Lymph node ratio is superior to AJCC N stage for predicting recurrence in papillary thyroid carcinoma

Central lymph node ratio is an important recurrence prognostic factor for pediatric differentiated thyroid cancer

Background

The current risk stratification methods for Pediatric Differentiated Thyroid Carcinoma (DTC) are deemed inadequate due to the high recurrence rates observed in this demographic. This study investigates alternative clinicopathological factors, specifically the Central Lymph Node Ratio (CLNR), for improved risk stratification in pediatric DTC.

Methods

A retrospective review of 100 pediatric DTC patients, aged 19 or younger, treated between December 2012 and January 2021 at the First Affiliated Hospital of Guangxi Medical University was conducted. Clinicopathological variables were extracted, and univariate logistic regression identified factors correlated with recurrence. Kaplan-Meier (KM) survival analysis and subsequent statistical tests were used to assess the significance of these factors.

Results

The CLNR, with a cutoff value of 77.78%, emerged as a significant predictor of recurrence. Patients with a CLNR above this threshold had a 5.467 times higher risk of recurrence. The high CLNR group showed a higher proportion of male patients, clinically lymph node positivity (cN1), and extrathyroidal extension (ETE) compared to the low-risk group (p<0.05).

Conclusion

CLNR is a valuable predictor for recurrence in pediatric DTC and aids in stratifying patients based on Recurrence-Free Survival (RFS). For patients with a high CLNR, aggressive iodine-131 therapy, stringent TSH suppression, and proactive postoperative surveillance are recommended to mitigate recurrence risk and facilitate timely detection of recurrent lesions.

Biomarkers in Thyroid Cancer: Emerging Opportunities from Non-Coding RNAs and Mitochondrial Space

Thyroid cancer diagnosis primarily relies on imaging techniques and cytological analyses. In cases where the diagnosis is uncertain, the quantification of molecular markers has been incorporated after cytological examination. This approach helps physicians to make surgical decisions, estimate cancer aggressiveness, and monitor the response to treatments. Despite the availability of commercial molecular tests, their widespread use has been hindered in our experience due to cost constraints and variability between them. Thus, numerous groups are currently evaluating new molecular markers that ultimately will lead to improved diagnostic certainty, as well as better classification of prognosis and recurrence. In this review, we start reviewing the current preoperative testing methodologies, followed by a comprehensive review of emerging molecular markers. We focus on micro RNAs, long non-coding RNAs, and mitochondrial (mt) signatures, including mtDNA genes and circulating cell-free mtDNA. We envision that a robust set of molecular markers will complement the national and international clinical guides for proper assessment of the disease.

Development and validation of prediction models for papillary thyroid cancer structural recurrence using machine learning approaches

BACKGROUND

Although papillary thyroid cancer (PTC) patients are known to have an excellent prognosis, up to 30% of patients experience disease recurrence after initial treatment. Accurately predicting disease prognosis remains a challenge given that the predictive value of several predictors remains controversial. Thus, we investigated whether machine learning (ML) approaches based on comprehensive predictors can predict the risk of structural recurrence for PTC patients.

METHODS

A total of 2244 patients treated with thyroid surgery and radioiodine were included. Twenty-nine perioperative variables consisting of four dimensions (demographic characteristics and comorbidities, tumor-related variables, lymph node (LN)-related variables, and metabolic and inflammatory markers) were analyzed. We applied five ML algorithms-logistic regression (LR), support vector machine (SVM), extreme gradient boosting (XGBoost), random forest (RF), and neural network (NN)-to develop the models. The area under the receiver operating characteristic (AUC-ROC) curve, calibration curve, and variable importance were used to evaluate the models' performance.

RESULTS

During a median follow-up of 45.5 months, 179 patients (8.0%) experienced structural recurrence. The non-stimulated thyroglobulin, LN dissection, number of LNs dissected, lymph node metastasis ratio, N stage, comorbidity of hypertension, comorbidity of diabetes, body mass index, and low-density lipoprotein were used to develop the models. All models showed a greater AUC (AUC = 0.738 to 0.767) than did the ATA risk stratification (AUC = 0.620, DeLong test: P < 0.01). The SVM, XGBoost, and RF model showed greater sensitivity (0.568, 0.595, 0.676), specificity (0.903, 0.857, 0.784), accuracy (0.875, 0.835, 0.775), positive predictive value (PPV) (0.344, 0.272, 0.219), negative predictive value (NPV) (0.959, 0.959, 0.964), and F1 score (0.429, 0.373, 0.331) than did the ATA risk stratification (sensitivity = 0.432, specificity = 0.770, accuracy = 0.742, PPV = 0.144, NPV = 0.938, F1 score = 0.216). The RF model had generally consistent calibration compared with the other models. The Tg and the LNR were the top 2 important variables in all the models, the N stage was the top 5 important variables in all the models.

CONCLUSIONS

The RF model achieved the expected prediction performance with generally good discrimination, calibration and interpretability in this study. This study sheds light on the potential of ML approaches for improving the accuracy of risk stratification for PTC patients.

TRIAL REGISTRATION

Retrospectively registered at www.chictr.org.cn (trial registration number: ChiCTR2300075574, date of registration: 2023-09-08).

Risk-stratified Distant Metastatic Thyroid Cancer with Clinicopathological Factors and BRAF/TERT Promoter Mutations

OBJECTIVE

To assess the prognostic value of clinicopathological factors as well as BRAF and TERT promoter mutations in predicting distant metastasis in patients with papillary thyroid cancer.

DESIGN

The status of BRAF and TERTp mutations were available in 1,208 thyroid cancer patients who received thyroidectomy at Jiangyuan Hospital Affiliated to Jiangsu Institute of Nuclear Medicine from January 2008 to December 2021. Based on inclusion criteria, 99 distant metastasis thyroid cancers (DM-TCs) and 1055 patients without DM (Non-DM-TCs) were retrospectively reviewed.

RESULTS

After univariate and multivariate analyses, a risk model was established for DM prediction based on factors: T3/T4 stage, lymph node metastasis (LNM) number over 5, and BRAF/TERT mutations (TLBT). It was defined based on the number of TLBT factors: low risk (no risk factor, n=896), intermediate risk (1 risk factor, n=199), and high risk (≥2 risk factors, n=59). Notably, compared with patients with low and intermediate risks, patients assigned to high TLBT risk have a shorter time of DM disease-free survival. Except for gene mutation, other factors were also included in the 2015 American Thyroid Association (ATA) risk guideline. Comparing with the ATA risk category, this risk model showed a better performance in predicting DM-TCs.

CONCLUSIONS

This study proposes a TLBT risk classifier consisting of T3/T4 stages, LNM (n>5), and BRAF+TERTp mutations for predicting DM-TCs. TLBT risk stratification may help clinicians make personalized treatment management and follow-up strategies.

Prognostic impact of lymph node characteristics after therapeutic neck dissection for classic N1 papillary thyroid cancer

BACKGROUND

The impact of lymph node characteristics on mortality and recurrence remains controversial. This study evaluated the prognostic impact of lymph node characteristics in a large, homogenous cohort of patients with therapeutic neck dissection for clinically N1 classic papillary thyroid cancer (PTC).

METHODS

All consecutive adult patients with therapeutic central and lateral neck dissection for PTC at a French referral centre were prospectively enrolled from January 2000 until June 2021. The primary outcome was the impact of lymph node characteristics in predicting a disease event (persistence or recurrence), using univariable and multivariable logistic regression modelling.

RESULTS

A total of 462 patients were included. Lymph node capsular rupture was seen in 260 patients (56.3 per cent). Median maximum lymph node size was 15 (i.q.r. 9-23) mm. The median central, lateral, and total lymph node ratio (LNR) was 0.50 (i.q.r. 0.22-0.75), 0.15 (i.q.r. 0.07-0.29), and 0.26 (i.q.r. 0.14-0.41), respectively. After a median follow-up of 93 (i.q.r. 50-149) months, 182 (39.4 per cent) patients had a disease event. After multivariable analysis, the number of harvested lymph node >35 (OR 2.33 (95 per cent c.i. 1.10-4.95)), presence of lymph node capsular rupture (OR 1.92 (1.17-3.14)), and total LNR >0.20 (OR 2.37 (1.08-5.19)) and >0.40 (OR 4.92 (1.61-15.03)) predicted a disease event. An LNR of 0.20 predicted a disease event with a sensitivity of 80.8 per cent and a specificity of 50.4 per cent.

CONCLUSION

Disease persistence or recurrence after thyroidectomy with therapeutic neck dissection for classic PTC with preoperative nodal disease appears to depend on number of harvested lymph node, presence of lymph node capsular rupture, and total LNR.

Nomogram to predict the risk of biochemical recurrence and structural recurrence in patients with stage cN1 papillary thyroid carcinoma

INTRODUCTION

Although papillary thyroid carcinoma (PTC) is thought to be the least aggressive thyroid cancer, it has a significant recurrence rate. Therefore, we aimed to develop a nomogram to estimate the probability of biochemical recurrence (BIR) and structural recurrence (STR) in patients with stage cN1 PTC.

METHODS

We studied the relationship between the characteristics of patients with stage N1a PTC and the risk of recurrence by analysing the data of 617 inpatients (training cohort) and 102 outpatients (validation cohort) in our hospital. We used the least absolute shrinkage and selection operator regression model to identify prognostic indicators to construct nomograms to predict the risk of BIR and STR.

RESULTS

There were 94 (15.24%) BIR cases in the training cohort and 36 (35.29%) in the validation cohort. There were 31 (5.02%) STR cases in the training cohort and 23 (22.55%) cases in the validation cohort. The variables included in the BIR nomogram were sex, age at diagnosis, tumour size, extrathyroidal infiltration, and lymph node ratio (LNR). While the variables included in the STR nomogram were tumour size, extrathyroidal infiltration, BRAF state, metastatic lymph nodes, and LNR. Both the prediction models demonstrated good discrimination ability. The results showed the calibration curve of the nomogram was near the optimum diagonal line, and the decision curve analysis showed a noticeably better benefit.

CONCLUSION

The LNR may be a valid prognostic indicator for patients with stage cN1 PTC. The nomograms could help clinicians identify high-risk patients and choose the best postsurgical therapy and monitoring.

Assessing the role of central lymph node ratio in predicting recurrence in N1a low-to-intermediate risk papillary thyroid carcinoma

Introduction

Lymph node metastasis in patients with papillary thyroid carcinoma (PTC) is associated with postoperative recurrence. Recently, most studies have focused on the evaluation of recurrence in patients with late-stage PTC, with limited data on those with early-stage PTC. We aimed to assess the relationship between lymph node ratio (LNR) and recurrence in low-to-intermediate-risk patients and validate its diagnostic efficiency in both structural (STR) and biochemical recurrence (BIR).

Methods

Clinical data of patients with PTC diagnosed at the Affiliated Hospital of Jining Medical University were retrospectively collected. The optimal LNR cut-off values for disease-free survival (DFS) were determined using X-tile software. Predictors were validated using univariate and multivariate Cox regression analyses.

Results

LNR had a higher diagnostic effectiveness than metastatic lymph nodes in patients with low-to-intermediate recurrence risk N1a PTC. The optimal LNR cutoff values for STR and BIR were 0.75 and 0.80, respectively. Multivariate Cox regression analysis showed that LNR≥0.75 and LNR≥0.80 were independent factors for STR and BIR, respectively. The 5-year DFS was 90.5% in the high LNR (≥0.75) and 96.8% in low LNR (<0.75) groups for STR. Regarding BIR, the 5-year DFS was 75.7% in the high LNR (≥0.80) and 86.9% in low LNR (<0.80) groups. The high and low LNR survival curves exhibited significant differences on the log-rank test.

Conclusion

LNR was associated with recurrence in patients with low-to-intermediate recurrence risk N1a PTC. We recommend those with LNR≥0.75 require a comprehensive evaluation of lateral neck lymphadenopathy and consideration for lateral neck dissection and RAI treatment.

Analysis of factors associated with the prognosis of papillary thyroid cancer and the construction of a survival model

OBJECTIVE

To study the survival prediction value of lymph node ratio (LNR) and preoperative thyroglobulin (Tg) in the prognosis of thyroid papillary carcinoma (PTC).

METHODS

A total of 495 patients with PTC and lymph node metastasis treated at the Cancer Hospital of Xinjiang Medical University were selected for a retrospective study. The disease-free survival (DFS) of patients was the follow-up endpoint. DFS was calculated for all patients. The Cox proportional risk regression model and nomogram were used to predict the survival prognosis of PTC with lymph node metastasis by index. LNR and preoperative Tg level cutoff values were obtained using ROC curves. To express DFS, Kaplan-Meier survival curves were created. Using 3- and 5-year calibration curves and AUC values, the prognostic models' precision and discrimination were assessed. Clinical decision curve analysis was used to forecast clinical benefitability. Finally, the results were validated using internal cross-validation.

RESULTS

The cutoff values of LNR and preoperative Tg level were 0.295 and 50.24, respectively, and they were divided into two groups according to the cutoff values. Multifactorial Cox regression models showed that NLNM, LNR, and preoperative Tg level (all p < 0.05) were independent risk factors affecting the prognosis of PTC with lymph node metastasis. Kaplan-Meier curves showed higher DFS rates in the group with low NLNM (<10), LNR (<0.295), and preoperative Tg level (<50.24) groups. The 3-year and 5-year calibration curves showed good agreement. A ROC curve analysis was performed on the nomogram model, and its AUC values at 3 and 5 years were, respectively, 0.805 and 0.793. Clinical decision curves indicate good clinical benefit. Finally, internal cross-validation demonstrated the legitimacy of the prognostic model.

CONCLUSION

The LNR and preoperative Tg levels, in combination with other independent factors, were effective in predicting the survival prognosis for patients with PTC.

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

Papillary thyroid microcarcinoma (PTMC) typically has an indolent course and excellent prognosis. Nonetheless, a subset of PTMC carries a risk of lymph node metastasis (LNM) and local recurrence. PTC from the Middle Eastern population is unique with respect to demographic and clinico-pathological characteristics as compared to other ethnicities of the world. The risk factors of LNM in PTMC patients of Middle Eastern ethnicity have not been fully explored. The present study aims to investigate the influencing factors of LNM in Middle Eastern PTMC patients and its predictive impact on patient’s outcome. A total of 226 confirmed PTMC cases were selected in this retrospective study. The correlation between clinico-pathological, as well as molecular, characteristics and LNM was evaluated. Multivariate analysis was performed by logistic regression and Cox proportional hazards models. Among the 226 patients, the rate of LNM was 43.8% (99/226). Bilaterality, multifocality, gross extrathyroidal extension (ETE), and intermediate-to-high American Thyroid Association (ATA) risk tumors were significantly associated with LNM in PTMC. Multivariate logistic regression analysis showed that bilaterality and gross ETE were independent predictive factors for LNM in PTMC. The recurrence-free survival (RFS) was shorter in PTMC with LNM compared to those without LNM (p = 0.0051) and was significant on multivariate analysis. In conclusion, our study showed that bilaterality and gross ETE were independent influencing factors of LNM in Saudi patients with PTMC. LNM was also associated with shorter RFS. The identification of risk factors for LNM in patients of Middle Eastern ethnicity could help the individualization of clinical management for PTMC patients.

Central Lymph Node Ratio Predicts Recurrence in Patients with N1b Papillary Thyroid Carcinoma

Simple Summary

The lymph node ratio (LNR) is an emerging predictive marker for recurrence in papillary thyroid carcinoma (PTC). The purpose of this study was to investigate the association between LNR and disease-free survival (DFS) in patients with N1b PTC. Unlike that in the lateral or whole neck, LNR in the central compartment (CLNR) was found to have prognostic significance. The high-CLNR group (CLNR ≥ 0.7) had worse DFS and was 4.5 times more likely to experience recurrence in patients with N1b PTC.

Abstract

The lymph node ratio (LNR) indicates the number of metastatic lymph nodes (LNs) to the total number of LNs. The prognostic value of LNR in papillary thyroid carcinoma (PTC) and other solid tumors is known. This study aimed to investigate the relationship between LNR and disease-free survival (DFS) in patients with PTC with lateral LN metastases (N1b PTC). A total of 307 patients with N1b PTC who underwent total thyroidectomy and therapeutic central and lateral LN dissection were retrospectively analyzed. The DFS and recurrence risk in the patients with LNR, central-compartment LNR (CLNR), and lateral-compartment LNR (LLNR) were compared. The mean follow-up duration was 93.6 ± 19.9 months. Eleven (3.6%) patients experienced recurrence. Neither LNR nor LLNR affected the recurrence rate in our analysis (p = 0.058, p = 0.106, respectively). However, there was a significant difference in the recurrence rates between the patients with low and high CLNR (2.1% vs. 8.8%, p = 0.017). In the multivariate analysis, CLNR ≥ 0.7 and perineural invasion were independent predictors of tumor recurrence. High CLNR was associated with an increased risk of recurrence, and was shown to be a significant predictor of prognosis in patients with N1b PTC.