Management of cN0 papillary thyroid microcarcinoma patients according to risk-scoring model for central lymph node metastasis and predictors of recurrence

A lasso-based model to predict lateral lymph node metastasis in unifocal papillary thyroid carcinoma with central lymph node metastasis

OBJECTIVE

To screen the risk factors for lateral lymph node metastasis (LLNM) in unifocal papillary thyroid carcinoma (PTC) with central lymph node metastasis (CLNM) and create a corresponding model.

METHODS

A retrospective analysis of 362 patients from our hospital was performed. All patients were randomized into training and validation groups in a ratio of 7:3. Risk factors were screened using the least absolute shrinkage and selection operator (LASSO) and logistic regression analysis.

RESULTS

The analysis indicated that upper location, number of CLNM ≥ 3, rate of CLNM ≥ 0.172, prelaryngeal LNM, pretracheal LNM, and tall cell variant of papillary thyroid carcinoma (TCV-PTC) are independent risk factors. Visualizing the model with a nomogram, receiver operating characteristic (ROC) curves revealed an area under the curve (AUC) of 0.773 for the training group and 0.779 for the validation group. This confirms the stability and outstanding accuracy of the model. Also, the calibration curves and clinical descision curves reflect strong calibration,offering potential clinical benefits.

CONCLUSIONS

The risk factors for LLNM include metastasis to the prelaryngeal lymph nodes, metastasis to the pretracheal lymph nodes, location in the upper level, number of metastases ≥3 in CLNM, TCV-PTC and metastasis rate ≥0.172. A nomogram incorporating these factors exhibits excellent predictive value and stability.

Ultrasound and clinical factors predicting central lymph node metastases in patients with unilateral multifocal papillary thyroid carcinoma

OBJECTIVE

This retrospective study involving a large dataset of unilateral multifocal papillary thyroid carcinoma (UM-PTC) sought to identify factors that predict central lymph node metastases (CLNM) in patients.

METHODS

We identified a cohort of 158 patients who underwent cervical ultrasonography followed by UM-PTC diagnosis based on postoperative pathology. The relationship between CLNM and UM-PTC clinical ultrasound features was evaluated using univariate and multivariate analyses. Receiver operating characteristic (ROC) curve analysis was used to determine the ability of total tumor diameter (TTD) to predict CLNM.

RESULTS

Among the 158 UM-PTC patients, the incidence of CLNM was 29.7% (47/158). Univariate and multivariate analyses revealed that a number of similarity of sonographic features (NSSF) ≥4 (odds ratio [OR] = 11.335, 95% confidence interval [CI]: 3.95-32.50, p = 0.000), microcalcifications (OR = 3.54, 95% CI: 1.30-9.70, p = 0.014), a TTD of ≥2 cm (OR = 4.48, 95% CI: 1.62-12.34, p = 0.004), number of nodules ≥3 (OR = 13.17, 95% CI: 3.24-53.52, p = 0.000), and Lateral cervical lymph node metastasis (LLNM) (OR = 5.57, 95% CI: 1.59-19.48, p = 0.007) were independently associated with CLNM in UM-PTC. ROC curve analysis revealed that the TTD cut-off of 1.795 cm had a sensitivity of 0.723 and a specificity of 0.676 for predicting CLNM.

CONCLUSIONS

Patients with UM-PTC are at high risk of CLNM. NSSF ≥4, microcalcifications, TTD of ≥2 cm, LLNM, and a number of nodules ≥3 were independently associated with CLNM. Our data show that ultrasound may guide surgical decisions in the treatment of UM-PTC.

Development and validation of a multidimensional machine learning-based nomogram for predicting central lymph node metastasis in papillary thyroid microcarcinoma

Background

Papillary thyroid microcarcinoma (PTMC), a subset of papillary thyroid carcinoma (PTC), is characterized by tumors ≤10 mm in size. While generally indolent, central lymph node metastasis (CLNM) is associated with higher risks of recurrence and distant metastasis. Existing prediction models for CLNM predominantly depend on isolated clinical or imaging parameters, failing to integrate multidimensional predictors such as clinicopathological, ultrasonographic, and serological features. This limitation significantly undermines their clinical applicability. Therefore, we developed a machine learning-based nomogram that integrates comprehensive predictors to enhance preoperative risk stratification and facilitate personalized surgical decision-making.

Methods

A retrospective study was conducted on 503 PTMC patients who underwent thyroidectomy in Liaoyang Central Hospital between 2020 and 2023. Patients were randomly divided into training (n=352) and validation (n=151) cohorts. Inclusion criteria required preoperative imaging to confirm no cervical lymph node metastasis (LNM), complete clinicopathologic data, and initial surgery with central lymph node dissection, as well as postoperative pathology confirming PTC. Multidimensional predictors (clinical demographics, ultrasonographic features, serological markers, and histopathological characteristics) were analyzed. CLNM was definitively diagnosed via postoperative histopathology. Least absolute shrinkage and selection operator (LASSO) regression was used to identify key predictors, which were incorporated into a logistic regression model. The model's performance was evaluated using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA).

Results

Among 503 enrolled patients (mean age: 48.5 years; male: 24%, female: 76%), CLNM was pathology confirmed in 28.8% (145/503). Age, gender, tumor size, tumor location, and extrathyroidal extension (ETE) were identified as independent predictors of CLNM. The nomogram achieved an area under the curve (AUC) of 0.88 (sensitivity 0.84, specificity 0.76) in the training cohort and 0.78 (sensitivity 0.80, specificity 0.70) in the validation cohort. Calibration plots indicated excellent agreement between predicted and observed probabilities, with mean absolute errors below 0.05. DCA demonstrated clinical utility for threshold probabilities ranging from 15% to 88%. These results suggest that the nomogram has good predictive performance and clinical applicability in assessing the risk of CLNM in PTMC patients.

Conclusions

This Machine learning-based predictive nomogram provides a reliable tool for assessing CLNM risk in PTMC patients, supporting personalized surgical strategies. Further validation in external cohorts is required to confirm its generalizability.

The impact of age at diagnosis on central lymph node metastasis in clinically low-risk papillary thyroid microcarcinoma patients

BACKGROUND

Age is an independent risk factor for central lymph node metastasis (CLNM) in clinically negative lymph node (cN0) papillary thyroid microcarcinoma (PTMC) patients. The objective of this study was to investigate the impact of age on CLNM in clinically low-risk PTMC patients.

METHODS

A retrospective analysis was performed on patients with clinically low-risk PTMC who underwent surgery between January 2016 and December 2018. Logistic regression analysis was used to examine the impact of age on the risk of CLNM. The associations between age and pN1a and the lymph node ratio (LNR) were examined by a restricted cubic spline (RCS) curve with logistic regression models.

RESULTS

A total of 1352 patients (mean [range] age, 43[18-76] years; 325 males [24.0%]) were enrolled in this study. Logistic regression analysis revealed that age was a significant factor influencing the risk of CLNM (OR 0.95, 95% CI 0.94-0.96; p < 0.001). The RCS curve revealed a significant nonlinear association between age and pN1a status and the LNR. For patients under the age of 55, the risk of CLNM (OR 0.59, 95% CI 0.55-0.65, p < 0.001) and the LNR (beta - 0.23, 95% CI -0.27, -0.19, p < 0.001) significantly decreased as age increased. For patients aged ≥ 55 years, the risk of LNM (OR 1.03, 95% CI 0.81-1.32; p = 0.79) and the LNR (Beta - 0.03, 95% CI -0.07,0.13, p = 0.54) did not change with age.

CONCLUSIONS

This study confirmed that age was a significant factor influencing the risk and severity of CLNM in patients with low-risk PTMC. The risk and severity of LNM were lowest in patients aged ≥ 55 years.

Meta-analysis of prediction models for predicting lymph node metastasis in thyroid cancer

BACKGROUND

The purpose of this systematic review and meta-analysis is to assess the efficacy of various machine learning (ML) techniques in predicting preoperative lymph node metastasis (LNM) in patients diagnosed with papillary thyroid carcinoma (PTC). Although prior studies have investigated the potential of ML in this context, the current evidence is not sufficiently strong. Hence, we undertook a thorough analysis to ascertain the predictive accuracy of different ML models and their practical relevance in predicting preoperative LNM in PTC patients.

MATERIALS AND METHODS

In our search, we thoroughly examined PubMed, Cochrane Library, Embase, and Web of Science, encompassing their complete database history until December 3rd, 2022. To evaluate the potential bias in the machine learning models documented in the included studies, we employed the Prediction Model Risk of Bias Assessment Tool (PROBAST).

RESULTS

A total of 107 studies, involving 136,245 patients, were included. Among them, 21,231 patients showed central LNM (CLNM) and 4,637 had lateral LNM (LLNM). The meta-analysis results revealed that the c-index for predicting LNM, CLNM, and LLNM were 0.762 (95% CI: 0.747-0.777), 0.762 (95% CI: 0.747-0.777), and 0.803 (95% CI: 0.773-0.834) in the training set, and 0.773 (95% CI: 0.754-0.791), 0.762 (95% CI: 0.747-0.777), and 0.829 (95% CI: 0.779-0.879) in the validation set, respectively. A total of 134 machine learning-based prediction models were included, covering 10 different types. Logistic Regression (LR) was the most commonly used model, accounting for 81.34% (109/134) of the included models.

CONCLUSIONS

Machine learning methods have shown a certain level of accuracy in predicting preoperative LNM in PTC patients, indicating their potential as a predictive tool. Their use in the clinical management of PTC holds great promise. Among the various ML models investigated, the performance of logistic regression-based nomograms was deemed satisfactory.

Risk factors for cervical lymph node metastasis of papillary thyroid cancer in elderly patients aged 65 and older

Objective

To assess the risk factors of cervical lymph node metastasis in elderly patients aged 65 years and older diagnosed with papillary thyroid cancer (PTC).

Design and method

In this retrospective analysis, we included a total of 328 elderly patients aged 65 years and older diagnosed with PTC. We thoroughly examined clinical features from these patients. Utilizing univariate and multivariate logistic regression analyses, we aimed to identify factors contributing to the risk of central and lateral lymph node metastasis (CLNM/LLNM) in this specific population of PTC patients aged 65 years and older.

Results

In the univariate analysis, CLNM was significantly associated with tumor size, multifocality, bilaterality, and microcalcification, while only tumor size ≥ 1cm (OR = 0.530, P = 0.019, 95% CI = 0.311 - 0.900) and multifocality (OR = 0.291, P < 0.001, 95% CI = 0.148 - 0.574) remained as risk factors in the multivariate analysis. LLNM was confirmed to be associated with male (OR = 0.454, P < 0.020, 95% CI = 0.233 - 0.884), tumor size ≥ 1cm (OR = 0.471, P = 0.030, 95% CI = 0.239 - 0.928), age ≥ 70 (OR = 0.489, P = 0.032, 95% CI = 0.254 - 0.941), and microcalcification (OR = 0.384, P = 0.008, 95% CI = 0.189 - 0.781) in the multivariate analysis. In elderly PTC patients with CLNM, male gender (OR = 0.350, P = 0.021, 95% CI = 0.143 - 0.855), age ≥ 70 (OR = 0.339, P = 0.015, 95% CI = 0.142 - 0.810), and bilaterality (OR = 0.320, P = 0.012, 95% CI = 0.131 - 0.779) were closely associated with concomitant LLNM in both univariate and multivariate analyses.

Conclusion

For elderly PTC patients aged 65 and older, tumor size ≥ 1cm and multifocality are significant risk factors for CLNM. Meanwhile, male, tumor size ≥ 1cm, age ≥ 70, and microcalcification are crucial predictors for LLNM. In patients already diagnosed with CLNM, male, age ≥ 70, and bilaterality increase the risk of LLNM.

Application of ultrasound elastography and radiomic for predicting central cervical lymph node metastasis in papillary thyroid microcarcinoma

Objective

This study aims to combine ultrasound (US) elastography (USE) and radiomic to predict central cervical lymph node metastasis (CLNM) in patients with papillary thyroid microcarcinoma (PTMC).

Methods

A total of 204 patients with 204 thyroid nodules who were confirmed with PTMC and treated in our hospital were enrolled and randomly assigned to the training set (n = 142) and the validation set (n = 62). US features, USE (gender, shape, echogenic foci, thyroid imaging reporting and data system (TIRADS) category, and elasticity score), and radiomic signature were employed to build three models. A nomogram was plotted for the combined model, and decision curve analysis was applied for clinical use.

Results

The combined model (USE and radiomic) showed optimal diagnostic performance in both training (AUC = 0.868) and validation sets (AUC = 0.857), outperforming other models.

Conclusion

The combined model based on USE and radiomic showed a superior performance in the prediction of CLNM of patients with PTMC, covering the shortage of low specificity of conventional US in detecting CLNM.

Clinical and molecular features of progressive papillary thyroid microcarcinoma

In recent decades, the prevalence of thyroid cancer has risen substantially, with papillary thyroid microcarcinoma (PTMC) constituting over 50% of cases. Although most PTMCs exhibit indolent growth and a favorable prognosis, some present an increased risk of recurrence and an unfavorable prognosis due to high-risk characteristics such as lymph node metastasis, extrathyroidal extension, and distant metastasis. The early identification of clinically progressing PTMC remains elusive. In this review, the authors summarize findings from PTMC progression-related literature, highlighting that factors such as larger tumor size, cervical lymph node metastasis, extrathyroidal extension, younger age, higher preoperative serum thyroid-stimulating hormone levels, family history, and obesity positively correlate with PTMC progression. The role of multifocality in promoting PTMC progression; however, remains contentious. Furthermore, recent studies have shed light on the impact of mutations, such as BRAF and TERT mutations, on PTMC progression. Researchers have identified several mRNAs, noncoding RNAs, and proteins associated with various features of PTMC progression. Some studies propose that peripheral and tumor tissue-infiltrating immune cells could serve as biomarkers for the clinical progression of PTMC. Collectively, these clinical and molecular features offer a rationale for the early detection and the development of precision theranostic strategies of clinically progressive PTMC.

Papillary Thyroid Microcarcinoma: Differences between Lesions in Incidental and Nonincidental Settings-Considerations on These Clinical Entities and Personal Experience

Papillary thyroid microcarcinoma (PTMC) represents 35-40% of all papillary cancers; it is defined as a nodule ≤ 10 mm at the time of histological diagnosis. The clinical significance of PTMC is still controversial, and it may be discovered in two settings: incidental PTMC (iPTMC), in which it is identified postoperatively upon histological examination of thyroid specimens following thyroid surgery for benign disease, and nonincidental PTMC (niPTMC), in which it is diagnosed before surgery. While iPTMC appears to be related to mild behavior and favorable clinical outcomes, niPTMC may exhibit markers of aggressiveness. We retrospectively review our experience, selecting 54 PTMCs: 28 classified as niPTMC (52%) and 26 classified as iPTMC (48%). Patients with niPTMC showed significant differences, such as younger age at diagnosis (p < 0.001); a lower male/female ratio (p < 0.01); a larger mean nodule diameter (p < 0.001); and a higher rate of aggressive pathological findings, such as multifocality, capsular invasion and/or lymphovascular invasion (p = 0.035). Other differences found in the niPTMC subgroup included a higher preoperative serum TSH level, higher hospital morbidity and a greater need for postoperative iodine ablation therapy (p < 0.05), while disease-free long-term survival did not differ between subgroups (p = 0.331) after a mean follow-up (FU) of 87 months, with one nodal recurrence among niPTMCs. The differences between iPTMC and niPTMC were consistent: patients operated on for total thyroidectomy and showing iPTMC can be considered healed after surgery, and follow-up should be designed to properly calibrate hormonal supplementation; conversely, niPTMC may sometimes exhibit aggressive behavior, and so the FU regimen should be closer and aimed at early detection of cancer recurrence.

Deep learning prediction model for central lymph node metastasis in papillary thyroid microcarcinoma based on cytology

Controversy exists regarding whether patients with low-risk papillary thyroid microcarcinoma (PTMC) should undergo surgery or active surveillance; the inaccuracy of the preoperative clinical lymph node status assessment is one of the primary factors contributing to the controversy. It is imperative to accurately predict the lymph node status of PTMC before surgery. We selected 208 preoperative fine-needle aspiration (FNA) liquid-based preparations of PTMC as our research objects; all of these instances underwent lymph node dissection and, aside from lymph node status, were consistent with low-risk PTMC. We separated them into two groups according to whether the postoperative pathology showed central lymph node metastases. The deep learning model was expected to predict, based on the preoperative thyroid FNA liquid-based preparation, whether PTMC was accompanied by central lymph node metastases. Our deep learning model attained a sensitivity, specificity, positive prediction value (PPV), negative prediction value (NPV), and accuracy of 78.9% (15/19), 73.9% (17/23), 71.4% (15/21), 81.0% (17/21), and 76.2% (32/42), respectively. The area under the receiver operating characteristic curve (value was 0.8503. The predictive performance of the deep learning model was superior to that of the traditional clinical evaluation, and further analysis revealed the cell morphologies that played key roles in model prediction. Our study suggests that the deep learning model based on preoperative thyroid FNA liquid-based preparation is a reliable strategy for predicting central lymph node metastases in thyroid micropapillary carcinoma, and its performance surpasses that of traditional clinical examination.

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.

Prediction model of cervical lymph node metastasis based on clinicopathological characteristics of papillary thyroid carcinoma: a dual-center retrospective study

BACKGROUND

The overall prevalence of papillary thyroid carcinoma (PTC) patients is expanding along with an ongoing increase in thyroid cancer incidence. Patients with PTC who have lymph node metastases have a poor prognosis and a high death rate. There is an urgent need for indicators that can predict lymph node metastasis (LNM) before surgery as current imaging techniques, such as ultrasonography, do not have sufficient sensitivity to detect LNM. To predict independent risk factors for Central lymph node metastasis (CLNM) or Lateral lymph node metastasis (LLNM), we therefore developed two nomograms based on CLNM and LLNM, separately.

METHODS

In two centers, the Second Affiliated Hospital of Nanchang University and Yichun People's Hospital, we retrospectively analyzed clinicopathological characteristics of PTC patients. We utilized multivariate analysis to screen for variables that might be suspiciously related to CLNM or LLNM. Furthermore, we developed nomograms to graphically depict the independent risk valuables connected to lymph node metastasis in PTC patients.

RESULT

Ultimately, 6068 PTC patients in all were included in the research. Six factors, including age<45, male, mETE, TSH>1.418, tumor size>4cm, and location (multicentric and lobe), were observed to be related to CLNM. Age<45, male, mETE (minimal extrathyroidal extension), multifocality, TSH≥2.910, CLNM positive, and tumor size>4cm were regarded as related risk factors for LLNM. The two nomograms developed subsequently proved to have good predictive power with 0.706 and 0.818 and demonstrated good clinical guidance functionality with clinical decision curves and impact curves.

CONCLUSION

Based on the successful establishment of this dual-institution-based visual nomogram model, we found that some clinical features are highly correlated with cervical lymph node metastasis, including CLNM and LLNM, which will better help clinicians make individualized clinical decisions for more effectively rationalizing managing PTC patients.

Factors of Central Lymph Node Metastasis in Papillary Thyroid Cancer Based on C-TIRADS Analysis

To study risk factors for central lymph node metastasis (CLNM) in papillary thyroid cancer (PTC) using the Chinese Thyroid Imaging Reporting and Data System (C-TIRADS). We retrospectively analysed patients who underwent PTC surgery and central lymph node dissection at First People's Hospital of Foshan City. The clinical and ultrasonic data of the patients from 1150 cases were analysed by multivariate regression to evaluate the correlation between grayscale ultrasound (US) features, C-TIRADS score, and the classification of thyroid nodules and CLNM of PTCs. The C-TIRADS score was 3.0±1.0 in the CLNM group, which was higher than that in the non-CLNM group (p<0.001). Sex (male) (OR=1.586, 95% CI 1.232-2.042, p<0.001), age (≤45 years) (OR=1.508, 95% CI 1.184-1.919, p=0.001), location of nodes (lower pole) (OR=2.193, 95% CI 1.519-3.166, p<0.001), number (multifocal) (OR=2.204, 95% CI 1.227-2.378, p<0.001), microcalcification (OR=1.610, 95% CI 2.225-4.434, p=0.002), extrathyroidal extension (OR=2.204, 95% CI 1.941-3.843, p<0.001), maximum diameter of nodule (≥20 mm) (OR=3.211, 95% CI 2.337-4.411, p<0.001), and C-TIRADS score (OR=1.356, 95% CI 1.204-1.527, p<0.001) were PTC in independent risk factors for CLNM. The C-TIRADS score of PTC combined with the location, number, size, and ultrasound features of the lesion and the patient's sex and age are important in predicting whether they present with CLNM and provide a reference basis for the clinical formulation of a reasonable surgical treatment plan.

Prevalence of Occult Central Lymph Node Metastasis by Tumor Size in Papillary Thyroid Carcinoma: A Systematic Review and Meta-Analysis

BACKGROUND

While papillary thyroid carcinoma (PTC) is associated with high occult central neck metastasis (CNM) rates, prophylactic central neck dissection (pCND) is controversial. This meta-analysis aims to look at the occult CNM rate according to tumor size.

METHODS

A literature search was conducted in PubMed from inception to April 2023. Inclusion criteria were primary studies that determined occult CNM rates in cN0 PTC by tumor size. Heterogeneity, influential case diagnostics, and proportion data were evaluated with Cochran's Q-test, Baujat plots and Forest plots, respectively.

RESULTS

Fifty-two studies were included in this meta-analysis. The findings demonstrated an occult CNM rate of 30.3% for tumors ≤ 5 mm, 32.7% for tumors ≤ 1 cm, 46.0% for tumors between 1 and 2 cm, 43.1% for tumors between 2 and 4 cm, and 61.2% for tumors > 4 cm. The heterogeneity of each study group was high, though no publication bias was noted. While there was a trend towards increased occult CNM rates with larger tumors, comparisons between different size cutoffs varied in significance.

CONCLUSION

This comprehensive review affirms that occult CNM is high and that an ipsilateral pCND can be justified in all PTC patients for accurate differentiation between Stage I and Stage II disease and its clinical implications.

Construction and validation of nomograms to reduce completion thyroidectomy by predicting lymph node metastasis in low-risk papillary thyroid carcinoma

CONTEXT

More than 5 central lymph nodes metastases (CLNM) or lateral lymph node metastasis (LLNM) indicates a higher risk of recurrence in low-risk papillary thyroid carcinoma (PTC) and may lead to completion thyroidectomy (CTx) in patients initially undergoing lobectomy.

OBJECTIVE

To screen potentially high-risk patients from low-risk patients by using preoperative and intraoperative clinicopathological features to predict lymph node status.

METHODS

A retrospective analysis of 8301 PTC patients in Wuhan Union Hospital database (2009-2021) was performed according to the 2015 American Thyroid Association (ATA) and 2021 National Comprehensive Cancer Network (NCCN) guidelines, respectively. Logistic regression and best subsets regression were used to identify risk factors. Nomograms were established and externally validated using the Differentiated Thyroid Cancer in China cohort.

RESULTS

More than 5 CLNM or LLNM was detected in 1648 (19.9%) patients. Two predictive models containing age, gender, maximum tumor size, free thyroxine (FT4) and palpable node (all p < 0.05) were established. The nomogram based on NCCN criteria showed better discriminative power and consistency with a specificity of 0.706 and a sensitivity of 0.725, and external validation indicated that 76% of potentially high-risk patients could achieve preoperative conversion of surgical strategy.

CONCLUSIONS

Models based on large cohorts with good predictive performance were constructed and validated. Preoperative low-risk (T1-2N0M0) patients with age younger than 40 years, male gender, large tumor size, low FT4 and palpable nodes may be at high risk of LLNM or more than 5 CLNM, and they should receive more aggressive initial therapy to reduce CTx.

Patient's age with papillary thyroid cancer: Is it a key factor for cervical lymph node metastasis?

BACKGROUND

Age is one of the important prognostic indicators of papillary thyroid cancer (PTC). However, the distinct metastatic patterns and prognosis of age-related lymph node metastasis (LNM) are unclear. This study aims to investigate the impact of age on LNM.

METHODS

We conducted two independent cohort studies to assess age-nodal disease association using logistic regression analysis and a restricted cubic splines model. A multivariable Cox regression model was utilized to test the impact of nodal disease on cancer-specific survival (CSS) after age stratification.

RESULTS

For this study, we included 7572 and 36,793 patients with PTC in Xiangya and SEER cohorts, respectively. After adjustment, advanced age was linearly associated with decreasing risk of central LNM. Patients of age ≤18 years (OR = 4.41, P < 0.001) and 19-45 years (OR = 1.97, P = 0.002) had a higher risk of developing lateral LNM than patients of age >60 years in both cohorts. Furthermore, CSS is significantly reduced in N1b disease (P < 0.001), not N1a disease, regardless of age. The incidence of high-volume LNM (HV-LNM) was significantly higher in patients of age ≤18 years and 19-45 years than in those of age >60 years (P < 0.001), in both cohorts. In addition, CSS was compromised in patients with PTC of age 46-60 years (HR = 1.61, P = 0.022) and those of age >60 (HR = 1.40, P = 0.021) after developing HV-LNM.

CONCLUSIONS

Patient age is significantly associated with LNM and HV-LNM. Patients with N1b disease or patients with HV-LNM of age >45 years have significantly shorter CSS. Age can, thus, be a useful guide for determining treatment strategies in PTC.

The risk of central nodal metastasis based on prognostic factors of the differentiated thyroid carcinoma: a systematic review and meta-analysis study

BACKGROUND AND AIM

Despite improving the 10-year disease-free-survival, prophylactic central neck dissection (pCND) in differentiated thyroid carcinoma (DTC) should only be considered in patients with high risk factors for lymph node (LN) metastasis due to the increases in the risk of postoperative complications. Our aim was to identify the risk factors for central lymph node metastasis (CLNM) in DTC.

METHOD

We searched PubMed, Scopus, Web of science, Cochrane library for eligible studies from inception to November 1, 2021 and a systematic review and meta-analysis were carried out to identify the risk factors for CLNM in DTC.

RESULTS

We included 41 studies with total of 27,741 patients in this study. The pooled results in this meta-analysis showed that these risk factors were significantly associated with CLNM: age < 45 years (odds ratio (OR) 1.64, 95% confidence interval (CI) 1.34-1.99, p < 0.00001), male sex (OR 1.73, 95% CI 1.54-1.93, p < 0.00001), multifocality (OR 1.87, 95% CI 1.59-2.19, p < 0.00001), bilateral disease (OR 1.43, 95% CI 1.15-1.78, p < 0.001), capsular invasion (OR 1.67, 95% CI 1.10-2.54, p < 0.02), lymphovascular invasion (OR 4.89, 95% CI 2.76-8.66, p < 0.00001) and extra-thyroidal extension (OR 2.43, 95% CI 1.97-3.00, p < 0.00001). In addition, young age (< 45 years), male sex, multifocality, and extra-thyroidal extension were significantly associated with large-volume CLNM in clinically N0 DTC patients. However, the presence of Hashimoto's thyroiditis was not a predictors of large-volume CLNM.

CONCLUSION

Young age (< 45 years), male sex, bilateral disease, multifocality, capsular invasion, lymphovascular invasion and extra-thyroidal extension are significantly associated with CLNM and pCND would be expected to have a higher yield in patients with these risk factors.

Clinical Value of Ultrasonography and Serum Markers in Preoperative N Staging of Thyroid Cancer

We aimed to determine factors influencing lymph node metastasis (LNM) and develop a more effective method to assess preoperative N staging. Overall, data of 2130 patients who underwent thyroidectomy for thyroid cancer between 2018 and 2021 were retrospectively analysed. Patients were divided into groups according to pN0, pN1a, and pN1b stages. Pathology was used to analyse the correlation between preoperative serum marker indicators and LNM. Receiver operating characteristic curves were used to compare the diagnostic value of ultrasound (US) examination alone, serum thyroglobulin, age, and combined method for LNM. A significant moderate agreement was observed between preoperative US and postoperative pathology for N staging. Between the pN0 and pN1 (pN1a + pN1b) groups, the differences in free triiodothyronine, anti-thyroid peroxidase antibody, and serum thyroglobulin levels were statistically significant. Among the indicators, serum thyroglobulin was an independent predictor of LNM. The area under the receiver operating characteristic curve was 0.610 for serum thyroglobulin level for predicting LNM, 0.689 for US alone, and 0.742 for the combined method. Both preoperative US and serum thyroglobulin level provide a specific value when evaluating the N staging of thyroid cancer, and the combined method is more valuable in the diagnosis of LNM than US alone.

Exploring risk factors for cervical lymph node metastasis in papillary thyroid microcarcinoma: construction of a novel population-based predictive model

BACKGROUND

Machine learning was a highly effective tool in model construction. We aim to establish a machine learning-based predictive model for predicting the cervical lymph node metastasis (LNM) in papillary thyroid microcarcinoma (PTMC).

METHODS

We obtained data on PTMC from the SEER database, including 10 demographic and clinicopathological characteristics. Univariate and multivariate logistic regression (LR) analyses were applied to screen the risk factors for cervical LNM in PTMC. Risk factors with P < 0.05 in multivariate LR analysis were used as modeling variables. Five different machine learning (ML) algorithms including extreme gradient boosting (XGBoost), random forest (RF), adaptive boosting (AdaBoost), gaussian naive bayes (GNB) and multi-layer perceptron (MLP) and traditional regression analysis were used to construct the prediction model. Finally, the area under the receiver operating characteristic (AUROC) curve was used to compare the model performance.

RESULTS

Through univariate and multivariate LR analysis, we screened out 9 independent risk factors most closely associated with cervical LNM in PTMC, including age, sex, race, marital status, region, histology, tumor size, and extrathyroidal extension (ETE) and multifocality. We used these risk factors to build an ML prediction model, in which the AUROC value of the XGBoost algorithm was higher than the other 4 ML algorithms and was the best ML model. We optimized the XGBoost algorithm through 10-fold cross-validation, and its best performance on the training set (AUROC: 0.809, 95%CI 0.800-0.818) was better than traditional LR analysis (AUROC: 0.780, 95%CI 0.772-0.787).

CONCLUSIONS

ML algorithms have good predictive performance, especially the XGBoost algorithm. With the continuous development of artificial intelligence, ML algorithms have broad prospects in clinical prognosis prediction.

Nomograms based on sonographic and clinicopathological characteristics to predict lateral lymph node metastasis in classic papillary thyroid carcinoma

BACKGROUND

Lateral lymph node metastasis (LLNM) has been considered a risk factor of recurrence in papillary thyroid cancer (PTC). Preoperative detecting LLNM accurately is difficult. Solitary lateral lymph node metastasis is a special type of LLNM. We aimed to develop nomograms for predicting LLNM and multiple lateral lymph node metastasis (MLLNM).

METHODS

We retrospectively retrieved 528 classic PTC patients that underwent surgery between March 2019 and May 2020. Sonographic and clinicopathological features were collected. Risk factors of LLNM and MLLNM were determined by univariate and multivariate analysis. Nomograms for predicting LLNM and MLLNM were developed.

RESULTS

LLNM was independently associated with tumor size, the number of foci, location, margin, central lymph node metastasis, and lymph node ratio. Independent predictors of MLLNM were age, margin, and the number of metastatic lymph nodes in central compartment. By using above variables, we constructed nomograms for predicting LLNM and MLLNM, with area under curves of 0.864 and 0.748, respectively.

CONCLUSION

Through these accurate and easy-to-use nomograms, we can detect the risk of residual LLNM postoperatively for classic PTC patients who did not receive lateral neck dissection and provide an individualized plan for postoperative management of classic PTC patients.

Ultrasound gray scale ratio for differential diagnosis of papillary thyroid microcarcinoma from benign micronodule in patients with Hashimoto's thyroiditis

BACKGROUND

To investigate the diagnostic value of ultrasound gray scale ratio (UGSR) in differentiating papillary thyroid microcarcinomas (PTMCs) from benign micronodules (BMNs) in patients with Hashimoto's thyroiditis (HT).

METHODS

The ultrasound images of 285 PTMCs (from 247 patients) and 173 BMNs (from 140 patients) in the HT group, as well as 461 PTMCs (from 417 patients) and 234 BMNs (from 197 patients) in the non-HT group were retrospectively analyzed. The diagnosis of all cases was confirmed by histopathological examinations. The gray scale values of the nodules and surrounding thyroid tissues were measured and subsequently the UGSRs were calculated. Receiver operating characteristic curve analysis was used to determine the area under the curve (AUC), optimal UGSR threshold, sensitivity and specificity in differentiating PTMCs and BMNs in the two groups.

RESULTS

The UGSR of PTMC and BMN was 0.52 ± 0.12 and 0.85 ± 0.24 in the HT group (P < 0.001), and 0.57 ± 0.13 and 0.87 ± 0.20 in the non-HT group (P < 0.001), respectively. The difference in PTMC-UGSR was significant between the two groups (P < 0.001), whereas BMN-UGSR did not differ between the two groups (P = 0.416). The AUC, optimal UGSR threshold, sensitivity and specificity of UGSR for differentiating PTMC and BMN in the HT and non-HT group were 0.890 versus 0.901, 0.68 versus 0.72, 91.23% versus 90.67%, and 77.46% versus 82.05%, respectively.

CONCLUSIONS

The USGR of the HT group was lower than that of the non-HT group. Moreover, UGSR exhibited important diagnostic value in differentiating PTMC from BMN in both HT and non-HT groups.

A novel tool for predicting the risk of central lymph node metastasis in patients with papillary thyroid microcarcinoma: a retrospective cohort study

Introduction

Central lymph node status in papillary thyroid microcarcinoma (PTMC) plays an important role in treatment decision-making clinically, however, it is not easy to predict central lymph node metastasis (CLNM). The present work focused on finding the more rational alternative for evaluating central lymph node status while identifying influencing factors to construct a model to predict CLNM incidence.

Methods

In this study, we retrospectively analyzed the typical sonographic and clinicopathologic features of 546 PTMC patients who underwent surgery, among which, the data of 382 patients were recruited in the training cohort and that of 164 patients in the validation cohort. Based on the outcome of the training cohort, significant influencing factors were further identified through univariate analysis and were considered as independent variables in multivariable logistic regression analysis and incorporated in and presented with a nomogram.

Results

In total, six independent predictors, including the age, sex, tumor size, multifocality, capsular invasion, Hashimotos thyroiditis were entered into the nomogram. Both internal validation and external validation revealed the favorable discrimination of our as-constructed nomogram. Calibration curves exhibited high consistency. As suggested by decision-curve analyses, the as-constructed nomogram might be applied in clinic. Besides, the model also distinguished patients according to risk stratification.

Conclusions

The novel nomogram containing remarkable influencing factors for CLNM cases was established in the present work. The nomogram can assist clinicians in clinical decision-making.

Clinicopathologic predictors of central lymph node metastases in clinical node-negative papillary thyroid microcarcinoma: a systematic review and meta-analysis

Background

The presence of central lymph node metastases (CLNM) has been suggested as a risk factor for poorer prognosis and recurrence in papillary thyroid microcarcinoma (PTMC). However, the clinicopathologic factors for CLNM in clinical node-negative (CN0) PTMC were not well defined. This study aimed to perform a systematic review and meta-analysis to investigate the significant clinicopathologic predictors of CLNM in CN0 PTMC.

Methods

A systematic literature search was performed in PubMed, Embase, Cochrane Library, and Web of Science. Case-control studies on the association of clinicopathologic risk factors with CLNM in CN0 PTMC were included.

Results

Thirteen eligible studies involving 6068 patients with CN0 PTMC were included. From the pooled analyses, male (odds ratio [OR]: 2.07, 95% CI: 1.49–2.87, P < 0.001), multifocality (OR: 1.88, 95% CI: 1.54–2.29, P < 0.001), tumor size > 5 mm (OR: 1.84, 95% CI: 1.55–2.18, P < 0.001), and extrathyroidal extension (OR: 1.96, 95% CI: 1.30–2.95, P = 0.001) are significantly associated with increased risk of CLNM in CN0 PTMC. A sample size with a cutoff point of 200 was identified as the source of heterogeneity for sex according to meta-regression (t = 3.18, P = 0.033). Then, the subgroup analysis of male was performed, which illustrated that male increased the risk of CLNM in the small sample group (SG) and the large sample group (LG) by 6.11-folds and 2.01-folds, respectively (SG: OR, 6.11, 95% CI, 3.16–11.81, P < 0.001; LG: OR, 2.01, 95% CI, 1.65–2.46, P < 0.001).

Conclusions

Male, multifocality, tumor size > 5 mm, and extrathyroidal extension may be reliable clinical predictors of CLNM in CN0 PTMC. Moreover, prophylactic central lymph node dissection should be considered in surgical decision-making for CN0 PTMC patients, who are male, multifocal, with tumor size > 5 mm, and with extrathyroidal extension.

Trial registration

CRD42021242211 (PROSPERO)

Supplementary Information

The online version contains supplementary material available at 10.1186/s12957-022-02573-7.

Development and validation of an individualized nomogram for predicting the high-volume (> 5) central lymph node metastasis in papillary thyroid microcarcinoma

PURPOSE

Papillary thyroid microcarcinoma (PTMC) frequently presents a favorable clinical outcome, while aggressive invasiveness can also be found in some of this population. Identifying the risk clinical factors of high-volume (> 5) central lymph node metastasis (CLNM) in PTMC patients could help oncologists make a better-individualized clinical decision.

METHODS

We retrospectively reviewed the clinical characteristics of adult patients with PTC in the Surveillance, Epidemiology, and End Results (SEER) database between Jan 2010 and Dec 2015 and in one medical center affiliated to Chongqing Medical University between Jan 2018 and Oct 2020. Univariate and multivariate logistic regression analyses were used to determine the risk factors for high volume of CLNM in PTMC patients.

RESULTS

The male gender (OR = 2.02, 95% CI 1.46-2.81), larger tumor size (> 5 mm, OR = 1.64, 95% CI 1.13-2.38), multifocality (OR = 1.87, 95% CI 1.40-2.51), and extrathyroidal invasion (OR = 3.67; 95% CI 2.64-5.10) were independent risk factors in promoting high-volume of CLNM in PTMC patients. By contrast, elderly age (≥ 55 years) at diagnosis (OR = 0.57, 95% CI 0.40-0.81) and PTMC-follicular variate (OR = 0.60, 95% CI 0.42-0.87) were determined as the protective factors. Based on these indicators, a nomogram was further constructed with a good concordance index (C-index) of 0.702, supported by an external validating cohort with a promising C-index of 0.811.

CONCLUSION

A nomogram was successfully established and validated with six clinical indicators. This model could help surgeons to make a better-individualized clinical decision on the management of PTMC patients, especially in terms of whether prophylactic central lymph node dissection and postoperative radiotherapy should be warranted.

LASSO-based machine learning models for the prediction of central lymph node metastasis in clinically negative patients with papillary thyroid carcinoma

BACKGROUND

The presence of central lymph node metastasis (CLNM) is crucial for surgical decision-making in clinical N0 (cN0) papillary thyroid carcinoma (PTC) patients. We aimed to develop and validate machine learning (ML) algorithms-based models for predicting the risk of CLNM in cN0 patients.

METHODS

A total of 1099 PTC patients with cN0 central neck from July 2019 to March 2022 at our institution were retrospectively analyzed. All patients were randomly split into the training dataset (70%) and the validation dataset (30%). Eight ML algorithms, including the Logistic Regression, Gradient Boosting Machine, Extreme Gradient Boosting (XGB), Random Forest (RF), Decision Tree, Neural Network, Support Vector Machine and Bayesian Network were used to evaluate the risk of CLNM. The performance of ML models was evaluated by the area under curve (AUC), sensitivity, specificity, and decision curve analysis (DCA).

RESULTS

We firstly used the LASSO Logistic regression method to select the most relevant factors for predicting CLNM. The AUC of XGB was slightly higher than RF (0.907 and 0.902, respectively). According to DCA, RF model significantly outperformed XGB model at most threshold points and was therefore used to develop the predictive model. The diagnostic performance of RF algorithm was dependent on the following nine top-rank variables: size, margin, extrathyroidal extension, sex, echogenic foci, shape, number, lateral lymph node metastasis and chronic lymphocytic thyroiditis.

CONCLUSION

By incorporating clinicopathological and sonographic characteristics, we developed ML-based models, suggesting that this non-invasive method can be applied to facilitate individualized prediction of occult CLNM in cN0 central neck PTC patients.

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

Objective: The aim of this study was to establish a practical nomogram for preoperatively predicting the possibility of cervical lymph node metastasis (CLNM) based on clinicopathological and ultrasound (US) imaging characteristics in patients with clinically node-negative (cN0) unilateral papillary thyroid microcarcinoma (PTMC) in order to determine a personal surgical volume and therapeutic strategy. Methods: A total of 269 consecutive patients diagnosed with cN0 unilateral PTMC by postoperative pathological examination from January 2018 to December 2020 were retrospectively analyzed. All the patients underwent lobectomy or thyroidectomy with routine prophylactic central lymph node dissection (CLND) and were divided into a CLNM group and a non-CLNM group. Using logistic regression, the least absolute shrinkage and selection operator (LASSO) regression analysis was applied to determine the risk factors for CLNM in patients with unilateral cN0 PTMC. A nomogram including risk-factor screening using LASSO regression for predicting the CLNM in patients with cN0 unilateral PTMC was further developed and validated. Results: Risk factors identified by LASSO regression, including age, sex, tumor size, presence of extrathyroidal extension (ETE), tumor diameter/lobe thickness (D/T), tumor location, and coexistent benign lesions, were potential predictors for CLNM in patients with cN0 unilateral PTMC. Meanwhile, age (odds ratio [OR] = 0.261, 95% CI.104-0.605; P = 0.003), sex (men: OR = 3.866; 95% CI 1.758-8.880; P < 0.001), ETE (OR = 3.821; 95% CI 1.168-13.861; P = 0.032), D/T (OR = 72.411; 95% CI 5.483-1212.497; P < 0.001), and coexistent benign lesions (OR = 3.112 95% CI 1.407-7.303; P = 0.007) were shown to be significantly related to CLNM by multivariant logistic regression. A nomogram for predicting CLNM in patients with cN0 unilateral PTMC was established based on the risk factors identified by the LASSO regression analysis. The receiver operating characteristic (ROC) curve for predicting CLNM by nomogram showed that the area under the curve (AUC) was 0.777 and exhibited an excellent consistency. Conclusions: A nomogram based on clinical and US imaging characteristics for predicting the probability of CLNM in patients with cN0 unilateral PTMC was developed, which showed a favorable predictive value and consistency. Further prospective research to observe the oncological outcomes is necessary to determine whether the nomogram could potentially guide a personalized surgical volume and surgical approach.

Reevaluation of Criteria and Establishment of Models for Total Thyroidectomy in Differentiated Thyroid Cancer

Introduction

After the publication of the 2015 American Thyroid Association (ATA) guidelines, the indication for total thyroidectomy (TT) was reported to be underestimated before surgery, which may lead to a substantial rate of secondary completion thyroidectomy (CTx).

Methods and Materials

We retrospectively analyzed differentiated thyroid cancer patients from Wuhan Union Hospital (WHUH). Univariate analysis was performed to evaluate all preoperative and intraoperative factors. New models were picked out by comminating and arranging all significant factors and were compared with ATA and National Comprehensive Cancer Network (NCCN) guidelines in the multicenter prospective Differentiated Thyroid Cancer in China (DTCC) cohort.

Results

A total of 5,331 patients from WHUH were included. Pre- and intraoperative criteria individually identified 906 (17.0%) and 213 (4.0%) patients eligible for TT. Among all factors, age <35 years old, clinical N1, and ultrasound reported local invasion had high positive predictive value to predict patients who should undergo TT. Accordingly, we established two new models that minorly revised ATA guidelines but performed much better. Model 1 replaced “nodule size >4 cm” with “age <35 years old” and achieved significant increase in the sensitivity (WHUH, 0.711 vs. 0.484; DTCC, 0.675 vs. 0.351). Model 2 simultaneously demands the presence of “nodule size >4 cm” and “age <35 years old,” which had a significant increase in the specificity (WHUH, 0.905 vs. 0.818; DTCC, 0.729 vs. 0.643).

Conclusion

All high-risk factors had limited predictive ability. Our model added young age as a new criterion for total thyroidectomy to get a higher diagnostic value than the guidelines.

Web-Based Ultrasonic Nomogram Predicts Preoperative Central Lymph Node Metastasis of cN0 Papillary Thyroid Microcarcinoma

BACKGROUND

Many clinicians are facing the dilemma about whether they should apply the active surveillance (AS) strategy for managing Clinically Node-negative (cN0) PTMC patients in daily clinical practice. This research plans to construct a dynamic nomogram based on network, connected with ultrasound characteristics and clinical data, to predict the risk of central lymph node metastasis (CLNM) in cN0 PTMC patients before surgery.

METHODS

A retrospective analysis of 659 patients with cN0 PTMC who had underwent thyroid surgery and central compartment neck dissection. Patients were randomly (2:1) divided into the development cohort (439 patients) and validation cohort (220 patients). The group least absolute shrinkage and selection operator (Group Lasso) regression method was used to select the ultrasonic features for CLNM prediction in the development cohort. These features and clinical data were screened by the multivariable regression analysis, and the CLNM prediction model and web-based calculator were established. Receiver operating characteristic, calibration curve, Clinical impact curve and decision curve analysis (DCA) were used to weigh the performance of the prediction model in the validation set.

RESULTS

Multivariable regression analysis showed that age, tumor size, multifocality, the number of contact surface, and real-time elastography were risk factors that could predict CLNM. The area under the curve of the prediction model in the development and validation sets were 0.78 and 0.77, respectively, with good discrimination and calibration. A web-based dynamic calculator was built. DCA proved that the prediction model had excellent net benefits and clinical practicability.

CONCLUSIONS

The web-based dynamic nomogram incorporating US and clinical features was able to forecast the risk of preoperative CLNM in cN0 PTMC patients, and has good predictive performance. As a new observational indicator, NCS can provide additional predictive information.