Risk Factors and a Prediction Model of Lateral Lymph Node Metastasis in CN0 Papillary Thyroid Carcinoma Patients With 1-2 Central Lymph Node Metastases

FNA-Tg improves the diagnostic efficacy of FNAC for PTC lateral cervical LN metastasis

The detection of thyroglobulin in lymph node (LN) fine needle aspirates (FNA-Tg) is an effective supplement to ultrasound and fine needle aspiration cytology (FNAC) in diagnosis of cervical LN metastases for patients with papillary thyroid carcinoma (PTC). However, there is no unique cutoff value of FNA-Tg in practice. We aimed to determine the optimal cutoff of FNA-Tg and evaluate the interfering factors of FNA-Tg. A serial of 405 lateral cervical LN samples from 317 patients with PTC were analyzed retrospectively. Receiver operating characteristic (ROC) curve was used to determine the optimal cutoff of FNA-Tg. The factors that impact FNA-Tg were evaluated. Serum Tg, serum thyroglobulin antibody (TgAb) and serum thyroid stimulating hormone (TSH) level, and thyroid status did not differ between the malignant and benign LN groups. The median FNA-Tg was 3.81 ng/mL (0.16-33.40 ng/mL) in LNs without metastasis and 310.32 ng/mL (124.41-500.00 ng/mL) in LNs with metastasis, and the differences reached statistical significance (P < 0.01). The optimal cutoff of FNA-Tg was 36.22 ng/mL, and the values of area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) was 0.88, 0.89, 0.75, 0.91 and 0.71, respectively. The diagnostic accuracy of FNA-Tg cutoff of 36.22ng/mL was 85%. When FNAC and the present FNA-Tg cutoff of 36.22 ng/mL were combined together, the diagnostic sensitivity, specificity, PPV, NPV and diagnostic accuracy was 0.96, 0.75, 0.92, 0.87 and 91%, respectively. FNA-Tg improves the diagnostic efficacy of FNAC for PTC lateral cervical LN metastasis, and the optimal cutoff value of FNA-Tg was 36.22 ng/mL. FNA-Tg was affected by LN status but was not associated with serum Tg, serum TgAb, serum TSH and thyroid status.

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.

Predictive risk-scoring model for lateral lymph node metastasis in papillary thyroid carcinoma

This study aims to evaluate candidate risk factors for lateral lymph node metastasis (LLNM) and develop a predictive model to identify high-risk groups among patients with papillary thyroid carcinoma (PTC). Additionally, we identified risk factors for recurrence to inform postoperative therapeutic decisions and follow-up for physicians and patients. A total of 4107 patients (4884 lesions) who underwent lymph node dissection at our hospital from 2005 to 2014 were evaluated. LLNM risk was stratified, and a risk-scoring model was developed based on identified independent risk factors for LLNM. Cox's proportional hazards regression model was used to investigate the risk factors for recurrence. Lateral Lymph Node (LLN) metastasis was observed in 10.49% (431/4107) of patients. Multivariate analysis identified the following independent risk predictors for LLN metastasis: Age ≤ 35 years (P = 0.002), tumor size > 1.0 cm (P = 0.000), lobe dissemination (+) (P = 0.000), and CLNM (+) (P = 0.000). A 12-point risk-scoring model was constructed to predict stratified LLNM in PTC patients, with an area under the receiver operating characteristic curve (AUROC) of 0.794 (95% CI: 0.774-0.814) (P < 0.01). The Cox regression model indicated that tumor size > 1.0 cm, lobe dissemination (+), multifocality, Central Lymph Node Metastasis (CLNM), and LLNM were significant risk factors associated with poor outcomes. Based on the risk scoring model, additional investigations and comprehensive considerations are recommended for patients with a total score greater than 5, and prophylactic cervical lymph node dissection is performed if necessary. Additionally, more aggressive treatment and more frequent follow-ups should be considered for patients with tumor size > 1.0 cm, lobe dissemination (+), multifocality, CLNM, and LLNM.

Nomogram for predicting the risk of cervical lymph node metastases and recurrence in papillary thyroid carcinoma based on the thyroid differentiation score system and clinical characteristics

BACKGROUND

This study aimed to identify independent predictors of cervical central lymph node metastasis (CLNM), cervical lateral lymph node metastasis (LLNM), and recurrence in patients with PTC, which could help guide the surgical management of these patients.

METHODS

This retrospective study analyzed data from 542 patients with PTC, who underwent thyroid surgery and were enrolled in The Cancer Genome Atlas and Gene Expression Omnibus databases. Patients were categorized into two groups based on the presence or absence of cervical LNM, classified as CLNM or LLNM. Data were randomly partitioned into training and validation sets in a ratio of 7:3. Age, sex, thyroid differentiation score (TDS), and other relevant attributes, were compared between the two groups using univariate and multivariate analyses and reported as odds ratios (OR) or hazard ratios (HR) with 95% confidence intervals (CI). Independent predictors were identified and used to develop nomograms. To assess the accuracy, discrimination, and clinical utility of the prediction model, calibration, receiver operating characteristic (ROC), and decision curve analysis (DCA) were performed for both the training and validation sets.

RESULTS

Of the 542 patients, 261 (48.15%) and 130 (23.99%) presented with CLNM and LLNM, respectively. The analyses identified several independent predictors for CLNM, including the presence of extrathyroidal invasion (OR 2.53, 95% CI 1.60-4.00), larger tumor dimension (OR 1.17, 95% CI 1.02-1.34), age over 55 years (OR 0.52, 95% CI 0.33-0.82), non-classic papillary subtype (OR 0.38, 95% CI 0.23-0.61), and lower TDS (OR 0.50, 95% CI 0.33-0.76). A greater number of excised cervical LNs (OR 12.30, 95% CI 4.35-34.77), the presence of CLNM (OR 1.07, 95% CI 1.04-1.10), and lower TDS (OR 0.09, 95% CI 0.04-0.21) were independent predictors for LLNM. Additionally, the independent predictors for relapse included age greater than 55 years (HR 1.87, 95% CI 1.00-3.49) and lower TDS (HR 0.35, 95% CI 0.20-0.62). These predictors were used to develop nomograms for CLNM, LLNM, and recurrence. ROC and DCA confirmed the discrimination and clinical utility of the models.

CONCLUSIONS

This study identified independent predictors of cervical CLNM, LLNM, and recurrence. Clinically relevant nomograms were developed that can assist in guiding cervical lymph node dissection and prediction of recurrence in patients with PTC.

A machine learning model utilizing Delphian lymph node characteristics to predict contralateral central lymph node metastasis in papillary thyroid carcinoma: a prospective multicenter study

BACKGROUND

This study aimed to use artificial intelligence (AI) to integrate various radiological and clinical pathological data to identify effective predictors of contralateral central lymph node metastasis (CCLNM) in patients with papillary thyroid carcinoma (PTC) and to establish a clinically applicable model to guide the extent of surgery.

METHODS

This prospective cohort study included 603 patients with PTC from three centers. Clinical, pathological, and ultrasonographic data were collected and utilized to develop a machine learning (ML) model for predicting CCLNM. Model development at the internal center utilized logistic regression along with other ML algorithms. Diagnostic efficacy was compared among these methods, leading to the adoption of the final model (random forest). This model was subject to AI interpretation and externally validated at other centers.

RESULTS

CCLNM was associated with multiple pathological factors. The Delphian lymph node metastasis ratio, ipsilateral central lymph node metastasis number, and presence of ipsilateral central lymph node metastasis were independent risk factors for CCLNM. Following feature selection, a Delphian lymph node-CCLNM (D-CCLNM) model was established using the Random forest algorithm based on five attributes. The D-CCLNM model demonstrated the highest area under the curve (AUC; 0.9273) in the training cohort and exhibited high predictive accuracy, with AUCs of 0.8907 and 0.9247 in the external and validation cohorts, respectively.

CONCLUSIONS

The authors developed a new, effective method that uses ML to predict CCLNM in patients with PTC. This approach integrates data from Delphian lymph nodes and clinical characteristics, offering a foundation for guiding surgical decisions, and is conveniently applicable in clinical settings.

Prediction of lateral lymph node metastasis with short diameter less than 8 mm in papillary thyroid carcinoma based on radiomics

OBJECTIVE

The aim of this study was to establish an ensemble learning model based on clinicopathological parameter and ultrasound radomics for assessing the risk of lateral cervical lymph node with short diameter less than 8 mm (small lymph nodes were used instead) metastasis in patients with papillary thyroid cancer (PTC), thereby guiding the selection of surgical methods.

METHODS

This retrospective analysis was conducted on 454 patients diagnosed with papillary thyroid carcinoma who underwent total thyroidectomy and lateral neck lymph node dissection or lymph node intraoperative frozen section biopsy at the First Hospital of China Medical University between January 2015 and April 2022. In a ratio of 8:2, 362(80%) patients were assigned to the training set and 92(20%) patients were assigned to the test set. Clinical pathological features and radomics features related to ultrasound imaging were extracted, followed by feature selection using recursive feature elimination (RFE). Based on distinct feature sets, we constructed ensemble learning models comprising random forest (RF), extreme gradient boosting (XGBoost), categorical boosting (CatBoost), gradient boosting decision tree (GBDT), and light gradient boosting machine (Lightgbm) to develop clinical models, radiomics models, and clinical-radiomic models. Through the comparison of performance metrics such as area under curve (AUC), accuracy (ACC), specificity (SPE), precision (PRE), recall rate, F1 score, mean squared error (MSE) etc., we identified the optimal model and visualized its results using shapley additive exPlanations (SHAP).

RESULTS

In this study, a total of 454 patients were included, among whom 342 PTC patients had small lymph node metastasis in the lateral neck region, while 112 did not have any metastasis. A total of 1035 features were initially considered for inclusion in this study, which were then narrowed down to 10 clinical features, 8 radiomics features, and 17 combined clinical-omics features. Based on these three feature sets, a total of fifteen ensemble learning models were established. In the test set, RF model in the clinical model is outperforms other models (AUC = 0.72, F1 = 0.75, Jaccard = 0.60 and Recall = 0.84), while CatBoost model in the radiomics model is superior to other models (AUC = 0.91, BA = 0.83 and SPE = 0.76). Among the clinical-radiomic models, Catboost exhibits optimal performance (AUC = 0.93, ACC = 0.88, BA = 0.87, F1 = 0.91, SPE = 0.83, PRE = 0.88, Jaccard = 0.83 and Recall = 0.92). Using the SHAP algorithm to visualize the operation process of the clinical-omics CatBoost model, we found that clinical omics features such as central lymph node metastasis (CLNM), Origin_Shape_Sphericity (o_shap_sphericity), LoG-sigma3_first order_ Skewness (log-3_fo_skewness), wavelet-HH_first order_Skewness (w-HH_fo_skewness) and wavelet-HH_first order_Skewness (sqr_gldm_DNUN) had the greatest impact on predicting the presence of lateral cervical small lymph node metastasis in PTC patients.

CONCLUSIONS

(1) In this study, among the ensemble learning models established based on clinicopathological features and radiomics features for predicting PTC lateral small lymph node metastasis, the clinical-radiomic CatBoost model has the best performance. (2) SHAP can visualize how the clinical and radiomics features affect the results and realize the interpretation of the model. (3) The combined CatBoost model can improve the diagnostic accuracy of suspicious lymph nodes with short diameter < 8 mm that are difficult to obtain accurate puncture results. The combined application of radiomics features is more accurate and reasonable than the prediction of clinical data alone, which helps to accurately evaluate the surgical scope and provide support for individual clinical decision making.

Factors associated with occult lateral lymph node metastases in patients with clinically lymph node negative papillary thyroid carcinoma: a systematic review and meta-analysis

Background

It remains unclear which category of patients with clinically lymph node negative (cN0) papillary thyroid carcinoma (PTC) might have higher risk of occult lateral lymph node metastasis (OLLNM) due to the conflicting results in previous studies. This systematic review and meta-analysis aimed to investigate factors associated with OLLNM in patients with cN0 PTC.

Methods

PubMed, EMBASE, Cochrane Library and Web of Science were comprehensively searched by two independent investigators to 15 August 2022. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated for the pooled analysis. This systematic review and meta-analysis was registered in PROSPERO (CRD42022353567).

Results

Fifteen eligible studies involving 8369 patients with cN0 PTC were included in this meta-analysis. We found 7 factors significantly associated with OLLNM, including male (OR, 1.47; 95% CI, 1.30 to 1.66; P < 0.001), age<45y (OR, 1.65; 95% CI, 1.31 to 2.06; P < 0.001), tumor size > 10mm (OR, 3.17; 95% CI, 2.04 to 4.93; P <0.001), tumor located in upper pole (OR, 1.81; 95% CI, 1.44 to 2.27; P <0.001), bilaterality (OR, 1.66; 95% CI, 1.37 to 2.02; P <0.001), extrathyroidal extension (ETE) (OR, 2.52; 95% CI, 1.72 to 3.68; P <0.001) and increased number of central lymph node metastasis (CLNM) (OR, 6.84; 95% CI, 5.66 to 8.27; P <0.001). The results of sensitivity analysis and subgroup analysis were similar to the pooled results. No significant publication bias was observed.

Conclusions

The systematic review and meta-analysis identified 7 factors associated with OLLNM in patients with cN0 PTC. Future studies are needed to validate our results.

Systematic review registration

https://www.crd.york.ac.uk/prospero, identifier CRD42022353567.

Prediction of lateral neck metastasis in patients with papillary thyroid cancer with suspicious lateral lymph ultrasonic imaging based on central lymph node metastasis features

Neck lymphatic metastasis is a common occurrence with thyroid cancers, and pre operative lateral lymph node metastasis (LLNM) and postoperative lateral lymph node recurrence (LLNR) are two independent risk factors that are negatively associated with the prognosis of patients with thyroid cancer. The aim of the present study was to investigate the relationship between central lymph node metastasis (CLNM) and LLNM in patients with papillary thyroid carcinoma (PTC) with sonographically suspected LLNM, such as those without lymph node fine-needle aspiration (FNA) cytological results or negative FNA results at the time of diagnosis. The predictive ability of CLNM regarding LLNR was also investigated. The present study retrospectively reviewed the clinical data of 1,061 patients that were surgically treated for PTC and 128 patients with sonographically suspected lateral lymph nodes that received central lymph node dissection and lateral lymph node dissection at the Thyroid Department of The First Affiliated Hospital of Anhui Medical University (Hefei, China) from June 2019 to June 2021. In patients with suspicious ultrasonic images suggesting LLNM, a significant association between the central lymph node ratio (CLNR), the number of positive central lymph nodes and LLNM was demonstrated. Otherwise, there were no statistically significant differences between the CLNR in patients with PTC and patients with PTC without evidence of lateral cervical metastasis. However, the rate of LLNR increased significantly when the number of positive central lymph nodes was >3. In conclusion, the CLNR and the number of positive central lymph nodes could be used to predict LLNM in patients with PTC with sonographically suspect lateral lymph nodes, including those with no FNA cytological results or negative FNA results, which may potentially support physicians in making personalized clinical decisions.

Risk factors and distribution pattern of lateral lymph node recurrence after central neck dissection for cN1a papillary thyroid carcinoma

BACKGROUND

The indication and extent of selective lateral neck dissection (LND) for cN1a papillary thyroid carcinoma (PTC) remain uncertain. The present study aimed to identify potential predictors and distribution pattern of lateral lymph node recurrence (LLNR) after central neck dissection in cN1a PTC patients.

METHODS

The cN1a PTC patients who underwent initial central neck dissection at our centre were retrospectively reviewed, and the median follow-up period was 6.8 years. Reoperation with LND was performed when LLNR was confirmed. Risk factors for LLNR were identified, and the metastatic status of each lateral level was recorded.

RESULTS

Of the 310 patients enrolled in the present study, fifty-eight patients (18.7%) presented with LLNR. Six independent factors, including tumour diameter, pathological T4 stage, number of involved central lymph nodes, pTNM stage, extrathyroidal extension, and I131 treatment (P values < 0.05) were identified via multivariate analysis. LLNR was found at level II in 26 patients (44.8%), level III in 38 patients (65.5%), level IV in 30 patients (51.7%), and level V in 8 patients (13.8%). The number of positive lateral lymph nodes at levels II, III, IV and V was 44 (22.9%), 76 (39.6%), 63 (32.8%), and 9 (4.9%), respectively.

CONCLUSIONS

For cN1a PTC patients who underwent central neck dissection, tumour diameter ≥ 2 cm, pathological T4 stage, number of involved central lymph nodes ≥ 3, pTNM stage III-IV, extrathyroidal extension, and failure to receive I131 treatment were independent predictors of LLNR, which was more likely to occur at levels III and IV.

[Analysis of risk factors for lymph node metastasis in patients with CN0 papillary thyroid carcinoma]

Objective:To explore the risk factors of lymph node metastasis and multiple lymph node metastasis in patients with stage CN0 papillary thyroid carcinoma. Methods:The clinical case data of 3 099 patients with CN0 papillary thyroid cancer who underwent lymph node dissection at Xijing Hospital of Air Force Medical University from January 2013 to December 2022 were retrospectively analyzed, univariate and multivariate logistic regression were used to analyze the risk factors of lymph node metastasis and multiple lymph node metastasis. Results:Male gender, age<55 years, multifocal lesions, and lesion size ≥2 cm were independent risk factors for lymph node metastasis in CN0 patients（P<0.05）, while diabetes was an independent protective factor for lymph node metastasis（P<0.05）.Age<55 years, capsular invasion, and multifocal lesions were independent risk factors for the presence of ≥3 lymph nodes with metastasis （P<0.05）. Conclusion:In CN0 stage PTC patients, special attention should be given to the possibility of lymph node metastasis when they are male, aged <55 years, have multifocal lesions, or have lesion size >2 cm.

A novel minimally invasive endoscopic approach of thyroid surgery-endoscopic thyroidectomy via sternocleidomastoid muscle posteroinferior approach

Background

Since the endoscopic thyroidectomy was firstly reported by Hüscher in 1997, there has been an ongoing debate regarding whether mainstream endoscopic thyroidectomy can be classified as minimally invasive surgery. In this study, we innovatively proposed the endoscopic thyroidectomy via sternocleidomastoid muscle posteroinferior approach (ETSPIA), a novel minimally invasive surgical technique, and compared its efficacy with the well-established transoral endoscopic thyroidectomy vestibular approach (TOETVA).

Methods

We retrospectively analyzed 50 patients who underwent ETSPIA and 50 patients who underwent TOETVA at Beijing Tongren Hospital, comparing their clinical characteristics, operative duration, blood loss, postoperative alterations in parathyroid hormone (PTH) and serum calcium, recovery post-surgery, complications, and follow-up data.

Results

The ETSPIA group had a shorter operation time compared to the TOETVA group (243.40±58.67 vs. 278.08±78.50 min; P=0.01). The ETSPIA group also had less intraoperative blood loss than the TOETVA group (20.60±10.58 vs. 33.00±11.11 mL; P<0.001). More central lymph nodes were dissected in the ETSPIA group compared to the TOETVA group (5.90±4.72 vs. 3.36±2.80; P=0.002). However, the difference in the number of positive central lymph nodes dissected was not statistically significant (1.38±2.33 for ETSPIA vs. 0.94±1.39 for TOETVA; P=0.26). The ETSPIA group had a shorter length of stay (LOS) compared to the TOETVA group (6.82±2.02 vs. 8.26±2.72 days; P=0.003). The alteration in PTH levels 1 day after surgery was less pronounced in the ETSPIA group compared to the TOETVA group (-26.38%±18.43% vs. -35.75%±22.95%; P=0.04). At the 1-month postoperative mark, the ETSPIA group showed a marginal increase in PTH levels, whereas the TOETVA group exhibited a slight decrease (10.12%±35.43% vs. -11.53%±29.51%; P=0.03). Regarding the average percentage change in serum calcium level 1 day after surgery, the ETSPIA group showed a smaller change, though this difference was not statistically significant (-4.79%±5.47% vs. -5.66%±3.90%; P=0.40). Furthermore, the incidence of hoarseness attributable to transient recurrent laryngeal nerve (RLN) injury in postoperative patients was lower in the ETSPIA group compared to the TOETVA group, but this difference did not reach statistical significance (0% vs. 4%; P=0.15).

Conclusions

Overall, compared to TOETVA, the ETSPIA is characterized by a shorter operative route, enhanced protection of the parathyroid glands, reduced trauma, and expedited postoperative recovery.

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.

Risk factors analysis of lateral cervical lymph node metastasis in papillary thyroid carcinoma: a retrospective study of 830 patients

OBJECTIVE

The aim of this study is to investigate the risk factors for lateral cervical lymph node metastasis in papillary thyroid carcinoma (PTC).

METHODS

Clinicopathological data (age, gender, Hashimoto's thyroiditis, preoperative circulating tumor cells (CTCs), multifocal, maximum lesion diameter, invaded capsule, T stage, and lymph node metastasis) of 830 PTC patients diagnosed and treated in Meizhou People's Hospital from June 2021 to April 2023 were collected. The related factors of lateral cervical lymph node metastasis were analyzed.

RESULTS

There were 334 (40.2%), and 103 (12.4%) PTC patients with central lymph node metastasis, and lateral cervical lymph node metastasis, respectively. Compared with patients without lateral cervical lymph node metastasis, PTC patients with lateral cervical lymph node metastasis had a higher proportion of multifocal, maximum lesion diameter > 1 cm, invaded capsule, T3-T4 stage. Regression logistic analysis showed that male (odds ratio (OR): 2.196, 95% confidence interval (CI): 1.279-3.769, p = 0.004), age < 55 years old (OR: 2.057, 95% CI: 1.062-3.988, p = 0.033), multifocal (OR: 2.759, 95% CI: 1.708-4.458, p < 0.001), maximum lesion diameter > 1 cm (OR: 5.408, 95% CI: 3.233-9.046, p < 0.001), T3-T4 stage (OR: 2.396, 95% CI: 1.241-4.626, p = 0.009), and invaded capsule (OR: 2.051, 95% CI: 1.208-3.480, p = 0.008) were associated with lateral cervical lymph node metastasis.

CONCLUSIONS

Male, age < 55 years old, multifocal, maximum lesion diameter > 1 cm, T3-T4 stage, and invaded capsule were independent risk factors for lateral cervical lymph node metastasis in PTC.

Machine learning based on SEER database to predict distant metastasis of thyroid cancer

OBJECTIVE

Distant metastasis of thyroid cancer often indicates poor prognosis, and it is important to identify patients who have developed distant metastasis or are at high risk as early as possible. This paper aimed to predict distant metastasis of thyroid cancer through the construction of machine learning models to provide a reference for clinical diagnosis and treatment.

MATERIALS & METHODS

Data on demographic and clinicopathological characteristics of thyroid cancer patients between 2010 and 2015 were extracted from the National Institutes of Health (NIH) Surveillance, Epidemiology, and End Results (SEER) database. Our research used univariate and multivariate logistic models to screen independent risk factors, respectively. Decision Trees (DT), ElasticNet (ENET), Logistic Regression (LR), Extreme Gradient Boosting (XGBoost), Random Forest (RF), Multilayer Perceptron (MLP), Radial Basis Function Support Vector Machine (RBFSVM) and seven machine learning models were compared and evaluated by the following metrics: the area under receiver operating characteristic curve (AUC), calibration curve, decision curve analysis (DCA), sensitivity(also called recall), specificity, precision, accuracy and F1 score. Interpretable machine learning was used to identify possible correlation between variables and distant metastasis.

RESULTS

Independent risk factors for distant metastasis, including age, gender, race, marital status, histological type, capsular invasion, and number of lymph nodes metastases were screened by multifactorial regression analysis. Among the seven machine learning algorithms, RF was the best algorithm, with an AUC of 0.948, sensitivity of 0.919, accuracy of 0.845, and F1 score of 0.886 in the training set, and an AUC of 0.960, sensitivity of 0.929, accuracy of 0.906, and F1 score of 0.908 in the test set.

CONCLUSIONS

The machine learning model constructed in this study helps in the early diagnosis of distant thyroid metastases and helps physicians to make better decisions and medical interventions.

Predictive Value of Jugulo-omohyoid Lymph Nodes in Lateral Lymph Node Metastasis of Papillary Thyroid Cancer

BACKGROUND

Jugulo-omohyoid lymph nodes (JOHLN) metastasis has proven to be associated with lateral lymph node metastasis (LLNM). This study aimed to reveal the clinical features and evaluate the predictive value of JOHLN in PTC to guide the extent of surgery.

METHODS

A total of 550 patients pathologically diagnosed with PTC between October 2015 and January 2020, all of whom underwent thyroidectomy and lateral lymph node dissection, were included in this study.

RESULTS

Thyroiditis, tumor location, tumor size, extra-thyroidal extension, extra-nodal extension, central lymph node metastasis (CLNM), and LLMM were associated with JOHLN. Male, upper lobe tumor, multifocality, extra-nodal extension, CLNM, and JOHLN metastasis were independent risk factors from LLNM. A nomogram based on predictors performed well. Nerve invasion contributed the most to the prediction model, followed by JOHLN metastasis. The area under the curve (AUC) was 0.855, and the p-value of the Hosmer-Lemeshow goodness of fit test was 0.18. Decision curve analysis showed that the nomogram was clinically helpful.

CONCLUSION

JOLHN metastasis could be a clinically sensitive predictor of further LLM. A high-performance nomogram was established, which can provide an individual risk assessment of LNM and guide treatment decisions for patients.

Analysis of risk factors for lateral lymph node metastasis in T1 stage papillary thyroid carcinoma: a retrospective cohort study

Background

The occurrence of cervical lymph node metastasis in T1 stage papillary thyroid carcinoma (PTC) is frequently observed. Notably, lateral lymph node metastasis (LLNM) emerges as a critical risk factor adversely affecting prognostic outcomes in PTC. The primary aim of this investigation was to delineate the risk factors associated with LLNM in the initial stages of PTC.

Methods

This retrospective analysis encompassed 3,332 patients diagnosed with T1 stage PTC without evident LLNM at the time of diagnosis. These individuals underwent primary surgical intervention at West China Hospital, Sichuan University between June 2017 and February 2023. The cohort was divided into two groups: patients manifesting LLNM and those without metastasis at the time of surgery. Additionally, T1 stage PTC patients were subdivided into T1a and T1b categories. Factors influencing LLNM were scrutinized through both univariate and multivariate analyses.

Results

The incidence of LLNM was observed in 6.2% of the cohort (206 out of 3,332 patients). Univariate analysis revealed significant correlations between LLNM and male gender (P<0.001), tumor localization in the upper lobe (P<0.001), maximal volume of the primary tumor (P<0.001), largest tumor diameter (P<0.001), multifocality (P<0.001), and bilaterality (P<0.001), with the exception of age (P=0.788) and duration of active surveillance (AS) (P=0.978). Multivariate logistic regression analysis identified male gender (P<0.001), upper lobe tumor location (P<0.001), maximal primary tumor volume (P<0.001), and multifocality (P<0.001) as independent predictors of LLNM. However, age categories (≤55, >55 years), maximum tumor diameter, bilaterality, and surveillance duration did not exhibit a significant impact. Comparative analyses between T1a and T1b subgroups showed congruent univariate results but revealed differences in multivariate outcomes. In the T1a subgroup, gender, tumor location, and multifocality (all P<0.05) were associated with elevated LLNM risk. Conversely, in the T1b subgroup, tumor location, dimensions, and multifocality (all P<0.05) were significant predictors of LLNM risk, whereas gender (P=0.097) exerted a marginal influence.

Conclusions

The investigation highlights several key risk factors for LLNM in T1 stage PTC patients, including gender, upper lobe tumor location, larger tumor size, and multifocality. Conversely, prolonged AS and younger age did not significantly elevate LLNM risk, suggesting the viability of AS as a strategic option in selected cases.

Risk Factors and Prediction Models for Postoperative Pathologically Malignant TI-RADS 3 Thyroid Nodules

Objective: Our goal was to detect the risk factors for malignant TI-RADS 3 nodule and to construct a predictive model. Patients and Methods: All 199 patients with TI-RADS 3 nodule underwent first-time thyroid surgery from January 2018 to September 2021. Univariate analysis identified potential risk covariates and then incorporated these covariates into multivariate logistic regression to determine the risk factors for malignant TI-RADS 3 nodule and construct a predictive model. Results: Binary logistic regression analysis showed that age [odds ratio (OR): 0.926, 95% CI: 0.865-0.992; P = .029), low level of parathyroid hormone (OR: 0.940, 95% CI: 0.890-0.993; P = .027), and preoperative ultrasound features of TI-RADS 3 nodule, such as echogenicity (OR: 8.496, 95% CI: 1.377-52.406; P = .021), echogenic foci (OR: 8.611, 95% CI: 1.484-49.957; P = .016), and maximum tumor diameter (OR: 0.188, 95% CI: 0.040-0.888; P = .035) were independent risk factors for malignant TI-RADS 3 nodule. Based on these independent risk factors, a logistic regression model was established. The area under curve of the prediction model for TI-RADS 3 thyroid cancer was 0.921 (95% CI: 0.856-0.986, P < 0.001). The maximum Youden index was 0.698. The cut-off value, sensitivity, and specificity were 0.074, 84.6%, and 85.2%, respectively. Conclusion: Young age, iso/hypo/very hypo echo, echogenic foci, nodule diameter <30 mm, and low level of PTH are independent risk factors for TI-RADS 3 thyroid carcinomas. This prediction model has a high sensitivity and specificity. A prediction model value of more than 0.074 implies that the TI-RADS 3 nodule has undergone a malignant transformation, and fine needle aspiration is recommended in these cases.

Killing capacity analysis of tumor-infiltrating cytotoxic lymphocytes and impact on lymph node metastasis in differentiated papillary carcinoma of thyroid with the BRAF V600E mutation

BACKGROUND

Cytotoxic lymphocytes (CLs) express potent toxins, including perforin (P) and granzyme-B (G), which brings about target cell death. The purpose of this study was to evaluate the killing capacity of tumor-infiltrating CLs by means of P and G analysis, and explore the association with lymph node metastasis in papillary carcinoma of thyroid (PTC) without Hashimoto's thyroiditis (HT).

METHODS

Infiltration of lymphocytes in PTC was observed in frozen sections. Both fresh tumor tissues and paracancerous tissues with lymphocyte infiltration were collected and prepared into a single cell suspension. Flow cytometry was used to detect the percentages of CD3+P+, CD3+G+, CD8+P+, and CD8+G+ T lymphocytes (TLs) and CD16-CD56+P+ and CD16-CD56+G+ natural killer (NK) cells. Finally, we investigated differential expression of P and G in NK cells and cytotoxic T lymphocytes (CTLs) in paired tumor tissues (group T, n = 44) and paracancerous tissues (group N, n = 44) from patients with PTC with the BRAF V600E mutation. Furthermore, patients were divided into two groups according to whether cervical central lymph node metastasis (CCLNM) existed: group A (with lymph node metastases, n = 27) and group B (with nonlymph node metastases, n = 17). Patients were also divided into three groups according to the total number of positive CCLNM: group B, group C (with low-level lymph node metastases, less than 5, n = 17) and group D (with high-level lymph node metastases, no less than 5, n = 10).

RESULTS

The percentage of CD3+P+ CTLs was significantly higher in group N than in group T (P < 0.05). The percentage of CD8+G+ CTLs was significantly higher in group T than in group N (P < 0.05). The percentages of CD3+G+, CD16-CD56+P+and CD16-CD56+G+ NK cells showed no significant difference in either group T or group N (P > 0.05). The percentages of CD3+P+ CTLs in group A and group C were significantly higher in the paracancerous tissue than in the tumor tissue (P < 0.05). The percentages of CD8+G+ CTLs in group A and group C were significantly higher in the tumor tissues than in the paracancerous tissues (P < 0.05). The percentage of CD16-CD56+G+ NK cells in group D was significantly higher in the tumor tissues than in the paracancerous tissues (P < 0.05).

CONCLUSIONS

The killing capacity of infiltrating CLs in PTC differed between tumor tissues and paracancerous tissues. In cases with CCLNM, higher expression of CD16-CD56+G+ NK cells in tumor tissues may be associated with a high risk of lymph node metastasis.

Distinct risk factors of lateral lymph node metastasis in patients with papillary thyroid cancer based on age stratification

INTRODUCTION

Studies have revealed that age is associated with the risk of lateral lymph node metastasis (LLNM) in papillary thyroid cancer (PTC). This study aimed to identify the optimal cut point of age for a more precise prediction model of LLNM and to reveal differences in risk factors between patients of distinct age stages.

METHODS

A total of 499 patients who had undergone thyroidectomy and lateral neck dissection (LND) for PTC were enrolled. The locally weighted scatterplot smoothing (LOWESS) curve and the 'changepoint' package were used to identify the optimal age cut point using R. Multivariate logistic regression analysis was performed to identify independent risk factors of LLNM in each group divided by age.

RESULTS

Younger patients were more likely to have LLNM, and the optimal cut points of age to stratify the risk of LLNM were 30 and 45 years old. Central lymph node metastasis (CLNM) was a prominent risk factor for further LNM in all patients. Apart from CLNM, sex(p = 0.033), tumor size(p = 0.027), and tumor location(p = 0.020) were independent predictors for patients younger than 30 years old; tumor location(p = 0.013), extra-thyroidal extension(p < 0.001), and extra-nodal extension(p = 0.042) were independent risk factors for patients older than 45 years old.

CONCLUSIONS

Our study could be interpreted as an implication for a change in surgical management. LND should be more actively performed when CLNM is confirmed; for younger patients with tumors in the upper lobe and older patients with extra-thyroidal extension tumors, more aggressive detection of the lateral neck might be considered.

Prospective application of a prediction model for lateral lymph node metastasis in papillary thyroid cancer patients with central lymph node metastasis

Objective

This study aimed to develop and apply a prediction model to estimate the probability of lateral lymph node metastasis (LLNM) in patients with cN0 unilateral papillary thyroid carcinoma (PTC) with central lymph node metastasis (CLNM).

Setting

All study data were collected from a single tertiary hospital.

Methods

Univariable and multivariable logistic regression analyses were used to explore independent predictors of LLNM in the derivation and internal validation cohorts, which were used to construct and validate a nomogram. Another 96 patients were included prospectively to evaluate the efficacy of this nomogram.

Results

Maximum tumor diameter greater than 1.0 cm (OR, 2.712; 95% CI, 1.412-5.210), multifocality (OR, 2.758; 95% CI, 1.120-6.789), the number of CLNM ≥3 (OR, 2.579; 95% CI, 1.315-5.789), CLNM ratio ≥0.297 (OR, 2.905; 95% CI, 1.396-6.043), and tumors located in the upper portion (OR 2.846, 95% CI 1.151-7.039) were independent predictors associated with LLNM. The prediction model showed excellent discrimination with an AUC of 0.731 (95% CI, 0.635-0.827). Novel risk stratification for LLNM was constructed based on this nomogram. In the prospective cohort, we stratified these patients into three risk subgroups: low-, moderate-, and high-risk subgroups and we found that the probability of LLNM was positively correlated with the total points from the nomogram.

Conclusion

This nomogram was applied in prospective clinical practice and distinguished PTC patients with a genuinely high risk of LLNM. Surgeons can use our nomogram to tailor the surgical plan and to credibly determine further postoperative therapy.

Clinical features and imaging examination assessment of cervical lymph nodes for thyroid carcinoma

BACKGROUNDS

The purpose of this study is to investigate the relationship between clinical characteristics and cervical lymph node metastasis (LNM) in patients with thyroid carcinoma, as well as estimate the preoperative diagnosis values of ultrasound (US) and contrast enhanced computed tomography (CECT) examinations on the neck for detection of cervical LNM in thyroid carcinoma.

METHODS

A retrospective analysis of 3 026 patients with surgically proven thyroid carcinoma was conducted. Patients' clinical characteristics, including gender, age, tumor size, bilateral lesions, multifocality, adenomatous nodules, Hashimoto's thyroiditis (HT), and extrathyroidal extension, were collected to explore their association with cervical LNM in thyroid carcinoma. Preoperative assessments for central lymph node metastasis (CLNM) and lateral lymph node metastasis (LLNM) were conducted through US and CECT. The diagnostic value of US, CECT and US combined with CECT for detection of LNM located in various cervical compartments was estimated based on the pathological results.

RESULTS

The risk of cervical LNM was higher in thyroid cancer patients who were male, age < 55 years old, tumor size > 10 mm, bilateral lesions, and extrathyroidal extension, while multifocality, adenomatous nodules and HT had no significant effect on LNM. US, CECT and US combined with CECT all had a higher sensitivity to LLNM (93.1%, 57.8%, 95.4%) than to CLNM (32.3%, 29.0%, 43.4%). US and CECT had a high specificity to both CLNM and LLNM (94.3-97.8%).

CONCLUSION

Preoperative clinical characteristics and imaging examinations on patients with thyroid carcinoma are crucial to the evaluation of cervical lymph nodes and conducive to individualizing surgical treatments by clinicians. US combined with CECT are superior to single US or CECT alone in detection of CLNM and LLNM.

Exploration and validation of key genes associated with early lymph node metastasis in thyroid carcinoma using weighted gene co-expression network analysis and machine learning

Background

Thyroid carcinoma (THCA), the most common endocrine neoplasm, typically exhibits an indolent behavior. However, in some instances, lymph node metastasis (LNM) may occur in the early stages, with the underlying mechanisms not yet fully understood.

Materials and methods

LNM potential was defined as the tumor's capability to metastasize to lymph nodes at an early stage, even when the tumor volume is small. We performed differential expression analysis using the 'Limma' R package and conducted enrichment analyses using the Metascape tool. Co-expression networks were established using the 'WGCNA' R package, with the soft threshold power determined by the 'pickSoftThreshold' algorithm. For unsupervised clustering, we utilized the 'ConsensusCluster Plus' R package. To determine the topological features and degree centralities of each node (protein) within the Protein-Protein Interaction (PPI) network, we used the CytoNCA plugin integrated with the Cytoscape tool. Immune cell infiltration was assessed using the Immune Cell Abundance Identifier (ImmuCellAI) database. We applied the Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine (SVM), and Random Forest (RF) algorithms individually, with the 'glmnet,' 'e1071,' and 'randomForest' R packages, respectively. Ridge regression was performed using the 'oncoPredict' algorithm, and all the predictions were based on data from the Genomics of Drug Sensitivity in Cancer (GDSC) database. To ascertain the protein expression levels and subcellular localization of genes, we consulted the Human Protein Atlas (HPA) database. Molecular docking was carried out using the mcule 1-click Docking server online. Experimental validation of gene and protein expression levels was conducted through Real-Time Quantitative PCR (RT-qPCR) and immunohistochemistry (IHC) assays.

Results

Through WGCNA and PPI network analysis, we identified twelve hub genes as the most relevant to LNM potential from these two modules. These 12 hub genes displayed differential expression in THCA and exhibited significant correlations with the downregulation of neutrophil infiltration, as well as the upregulation of dendritic cell and macrophage infiltration, along with activation of the EMT pathway in THCA. We propose a novel molecular classification approach and provide an online web-based nomogram for evaluating the LNM potential of THCA (http://www.empowerstats.net/pmodel/?m=17617_LNM). Machine learning algorithms have identified ERBB3 as the most critical gene associated with LNM potential in THCA. ERBB3 exhibits high expression in patients with THCA who have experienced LNM or have advanced-stage disease. The differential methylation levels partially explain this differential expression of ERBB3. ROC analysis has identified ERBB3 as a diagnostic marker for THCA (AUC=0.89), THCA with high LNM potential (AUC=0.75), and lymph nodes with tumor metastasis (AUC=0.86). We have presented a comprehensive review of endocrine disruptor chemical (EDC) exposures, environmental toxins, and pharmacological agents that may potentially impact LNM potential. Molecular docking revealed a docking score of -10.1 kcal/mol for Lapatinib and ERBB3, indicating a strong binding affinity.

Conclusion

In conclusion, our study, utilizing bioinformatics analysis techniques, identified gene modules and hub genes influencing LNM potential in THCA patients. ERBB3 was identified as a key gene with therapeutic implications. We have also developed a novel molecular classification approach and a user-friendly web-based nomogram tool for assessing LNM potential. These findings pave the way for investigations into the mechanisms underlying differences in LNM potential and provide guidance for personalized clinical treatment plans.

Integrating US-guided FNAB, BRAFV600E mutation, and clinicopathologic characteristics to predict cervical central lymph-node metastasis in preoperative patients with cN0 papillary thyroid carcinoma

BACKGROUND

The prevalence of cervical central lymph-node metastasis (CLNM) is high in patients with papillary thyroid carcinoma (PTC). There is considerable controversy surrounding the benefits of prophylactic central lymph-node dissection (pCLND) in patients with clinically negative central compartment lymph nodes (cN0). Therefore, it is crucial to accurately predict the likelihood of cervical CLNM before surgery to make informed surgical decisions.

METHODS

Date from 214 PTC patients (cN0) who underwent partial or total thyroidectomy and pCLND at the Guizhou Provincial People's Hospital were collected and retrospectively analyzed. They were divided into two groups in accordance with cervical CLNM or not. Their information, including clinical characteristics, ultrasound (US) features, pathological results of fine-needle aspirations biopsy (FNAB), and other characteristics of the groups, was analyzed and compared using univariate and multivariate logistic regression analyses.

RESULTS

A total of 214 patients were eligible in this study. Among them, 43.5% (93/214) of PTC patients had cervical CLNM, and 56.5% (121/214) did not. The two groups were compared using a univariate analyses, and there were no significant differences between the two groups in aspect ratio, boundary, morphology, component, and BRAFV600E (P > 0.05), and there were significant differences between gender, age, maximum tumor size, tumor location, capsule contact, microcalcifications, color Doppler flow imaging (CDFI), and Hashimoto's thyroiditis (HT) (P < 0.05). A multivariate logistic regression analysis was performed to further clarify the correlation of these indices. However, only age (OR = 2.455, P = 0.009), maximum tumor size (OR = 2.586, P = 0.010), capsule contact (OR = 3.208, P = 0.001), and CDFI (OR = 2.225, P = 0.022) were independent predictors of cervical CLNM. Combining these four factors, the area under the receiver-operating characteristic (ROC) curve for the joint diagnosis is 0.8160 (95% 0.7596-0.8725). Univariate analysis indicated that capsule contact (P = 0.001) was a possible predictive factor of BRAFV600E mutation.

CONCLUSIONS

In conclusion, four independent predictors of cervical CLNM, including age < 45 years, tumor size > 1.0 cm, capsule contact, and rich blood flow, were screened out. Therefore, a comprehensive assessment of these risk factors should be conducted when designing individualized treatment regimens for PTC patients.

Risk factors for central lymph node metastasis in patients with papillary thyroid carcinoma: a retrospective study

Introduction

Thyroid cancer is the most prevalent endocrine malignancy, with its global incidence increasing annually in recent years. Papillary carcinoma is the most common subtype, frequently accompanied by cervical lymph node metastasis early on. Central lymph node metastasis (CLNM) is particularly the common metastasis form in this subtype, and the presence of lymph node metastasis correlates strongly with tumor recurrence. However, effective preoperative assessment methods for CLNM in patients with papillary thyroid carcinoma (PTC) remain lacking.

Methods

Data from 400 patients diagnosed with PTC between January 1, 2018, and January 1, 2022, at the Shandong Provincial Hospital were retrospectively analyzed. This data included clinicopathological information of the patients, such as thyroid function, BRAF V600E mutation, whether complicated with Hashimoto's thyroiditis, and the presence of capsular invasion. Univariate and multivariate logistic regression analyses were performed to assess the risk factors associated with cervical CLNM in patients with PTC. Subsequently, a clinical prediction model was constructed, and prognostic risk factors were identified based on univariate and multivariate Cox regression analyses.

Results

Univariate and multivariate analyses identified that age >45 years (P=0.014), body mass index ≥25 (P=0.008), tumor size ≥1 cm (P=0.001), capsular invasion (P=0.001), and the presence of BRAF V600E mutation (P<0.001) were significantly associated with an increased risk of CLNM. Integrating these factors into the nomogram revealed an area-under-the-curve of 0.791 (95% confidence interval 0.735-0.846) and 0.765 (95% confidence interval: 0.677-0.852) for the training and validation sets, respectively, indicating strong discriminative abilities. Subgroup analysis further confirmed that patients with papillary thyroid microcarcinoma and BRAF V600E mutations who underwent therapeutic central compartment neck dissection had significantly better 3-year disease-free survival than those who had prophylactic central compartment neck dissection (P<0.001).

Conclusion

The study revealed that age >45 years, body mass index ≥25, tumor size ≥1 cm, BRAF V600E mutation, and capsular invasion are the related risk factors for CLNM in patients with PTC. For patients with clinically nodal-negative (cN0) papillary thyroid microcarcinoma, accurately identifying the BRAF V600E mutation is essential for guiding the central lymph node dissection approach and subsequent treatments.

Predictive Factors Affecting the Development of Lateral Lymph Node Metastasis in Papillary Thyroid Cancer

Objectives

Lateral lymph node metastasis (LLNM) in papillary thyroid cancer (PTC) determines the extent of surgery to be performed and the prognosis of the disease. In this study, we aimed to evaluate the clinicopathological risk factors affecting the development of LLNM.

Methods

We retrospectively evaluated the demographic and clinicopathological data of 346 cases with PTC who were operated in our clinic between May 2012 and September 2020. The patients were divided into 2 groups as patients with LLNM (Group 1) and without LLNM (Group 2).

Results

Thirty-six (10.4%) patients out of 346 patients with PTC had LLNM. A statistically significant difference was found between Group 1 and Group 2 regarding the male gender (M/F: 38.9% vs. 21.6%; p=0.020), tumor size (2.30±1.99 cm vs. 1.31±1.40 cm; p=0.000), lymphovascular invasion (69.4 vs. 20.6%; p=0.000), multicentricity (69.4% vs. 35.5%; p=0.000), multifocality (p=0.000), aggressive variant (22.2% vs. 9.4%; p=0.000), extrathyroidal extension (50% vs. 16.1% p=0.000), central lymph node metastasis (CLNM) rates (75% vs. 6.5%; p=0.000), and ≥3 cm lymph node metastasis (48.5% vs. 0%, p=0.000), distant metastasis (2.1% vs. 0%, p=0.000), respectively. Multivariance analysis determined the presence of CLNM as an independent risk factor for the development of LLNM.

Conclusion

The presence of CLNM in patients with PTC was determined as an independent risk factor for the development of LLNM. Although there has been increasing debate about prophylactic central neck dissection (pCND) in LLNM, pCND should still be considered in these patients as the rate of CLNM is high in patients with LLNM. CLNM might be a reference for surgeons to determine the extent of surgery. In addition, the presence of CLNM is important for close follow-up for the early detection of LLNM recurrence.

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.

Nomogram for preoperative estimation risk of lateral cervical lymph node metastasis in papillary thyroid carcinoma: a multicenter study

BACKGROUND

Lateral lymph node metastasis (LLNM) is frequent in papillary thyroid carcinoma (PTC) and is associated with a poor prognosis. This study aimed to developed a clinical-ultrasound (Clin-US) nomogram to predict LLNM in patients with PTC.

METHODS

In total, 2612 PTC patients from two hospitals (H1: 1732 patients in the training cohort and 578 patients in the internal testing cohort; H2: 302 patients in the external testing cohort) were retrospectively enrolled. The associations between LLNM and preoperative clinical and sonographic characteristics were evaluated by the univariable and multivariable logistic regression analysis. The Clin-US nomogram was built basing on multivariate logistic regression analysis. The predicting performance of Clin-US nomogram was evaluated by calibration, discrimination and clinical usefulness.

RESULTS

The age, gender, maximum diameter of tumor (tumor size), tumor position, internal echo, microcalcification, vascularization, mulifocality, and ratio of abutment/perimeter (A/P) > 0.25 were independently associated with LLNM metastatic status. In the multivariate analysis, gender, tumor size, mulifocality, position, microcacification, and A/P > 0.25 were independent correlative factors. Comparing the Clin-US nomogram and US features, Clin-US nomogram had the highest AUC both in the training cohort and testing cohorts. The Clin‑US model revealed good discrimination between PTC with LLNM and without LLNM in the training cohort (AUC = 0.813), internal testing cohort (AUC = 0.815) and external testing cohort (AUC = 0.870).

CONCLUSION

Our findings suggest that the ClinUS nomogram we newly developed can effectively predict LLNM in PTC patients and could help clinicians choose appropriate surgical procedures.

Evaluation of serum B7-H3 expression, ultrasound and clinical characteristics to predict the risk of cervical lymph node metastases in papillary thyroid carcinoma by nomogram

BACKGROUND

Improving the preoperative diagnosis of cervical lymph node metastasis (LNM) will help improve the clinical outcomes of papillary thyroid carcinoma (PTC) patients. B7-H3, as an immune checkpoint of the B7 family, is highly expressed in PTC tissues and related to LNM and prognosis. We aimed to explore the clinical values of serum B7-H3 (sB7-H3) in predicting LNM in PTC by a nomogram prediction model.

METHODS

From September 2019 to May 2021, a total of 344 PTC patients with primary surgery in our hospital were enrolled in this research. Enzyme-linked Immunosorbent Assay (ELISA) was used to detect sB7-H3 from the peripheral blood of PTC patients and normal controls. We created a nomogram prediction model in combination with sB7-H3 expression, clinical and ultrasound characteristics to predict LNM in the early stage.

RESULTS

Gender (p = 0.001), age (p = 0.015), tumor size (p < 0.001), number of tumors (p = 0.021) and sB7-H3 expression (p = 0.003) were independent risk factors for LNM in PTC. All the factors were included in the nomogram. The area under the curve (AUC) was 73.9% (95% CI, 68.12%-79.69%).

CONCLUSION

The nomogram is helpful in assessing the risk of LNM in PTC. sB7-H3 has excellent potential in predicting LNM in patients with PTC as an adjunctive ultrasound tool.

Developing and validating a multivariable machine learning model for the preoperative prediction of lateral lymph node metastasis of papillary thyroid cancer

Background

At present, preoperative diagnosis of lateral cervical lymph node metastasis (LLNM) in patients with papillary thyroid carcinoma (PTC) mostly depends on the training and expertise of ultrasound doctors. A machine-learning model for predicting LLNM accurately before PTC surgery may help to determine the scope of surgery and reduce unnecessary surgical trauma.

Methods

The data of patients with primary PTC who underwent thyroidectomy with lateral cervical lymph node surgery at Beijing Tongren Hospital between July 2009 and June 2021 were retrospectively analyzed. All patients had complete ultrasonic examination, clinical data, and definite pathology diagnosis of lymph nodes. LLNM was confirmed by postoperative pathology. The patients were randomly divided into a training set (155 cases) and a test set (98 cases) at a ratio of 6:4. Eleven parameters, including patient demographics, ultrasound results, and tumor-related conditions, were collected, and a prediction model was established using the support vector machine (SVM) algorithm. Several other machine-learning algorithms were also used to establish models for comparison. The accuracy, precision, recall, F1-score, sensitivity, specificity, Cohen's kappa value, and area under the receiver operating characteristic curve (AUC) were used to evaluate model performance.

Results

A total of 87 males and 156 females were included in the study, aged 14-80 years. One hundred and four patients of them had LLNM and 139 did not have LLNM. The pandas Python library was used for the statistical analysis, and the Spearman coefficient was used to analyze the correlation between each parameter and the prediction index. The SVM model performed the best among all the models. Its accuracy, precision, recall, F1-score, sensitivity, specificity, Cohen's kappa value, and AUC were 90.8%, 91.0%, 90.8%, 90.8%, 87.5%, 94.0%, 81.6%, and 91.0%, respectively.

Conclusions

This model can enable surgeons to improve the accuracy of ultrasonography in predicting LLNM without additional examination, thus avoiding missing positive lateral cervical lymph nodes and reducing the sequelae caused by unnecessary lateral neck dissection.

Efficacy, safety, and controversy of ultrasound-guided radiofrequency ablation in the treatment of T1N0M0 papillary thyroid carcinoma

Objective

To evaluate the safety effect, and controversy on the treatment outcomes of radiofrequency ablation (RFA) for T1N0M0 papillary thyroid carcinoma (PTC).

Materials and methods

This study is assessed the medical records of 142 patients with primary T1N0M0 PTC tumors after RFA between 2014 and 2022. 4 patients underwent delayed surgery (DS) after RFA and 411 T1N0M0 patients underwent DS were recorded. Outcomes were compared between RFA and DS groups after propensity score matching (PSM).

Results

The maximal diameter (MD) and volume (V) increased in months 1 (P < 0.01) and reduced after the 6-month follow-up (all P < 0.01). The disappearance and disease progression rates were 53.5% and 2.1%, respectively. The complication and disease progression rates had no significant difference between RFA and DS (P>0.05). In some cases, the tumors were not fully inactivated after RFA, and the central compartment lymph node (CCLN) were metastasis. The CCLN metastasis rate was 13.4%. MD, V and clustered calcifications were independent risk factors for CCLN metastasis by univariate analysis.

Conclusions

RFA is an effective and safe treatment option in selected patients with solitary T1N0M0 PTC. There are the risks of tumor incompletely ablated and CCLN metastasis.

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.

Nomograms for the prediction of lateral lymph node metastasis in papillary thyroid carcinoma: Stratification by size

Background

Lateral lymph node metastasis (LLNM) is a risk factor of poor prognosis in papillary thyroid cancer (PTC). We aimed to determine predictive factors and develop the nomograms for LLNM in patients with papillary thyroid microcarcinoma (PTMC) and macro-PTC.

Methods

We reviewed the medical records of 1,106 patients who underwent surgery between January 2019 and January 2022. Patients were divided into a PTMC and a macro-PTC group. We developed preoperative and postoperative nomograms for predicting LLNM based on results of multivariate analysis. Internal calibration was performed for these models.

Results

The number of metastatic lymph nodes in lateral compartment was higher in macro-PTC patients. LLNM was independently associated with gender, the number of foci, location, shape, and central lymph node metastasis (CLNM) in PTMC patients. For macro-PTC patients, chronic lymphocytic thyroiditis, the number of foci, location, margin, CLNM, and central lymph node ratio were all independent predictors for LLNM. All the above factors were incorporated into nomograms, which showed the perfect discriminative ability.

Conclusion

The diameter of the tumor has an impact on the rate of LLNM. Separate predictive systems should be used for PTMC and macro-PTC patients for more accurate clinical assessment of lateral lymph node status. Through these nomograms, we can not only detect high-risk patients with occult LLNM preoperatively, but also form appropriate treatment protocols for postoperative management of PTC patients with different risks.

Lateral Involvement in Different Sized Papillary Thyroid Carcinomas Patients with Central Lymph Node Metastasis: A Multi-Center Analysis

Objective: To quantitatively predict the probability of lateral lymph node metastasis (LLNM) for papillary thyroid carcinomas (PTC) patients with central lymph node metastasis (CLNM) in order to guide postoperative adjuvant treatment. Methods: Five hundred and three PTC patients with CLNM from three medical centers were retrospectively analyzed. Results: The LLNM rate for all patients was 23.9% (120 in 503), with 15.5% (45 in 291) and 35.4% (75 in 212) for patients with papillary thyroid microcarcinoma (PTMC) and large papillary thyroid carcinoma (LPTC), respectively. Patients with no fewer than five positive central lymph nodes (CLN) exhibited a higher risk of LLNM. For patients with fewer than five positive CLN, a maximum diameter of positive CLN > 0.5 cm and the presence of ipsilateral nodular goiter were identified as independent risk factors of LLNM for papillary thyroid microcarcinoma (PTMC) patients. The independent risk factors of LLNM for large papillary thyroid carcinoma (LPTC) patients included a tumor located in the upper portion of thyroid, maximum tumor diameter ≥ 2.0 cm, maximum diameter of positive CLN > 0.5 cm, and the presence of thyroid capsular invasion. Predictive nomograms were established based on these risk factors for PTMC and LPTC patients, respectively. The accuracy and validity of our newly built models were verified by C-index and calibration curves. PTMC and LPTC patients with fewer than five positive CLN were each stratified into three subgroups based on their nomogram risk scores, and a detailed risk stratification flow chart was established for a more accurate evaluation of LLNM risk in PTC patients. Conclusions: A detailed stratification flow chart for PTC patients with CLNM to quantitatively assess LLNM risk was established, which may aid in clinical decision-making for those patients.

Nomogram model based on preoperative serum thyroglobulin and clinical characteristics of papillary thyroid carcinoma to predict cervical lymph node metastasis

OBJECTIVE

Preoperative evaluation of cervical lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) has been one of the serious clinical challenges. The present study aims at understanding the relationship between preoperative serum thyroglobulin (PS-Tg) and LNM and intends to establish nomogram models to predict cervical LNM.

METHODS

The data of 1,324 PTC patients were retrospectively collected and randomly divided into training cohort (n = 993) and validation cohort (n = 331). Univariate and multivariate logistic regression analyses were performed to determine the risk factors of central lymph node metastasis (CLNM) and lateral lymph node metastasis (LLNM). The nomogram models were constructed and further evaluated by 1,000 resampling bootstrap analyses. The receiver operating characteristic curve (ROC curve), calibration curve, and decision curve analysis (DCA) of the nomogram models were carried out for the training, validation, and external validation cohorts.

RESULTS

Analyses revealed that age, male, maximum tumor size >1 cm, PS-Tg ≥31.650 ng/ml, extrathyroidal extension (ETE), and multifocality were the significant risk factors for CLNM in PTC patients. Similarly, such factors as maximum tumor size >1 cm, PS-Tg ≥30.175 ng/ml, CLNM positive, ETE, and multifocality were significantly related to LLNM. Two nomogram models predicting the risk of CLNM and LLNM were established with a favorable C-index of 0.801 and 0.911, respectively. Both nomogram models demonstrated good calibration and clinical benefits in the training and validation cohorts.

CONCLUSION

PS-Tg level is an independent risk factor for both CLNM and LLNM. The nomogram based on PS-Tg and other clinical characteristics are effective for predicting cervical LNM in PTC patients.

Machine learning-based dynamic prediction of lateral lymph node metastasis in patients with papillary thyroid cancer

OBJECTIVE

To develop a web-based machine learning server to predict lateral lymph node metastasis (LLNM) in papillary thyroid cancer (PTC) patients.

METHODS

Clinical data for PTC patients who underwent primary thyroidectomy at our hospital between January 2015 and December 2020, with pathologically confirmed presence or absence of any LLNM finding, were retrospectively reviewed. We built all models from a training set (80%) and assessed them in a test set (20%), using algorithms including decision tree, XGBoost, random forest, support vector machine, neural network, and K-nearest neighbor algorithm. Their performance was measured against a previously established nomogram using area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA), precision, recall, accuracy, F1 score, specificity, and sensitivity. Interpretable machine learning was used for identifying potential relationships between variables and LLNM, and a web-based tool was created for use by clinicians.

RESULTS

A total of 1135 (62.53%) out of 1815 PTC patients enrolled in this study experienced LLNM episodes. In predicting LLNM, the best algorithm was random forest. In determining feature importance, the AUC reached 0.80, with an accuracy of 0.74, sensitivity of 0.89, and F1 score of 0.81. In addition, DCA showed that random forest held a higher clinical net benefit. Random forest identified tumor size, lymph node microcalcification, age, lymph node size, and tumor location as the most influentials in predicting LLNM. And the website tool is freely accessible at http://43.138.62.202/.

CONCLUSION

The results showed that machine learning can be used to enable accurate prediction for LLNM in PTC patients, and that the web tool allowed for LLNM risk assessment at the individual level.