor size, microscopic ETE, LN metastasis, and coexisting Hashimoto thyroiditis, our matched cohorts showed no difference in RFS between the total thyroidectomy and lobectomy groups.…”

Section: Discussionmentioning

confidence: 99%

“…The comparison of dichotomous data was performed by Pearson chi-squared tests. To minimize potential confounding effects and selection bias, we performed 3:1 propensity score matching [ 21 ]. We selected 4 factors that could affect the recurrence as follows: tumor size, microscopic ETE, LN metastasis, and coexisting Hashimoto thyroiditis.…”

Section: Methodsmentioning

confidence: 99%

Optimal Surgical Extent in Patients with Unilateral Multifocal Papillary Thyroid Carcinoma

Woo

Kwon

2022

Cancers

Self Cite

View full text Add to dashboard Cite

Multifocality increases the risk of recurrence in patients with papillary thyroid carcinoma (PTC); however, it is unclear whether multifocality justifies more extensive or aggressive surgical treatment. Here, we evaluated the effect of the operative extent on the recurrence-free survival (RFS) of patients with multifocal PTC. Between 2010 and 2019, 718 patients with unilateral multifocal PTC were enrolled; 115 patients (16.0%) underwent ipsilateral thyroid lobectomy, and 606 patients (84.0%) underwent total thyroidectomy. With a mean follow up of 5.2 years, RFS was comparable between the total thyroidectomy and lobectomy groups (p = 0.647) after adjusting for potential confounders. Multivariable Cox regression analysis also demonstrated that the operative extent was not an independent predictor of recurrence (HR 1.686, 95% CI: 0.321–8.852). Subgroup analyses further indicated that both total thyroidectomy and thyroid lobectomy resulted in comparable RFS for multifocal PTC patients with other high-risk factors, including tumor size > 1 cm (p = 0.711), lymph node metastasis (p = 0.536), and intermediate ATA risk of recurrence (p = 0.682). In conclusion, thyroid lobectomy was not associated with the risk of recurrence in patients with multifocal PTCs. Multifocality in PTC may not always require aggressive surgery.

show abstract

“…TC ( 7 ), gender ( 8 ), TPO-Ab ( 9 ), Tg-Ab ( 10 ), CR, and TG all have involvement in thyroid diseases. For example, males have a higher risk of recurrence in patients with papillary thyroid carcinoma than females ( 11 ). Another study evaluated the associations between thyroid antibodies (TPO-Ab and Tg-Ab) and the risk of preterm delivery ( 12 ).…”

Section: Discussionmentioning

confidence: 99%

Predicting Elevated TSH Levels in the Physical Examination Population With a Machine Learning Model

Cheng

Deng

et al. 2022

Front. Endocrinol.

View full text Add to dashboard Cite

ObjectiveThe purpose of this study was to predict elevated TSH levels by developing an effective machine learning model based on large-scale physical examination results.MethodsSubjects who underwent general physical examinations from January 2015 to December 2019 were enrolled in this study. A total of 21 clinical parameters were analyzed, including six demographic parameters (sex, age, etc.) and 15 laboratory parameters (thyroid peroxidase antibody (TPO-Ab), thyroglobulin antibody (TG-Ab), etc.). The risk factors for elevated TSH levels in the univariate and multivariate Logistic analyses were used to construct machine learning models. Four machine learning models were trained to predict the outcome of elevated TSH levels one year/two years after patient enrollment, including decision tree (DT), linear regression (LR), eXtreme Gradient boosting (XGBoost), and support vector machine (SVM). Feature importance was calculated in the machine learning models to show which parameter plays a vital role in predicting elevated TSH levels.ResultsA total of 12,735 individuals were enrolled in this study. Univariate and multivariate Logistic regression analyses showed that elevated TSH levels were significantly correlated with gender, FT3/FT4, total cholesterol (TC), TPO-Ab, Tg-Ab, creatinine (Cr), and triglycerides (TG). Among the four machine learning models, XGBoost performed best in the one-year task of predicting elevated TSH levels (AUC (0.87(+/- 0.03))). The most critical feature in this model was FT3/FT4, followed by TPO-Ab and other clinical parameters. In the two-year task of predicting TSH levels, none of the four models performed well.ConclusionsIn this study, we trained an effective XGBoost model for predicting elevated TSH levels one year after patient enrollment. The measurement of FT3 and FT4 could provide an early warning of elevated TSH levels to prevent relative thyroid diseases.

show abstract

Impact of Multifocality on the Recurrence of Papillary Thyroid Carcinoma

Cited by 22 publications

References 32 publications

Clinical outcomes of multifocal papillary thyroid cancer: A systematic review and meta‐analysis

Clinical outcomes of multifocal papillary thyroid cancer: A systematic review and meta‐analysis

Optimal Surgical Extent in Patients with Unilateral Multifocal Papillary Thyroid Carcinoma

Predicting Elevated TSH Levels in the Physical Examination Population With a Machine Learning Model

Contact Info

Product

Resources

About