Risk factors of papillary thyroid microcarcinoma that predispose patients to local recurrence

Abstract

Background Currently, less aggressive treatment or even active surveillance of papillary thyroid microcarcinoma (PTMC) is widely accepted and recommended as a therapeutic management option. However, there are some concerns about these approaches. We investigated whether there are any demographic, clinical and ultrasound characteristics of PTMC patients that are easy to obtain and clinically available before surgery to help clinicians make proper therapeutic decisions. Methods We performed a retrospective chart review of 5,021 patients with thyroid tumors surgically treated in one center in 2008–2018. Finally, 182 (3.62%) PTMC patients were selected (158 (86.8%) females and 24 (13.2%) males, mean age 48.8±15.4 years). We analyzed the disease-free survival (DFS) time of the PTMC patients according to demographic and histopathological parameters. Univariate and multivariate logistic regression analyses were used to assess the relationships of demographic, clinical and ultrasound characteristics with aggressive histopathological features. Results Age ≥55 years, hypoechogenicity, microcalcifications, irregular tumor shape, smooth margins and high vascularity significantly increased the risk for minimal extrathyroidal extension (minETE), lymph node metastasis (LNM), and capsular and vascular invasion (p<0.0001). Multivariate logistic regression analysis demonstrated a statistically significant risk of LNM (OR = 5.98, 95% CI: 2.32–15.38, p = 0.0002) and trends toward significantly higher rates of minETE and capsular and vascular invasion (OR = 2.24, 95% CI: 0.97–5.19, p = 0.056) in patients ≥55 years than in their younger counterparts. The DFS time was significantly shorter in patients ≥55 years (p = 0.015), patients with minETE and capsular and vascular invasion (p = 0.001 for all), patients with tumor size >5 mm (p = 0.021), and patients with LNM (p = 0.002). Conclusions The absence of microcalcifications, irregular tumor shape, blunt margins, hypoechogenicity and high vascularity in PTMC patients below 55 years and with tumor diameters below 5 mm may allow clinicians to select individuals with a low risk of local recurrence so that they can receive less aggressive management.

Risk Factors for Cervical Lymph Node Metastasis in Middle Eastern Papillary Thyroid Microcarcinoma

“…Furthermore, the rate of LNM varies in frequency from one study to another, ranging between 14% to 40% [ 21 , 22 , 23 ]. Chow et al [ 24 ] and others [ 25 , 26 ] have also documented the negative impact of LNM on recurrence of PTMC patients. However, at present, there are limited studies on the risk factors for LNM in PTMC in Middle Eastern populations.…”

Risk Factors of Central Lymph Node Metastasis in Papillary Thyroid Microcarcinoma and the Value of Sentinel Lymph Node Biopsy

“…Additionally, of the patients with tumor size larger than 5 mm, the rate of capsular invasion was 23% significantly higher than that of patients with tumor size of 5 mm and below ( P = 0.001). A currently retrospective chart review of 182 PTMC patients found that minimal extrathyroidal extension (ETE) rate was as high as 37.9% (69/182) ( 51 ). Some other researchers have divided PTMCs into two separate groups.…”

A Nomogram Based on Clinicopathological and Ultrasound Imaging Characteristics for Predicting Cervical Lymph Node Metastasis in cN0 Unilateral Papillary Thyroid Microcarcinoma

“…However, the study of Choi et al ( 16 ) put forward a point of view that, due to clinically apparent lymph node metastasis, AS rarely leads to favorable oncological outcomes. It is highly likely that lymph node metastasis will eventually lead to local recurrence and secondary operations, which greatly increase the risk of hypoparathyroidism and RLN injury during surgery and reduce the quality of life of the patient ( 17 , 18 ). Therefore, it is necessary to precisely stratify patients with PTMC and evaluate the risk of CLNM in these patients; hence, several researchers have attempted to develop optimal predictive models.…”