Cervical lymph node metastasis in thyroid microcarcinoma

2019 ◽  
Author(s):  
Mouna Bellakhdhar ◽  
Jihene Houas ◽  
Monia Ghammem ◽  
Abir Meherzi ◽  
Wassim Kermani ◽  
...  
2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Xiaojuan Zheng ◽  
Yunwen Jiang ◽  
Chenyin Zhao ◽  
Minxia Peng ◽  
Liyong Qian

Purpose. To investigate the pathology and prognostic value of hyperechoic echo halo in cN0 papillary thyroid microcarcinoma (PTMC) and the relationship between age, gender, and the formation of abnormal hyperechoic echo halo and cervical lymph node metastasis. Data of 97 patients who underwent surgical treatment for the first time for single PTMC between April 2016 and March 2017 were analyzed retrospectively. The boundary status of the PTMC was determined preoperatively. Grayscale values of the nodular center, hyperechoic echo halo, and normal thyroid tissue were acquired with Adobe Photoshop CS6 software. The histopathology of the boundary and status of the cervical lymph node metastasis were analyzed. Formation of abnormal hyperechoic halo and cervical lymph node metastasis in relation to age and gender were explored. The abnormal hyperechoic halo mainly represents cancer cell infiltration with reactive hyperplasia of inflammatory cells and fibrous tissue. In the hyperechoic halo group, the grayscale values for the nodular center, hyperechoic echo halo, and normal thyroid tissue were 1552.6±578.6, 5792.0±747.6, and 3582.7±759.0, respectively (P<0.05). The cervical lymph node metastasis rate was significantly lower in patients with hyperechoic halo (15.0%) than in those without (41.6%; P<0.05) and significantly higher in those aged <45 years (53.3%) than in those aged ≥45 years (28.4%; P<0.05). There were no significant correlations between gender and cervical lymph node metastasis or between age, gender, and hyperechoic halo formation (P>0.05). cN0 PTMC patients with abnormal hyperechoic halo and age >45 years have a significantly reduced risk of cervical lymph node metastasis and relatively good prognosis.


2021 ◽  
Vol 12 ◽  
Author(s):  
Jinxiao Sun ◽  
Qi Jiang ◽  
Xian Wang ◽  
Wenhua Liu ◽  
Xin Wang

ObjectiveAccurate preoperative identification of cervical lymph node metastasis (CLNM) is essential for clinical management and established of different surgical protocol for patients with papillary thyroid microcarcinoma (PTMC). Herein, we aimed to develop an ultrasound (US) features and clinical characteristics-based nomogram for preoperative diagnosis of CLNM for PTMC.MethodOur study included 552 patients who were pathologically diagnosed with PTMC between January 2015 and June 2019. All patients underwent total thyroidectomy or lobectomy and divided into two groups: CLNM and non-CLNM. Univariate and multivariate analysis were performed to examine risk factors associated with CLNM. A nomogram comprising the prognostic model to predict the CLNM was established, and internal validation in the cohort was performed.ResultsCLNM and non-CLNM were observed in 216(39.1%) and 336(60.9%) cases, respectively. Seven variables of clinical and US features as potential predictors including male sex (odd ratio [OR] = 1.974, 95% confidence interval [CI], 1.243-2.774; P =0.004), age &lt; 45 years (OR = 4.621, 95% CI, 2.160-9.347; P &lt; 0.001), US-reported CLN status (OR = 1.894, 95% CI, 0.754-3.347; P =0.005), multifocality (OR = 1.793, 95% CI, 0.774-2.649; P =0.007), tumor size ≥ 0.6cm (OR = 1.731, 95% CI,0.793-3.852; P =0.018), ETE (OR = 3.772, 95% CI, 1.752-8.441;P&lt; 0.001) and microcalcification (OR = 2.316, 95% CI, 1.099-4.964; P &lt; 0.001) were taken into account. The predictive nomogram was established by involving all the factors above used for preoperative prediction of CLNM in patients with PTCM. The nomogram model showed an AUC of 0.839 and an accuracy of 77.9% in predicting CLNM. Furthermore, the calibration curve demonstrated a strong consistency between nomogram and clinical findings in prediction CLNM for PTMC.ConclusionsThe nomogram achieved promising results for predicting preoperative CLNM in PTMC by combining clinical and US risk factor. Our proposed prediction model is able to help determine an individual’s risk of CLNM in PTMC, thus facilitate reasonable therapy decision making.


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