Nomogram for predicting central lymph node metastasis in papillary thyroid microcarcinoma: A retrospective cohort study of 8668 patients

A nomogram for enhanced risk stratification for predicting cervical lymph node metastasis in papillary thyroid carcinoma patients

BACKGROUND

Cervical lymph node metastasis (CLNM) significantly impacts the prognosis of papillary thyroid carcinoma (PTC) patients. Accurate CLNM prediction is crucial for surgical planning and patient outcomes. This study aimed to develop and validate a nomogram-based risk stratification system to predict CLNM in PTC patients.

METHODS

This retrospective study included 1069 patients from Zhongshan Hospital and 253 from the Qingpu Branch of Zhongshan Hospital. Preoperative ultrasound (US) data and various nodule characteristics were documented. Patients underwent lobectomy with central lymph node dissection and lateral dissection if suspicious. Multivariate logistic regression, least absolute shrinkage and selection operator (LASSO) regression, and the random forest algorithm were used to identify CLNM risk factors. A nomogram was constructed and validated internally and externally. Model performance was assessed via receiver operating characteristic (ROC) curves, calibration plots, DeLong's test, decision curve analysis (DCA), and the clinical impact curve (CIC).

RESULTS

Six independent CLNM risk factors were identified: age, sex, tumor size, calcification, internal vascularity, and US-reported CLNM status. The model's area under the curve (AUC) was 0.77 for both the training and the external validation sets. Calibration plots and Hosmer‒Lemeshow (HL) tests showed good calibration. The optimal cutoff value was 0.57, with a sensitivity of 58.02% and a specificity of 83.43%. Risk stratification on the basis of the nomogram categorized patients into low-, intermediate-, and high-risk groups, effectively differentiating the likelihood of CLNM, and an online calculator was created for clinical use.

CONCLUSION

The nomogram accurately predicts CLNM risk in PTC patients, aiding personalized surgical decisions and improving patient management.

The survival after thyroidectomy versus lobectomy in multifocal papillary thyroid microcarcinoma patients

BACKGROUND

The extent of thyroid surgery for multifocal papillary thyroid microcarcinoma (PTMC) remains controversial. Studies on the optimal surgical approach for a multifocal PTMC are scarce. This study aimed to compare the effectiveness of thyroidectomy and lobectomy for the treatment of multifocal PTMC.

METHODS

A population-based retrospective cohort of patients with multifocal PTMC was analyzed using the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2017, and divided into two groups (thyroidectomy, lobectomy) based on the surgical approach. The clinicopathologic features and survival outcomes were compared between the two groups. Cox proportional hazards regression analysis to explore prognostic factors of survival. Propensity score matching (PSM) was used to balance covariates.

RESULTS

Overall, a total of 9387 multifocal PTMC patients were included in the study. Among them, 8,107 (86.36%) patients received thyroidectomy, and 1280 (13.64%) patients underwent lobectomy. Compared to patients in the thyroidectomy group, patients in the lobectomy group were diagnosed with older age (50.47 years vs. 49.32 years, p = 0.003), a higher proportion of males (20.47% vs. 14.99%, p < 0.001), larger tumors (6.22 mm vs. 4.97 mm, p < 0.001), and more frequently underwent radiotherapy (35.40% vs. 10.16%, p < 0.001). Multivariate Cox regression analysis revealed that age was the only independent prognostic factor for thyroid cancer-specific survival (TCSS), and the determinants of overall survival (OS) were age and gender. Unadjusted survival analysis revealed no difference between the two treatment groups in TCSS (p = 0.598) and OS (p = 0.126). After 1:1 Propensity Score Matching (PSM), there was still no difference in TCSS (p = 0.368) or OS (p = 0.388). The stratified analysis revealed that for patients aged under or above 55, thyroidectomy was not associated with superior BCSS or OS (p > 0.05).

CONCLUSIONS

Thyroidectomy was not associated with improved survival compared to thyroid lobectomy for patients with multifocal PTMC.

Accurate preoperative prediction of nodal metastasis in papillary thyroid microcarcinoma: Towards optimal management of patients

OBJECTIVE

To enhance the accuracy in predicting lymph node metastasis (LNM) preoperatively in patients with papillary thyroid microcarcinoma (PTMC), refining the "low-risk" classification for tailored treatment strategies.

METHODS

This study involves the development and validation of a predictive model using a cohort of 1004 patients with PTMC undergoing thyroidectomy along with central neck dissection. The data was divided into a training cohort (n = 702) and a validation cohort (n = 302). Multivariate logistic regression identified independent LNM predictors in PTMC, leading to the construction of a predictive nomogram model. The model's performance was assessed through ROC analysis, calibration curve analysis, and decision curve analysis.

RESULTS

Identified LNM predictors in PTMC included age, tumor maximum diameter, nodule-capsule distance, capsular contact length, bilateral suspicious lesions, absence of the lymphatic hilum, microcalcification, and sex. Especially, tumors larger than 7 mm, nodules closer to the capsule (less than 3 mm), and longer capsular contact lengths (more than 1 mm) showed higher LNM rates. The model exhibited AUCs of 0.733 and 0.771 in the training and validation cohorts respectively, alongside superior calibration and clinical utility.

CONCLUSION

This study proposes and substantiates a preoperative predictive model for LNM in patients with PTMC, honing the precision of "low-risk" categorization. This model furnishes clinicians with an invaluable tool for individualized treatment approach, ensuring better management of patients who might be proposed observation or ablative options in the absence of such predictive information.

A nomogram for predicting adverse pathologic features in low-risk papillary thyroid microcarcinoma

BACKGROUND

Identifying risk factors for adverse pathologic features in low-risk papillary thyroid microcarcinoma (PTMC) can provide valuable insights into the necessity of surgical or non-surgical treatment. This study aims to develop a nomogram for predicting the probability of adverse pathologic features in low-risk PTMC patients.

METHODS

A total of 662 patients with low-risk PTMC who underwent thyroid surgery were retrospectively analyzed in Qilu Hospital of Shandong University from May 2019 to December 2021. Logistic regression analysis was used to determine the risk factors for adverse pathologic features, and a nomogram was constructed based on these factors.

RESULTS

Most PTMC patients with these adverse pathologic features had tumor diameters greater than 0.6 cm (p < 0.05). Other factors (age, gender, family history of thyroid cancer, history of autoimmune thyroiditis, and BRAFV600E mutation) had no significant correlation with adverse pathologic features (p > 0.05 each). The nomogram was drawn to provide a quantitative and convenient tool for predicting the risk of adverse pathologic features based on age, gender, family history of thyroid cancer, autoimmune thyroiditis, tumor size, and BRAFV600E mutation in low-risk PTMC patients. The areas under curves (AUC) were 0.645 (95% CI 0.580-0.702). Additionally, decision curve analysis (DCA) and calibration curves were used to evaluate the clinical benefits of this nomogram, presenting a high net benefit.

CONCLUSION

Tumor size > 0.60 cm was identified as an independent risk factor for adverse pathologic features in low-risk PTMC patients. The nomogram had a high predictive value and consistency based on these factors.

Preoperative risk stratification for patients with ≤ 1 cm papillary thyroid carcinomas based on preoperative blood inflammatory markers: construction of a dynamic predictive model

Objective

The aim of this study was to investigate the relationships and predictive value of preoperative peripheral blood inflammatory markers as a means by which to assess risk for patients with ≤ 1 cm papillary thyroid carcinomas (PTCs). In addition, a preoperative risk stratification predictive model was constructed and validated.

Methods

Clinical and pathologic data, as well as preoperative blood specimens, were collected from patients who underwent initial thyroid cancer surgery at the Hangzhou First People's Hospital, from January 2014 to January 2023. Risk assessment was performed based on postoperative pathology according to the 2015 ATA guidelines for recurrence risk stratification. Using univariate analysis and multivariate logistic regression, we identified independent risk factors associated with risk stratification. A predictive model was established and its discriminative and calibration abilities were validated. An independent validation dataset was used to verify the model, and the model was deployed as an online calculator.

Results

A total of 1326 patients were included in the study, with 1047 cases (79.0%) classified as low risk and 279 cases (21.0%) classified as intermediate to high risk. The modeling group consisted of 981 cases, through univariate analysis and multivariate logistic regression analysis, preoperative blood Neutrophil/Lymphocyte Ratio (NLR), gender, tumor diameter, and multifocality were identified as independent risk factors that distinguished between low and intermediate to high risk patients with ≤ 1 cm PTCs. The clinical predictive model exhibited an AUC of 0.785, specificity of 70.6%, and sensitivity of 75.8%. For the independent validation group of 345 patients, the AUC was 0.813, specificity was 83.8%, and sensitivity was 70.4%. The calibration curve and clinical decision curve indicate that the model demonstrates excellent calibration performance.

Conclusion

A dynamic clinical predictive model based on preoperative blood NLR and clinical information for patients with ≤ 1 cm PTCs was established. The model is useful for preoperative risk assessment of patients with ≤ 1 cm PTCs.

Central lymph nodes in frozen sections can effectively guide extended lymph node resection for papillary thyroid carcinoma

OBJECTIVES

The scope of lateral neck lymph node dissection (LND) in papillary thyroid carcinoma (PTC) remains controversial. Our research aimed to explore the value of central lymph node metastasis (CLNM) in frozen sections for predicting neck lateral lymph node metastasis (NLLNM) and to guide clinical surgeons in performing surgical lymph node dissection.

PATIENTS

A total of 275 patients with PTC with suspected 'Cervical lymph node metastasis (LNM, including CLNM and NLLNM)' underwent unilateral or bilateral thyroidectomy and an intraoperative frozen diagnosis of central lymph nodes (LNs), as well as central and neck lateral LND. Validity indices and consistency of central LNs in frozen sections were calculated. In total, 216 patients then met the inclusion criteria and were enrolled in the follow-up study. The clinical and pathological data of the patients were retrospectively analyzed. The relationship between the number, metastatic diameter, and the ratio of CLNM to NLLNM was investigated.

RESULTS

CLNM in frozen and paraffin-embedded sections was associated with NLLNM. Univariate and multivariate analyses revealed the following risk factors for NLLNM metastasis: maximum diameter, total number, and ratio of metastatic LNs. A significant result was obtained when a cut-off value of 2.050 mm for the maximum metastatic diameter, 5.5 in the total number, and 0.5342 for the CLNM ratio level was used. Interaction term analyses showed that the association between the number of CLNM and NLLNM differed according to maximum diameter.

CONCLUSION

Central LNs in frozen sections accurately predicted NLLNM. In patients with PTC with >5 CLNMs, ≥2 and ≤5 CLNMs and maximum metastatic diameter > 2 mm, neck lateral LND should be considered. Our findings will facilitate the identification of patients who are likely to benefit from extended lateral neck LND.

Safety and efficacy of microwave ablation for the treatment of low-risk papillary thyroid microcarcinoma: a prospective multicenter study

OBJECTIVES

To assess the safety and efficacy of ultrasound-guided thermal ablation for low-risk papillary thyroid microcarcinoma (PTMC) via a prospective multicenter study.

METHODS

From January 2017 through June 2021, low-risk PTMC patients were screened. The management details of active surveillance (AS), surgery, and thermal ablation were discussed. Among patients who accepted thermal ablation, microwave ablation (MWA) was performed. The main outcome was disease-free survival (DFS). The secondary outcomes were tumor size and volume changes, local tumor progression (LTP), lymph node metastasis (LNM), and complication rate.

RESULTS

A total of 1278 patients were included in the study. The operation time of ablation was 30.21 ± 5.14 min with local anesthesia. The mean follow-up time was 34.57 ± 28.98 months. Six patients exhibited LTP at 36 months, of whom 5 patients underwent a second ablation, and 1 patient received surgery. The central LNM rate was 0.39% at 6 months, 0.63% at 12 months, and 0.78% at 36 months. Of the 10 patients with central LNM at 36 months, 5 patients chose ablation, 3 patients chose surgery and the other 2 patients chose AS. The overall complication rate was 1.41%, and 1.10% of patients developed hoarseness of the voice. All of the patients recovered within 6 months.

CONCLUSIONS

Thermal ablation of low-risk PTMC was observed to be safe and efficacious with few minor complications. This technique may help to bridge the gap between surgery and AS as treatment options for patients wishing to have their PTMC managed in a minimally invasive manner.

CLINICAL RELEVANCE STATEMENT

This study proved that microwave ablation is a safe and effective treatment method for papillary thyroid microcarcinoma.

KEY POINTS

Percutaneous US-guided microwave ablation of papillary thyroid microcarcinoma is a very minimally invasive treatment under local anesthesia during a short time period. The local tumor progression and complication rate of microwave ablation in the treatment of papillary thyroid microcarcinoma are very low.

Risk factor analysis and prediction model for papillary thyroid carcinoma with lymph node metastasis

Objective

We aimed to identify the clinical factors associated with lymph node metastasis (LNM) based on ultrasound characteristics and clinical data, and develop a nomogram for personalized clinical decision-making.

Methods

A retrospective analysis was performed on 252 patients with papillary thyroid carcinoma (PTC). The patient's information was subjected to univariate and multivariate logistic regression analyses to identify risk factors. A nomogram to predict LNM was established combining the risk factors. The performance of the nomogram was evaluated using receiver operating characteristic (ROC) curve, calibration curve, cross-validation, decision curve analysis (DCA), and clinical impact curve.

Results

There are significant differences between LNM and non-LNM groups in terms of age, sex, tumor size, hypoechoic halo around the nodule, thyroid capsule invasion, lymph node microcalcification, lymph node hyperechoic area, peak intensity of contrast (PI), and area under the curve (AUC) of the time intensity curve of contrast (P<0.05). Age, sex, thyroid capsule invasion, lymph node microcalcification were independent predictors of LNM and were used to establish the predictive nomogram. The ROC was 0.800, with excellent discrimination and calibration. The predictive accuracy of 0.757 and the Kappa value was 0.508. The calibration curve, DCA and calibration curve demonstrated that the prediction model had excellent net benefits and clinical practicability.

Conclusion

Age, sex, thyroid capsule invasion, and lymph node microcalcification were identified as significant risk factors for predicting LNM in patients with PTC. The visualized nomogram model may assist clinicians in predicting the likelihood of LNM in patients with PTC prior to surgery.

An integrated nomogram combining deep learning, clinical characteristics and ultrasound features for predicting central lymph node metastasis in papillary thyroid cancer: A multicenter study

OBJECTIVE

Central lymph node metastasis (CLNM) is a predictor of poor prognosis for papillary thyroid carcinoma (PTC) patients. The options for surgeon operation or follow-up depend on the state of CLNM while accurate prediction is a challenge for radiologists. The present study aimed to develop and validate an effective preoperative nomogram combining deep learning, clinical characteristics and ultrasound features for predicting CLNM.

MATERIALS AND METHODS

In this study, 3359 PTC patients who had undergone total thyroidectomy or thyroid lobectomy from two medical centers were enrolled. The patients were divided into three datasets for training, internal validation and external validation. We constructed an integrated nomogram combining deep learning, clinical characteristics and ultrasound features using multivariable logistic regression to predict CLNM in PTC patients.

RESULTS

Multivariate analysis indicated that the AI model-predicted value, multiple, position, microcalcification, abutment/perimeter ratio and US-reported LN status were independent risk factors predicting CLNM. The area under the curve (AUC) for the nomogram to predict CLNM was 0.812 (95% CI, 0.794-0.830) in the training cohort, 0.809 (95% CI, 0.780-0.837) in the internal validation cohort and 0.829(95%CI, 0.785-0.872) in the external validation cohort. Based on the analysis of the decision curve, our integrated nomogram was superior to other models in terms of clinical predictive ability.

CONCLUSION

Our proposed thyroid cancer lymph node metastasis nomogram shows favorable predictive value to assist surgeons in making appropriate surgical decisions in PTC treatment.

A clinical and molecular pathology prediction model for central lymph node metastasis in cN0 papillary thyroid microcarcinoma

BACKGROUND

The frequency of thyroid cancer has rapidly increased in recent years globally. Thus, more papillary thyroid microcarcinoma (PTMC) patients are being diagnosed, including clinical lymph node-negative (cN0) patients. Our study attempted to develop a prediction model for assessing the probability of central lymph node metastasis (CLNM) in cN0 PTMC patients.

METHODS

A total of 595 patients from the Affiliated Hospital of Qingdao University (training cohort: 456 patients) and the Affiliated Hospital of Jining Medical University (verification cohort: 139 patients) who underwent thyroid surgery between January 2020 and May 2022 were enrolled in this study. Their clinical and molecular pathology data were analyzed with multivariate logistic regression to identify independent factors, and then we established a prediction model to assess the risk of CLNM in cN0 PTMC patients.

RESULTS

Multivariate logistic regression analysis revealed that sex, Hashimoto's thyroiditis (HT), tumor size, extrathyroidal extension, TERT promoter mutations and NRAS mutation were independent factors of CLNM. The prediction model demonstrated good discrimination ability (C-index: 0.757 and 0.753 in the derivation and validation cohorts, respectively). The calibration curve of the model was near the optimum diagonal line, and decision curve analysis (DCA) showed a noticeably better benefit.

CONCLUSION

CLNM in cN0 PTMC patients is associated with male sex, tumor size, extrathyroidal extension, HT, TERT promoter mutations and NRAS mutation. The prediction model exhibits good discrimination, calibration and clinical usefulness. This model will help to assess CLNM risk and make clinical decisions in cN0 PTMC patients.

A machine learning-based approach to predicting the malignant and metastasis of thyroid cancer

Background

Thyroid Cancer (TC) is the most common malignant disease of endocrine system, and its incidence rate is increasing year by year. Early diagnosis, management of malignant nodules and scientific treatment are crucial for TC prognosis. The first aim is the construction of a classification model for TC based on risk factors. The second aim is the construction of a prediction model for metastasis based on risk factors.

Methods

We retrospectively collected approximately 70 preoperative demographic and laboratory test indices from 1735 TC patients. Machine learning pipelines including linear regression model ridge, Logistic Regression (LR) and eXtreme Gradient Boosting (XGBoost) were used to select the best model for predicting deterioration and metastasis of TC. A comprehensive comparative analysis with the prediction model using only thyroid imaging reporting and data system (TI-RADS).

Results

The XGBoost model achieved the best performance in the final thyroid nodule diagnosis (AUC: 0.84) and metastasis (AUC: 0.72-0.77) predictions. Its AUCs for predicting Grade 4 TC deterioration and metastasis reached 0.84 and 0.97, respectively, while none of the AUCs for Only TI-RADS reached 0.70. Based on multivariate analysis and feature selection, age, obesity, prothrombin time, fibrinogen, and HBeAb were common significant risk factors for tumor progression and metastasis. Monocyte, D-dimer, T3, FT3, and albumin were common protective factors. Tumor size (11.14 ± 7.14 mm) is the most important indicator of metastasis formation. In addition, GGT, glucose, platelet volume distribution width, and neutrophil percentage also contributed to the development of metastases. The abnormal levels of blood lipid and uric acid were closely related to the deterioration of tumor. The dual role of mean erythrocytic hemoglobin concentration in TC needs to be verified in a larger patient cohort. We have established a free online tool (http://www.cancer-thyroid.com/) that is available to all clinicians for the prognosis of patients at high risk of TC.

Conclusion

It is feasible to use XGBoost algorithm, combined with preoperative laboratory test indexes and demographic characteristics to predict tumor progression and metastasis in patients with TC, and its performance is better than that of Only using TI-RADS. The web tools we developed can help physicians with less clinical experience to choose the appropriate clinical decision or secondary confirmation of diagnosis results.

Classic and Follicular Variant of Papillary Thyroid Microcarcinoma: 2 Different Phenotypes Beyond Tumor Size

Context

Despite the wide revision of current guidelines, the management of papillary thyroid microcarcinoma (mPTC) still has to be decided case by case. There is conflicting evidence about the role of more frequent histological subtypes, and no data about potential differences at presentation.

Objective

Our aim was to compare the phenotype of the 2 most frequent mPTC variants, namely, classical papillary thyroid microcarcinoma (mPTCc) and the follicular variant of papillary thyroid microcarcinoma (mFVPTC) .

Methods

Retrospective observational study, from January 2008 to December 2017 of a consecutive series of patients with mPTCc and mFVPTC. All cases were classified according to the 2015 American Thyroid Association (ATA) risk classification. Clinical and preclinical features of mPTCc and mFVPTC at diagnosis were collected. The comparison was also performed according to the incidental/nonincidental diagnosis and differences were verified by binary logistic analysis.

Results

In total, 235 patients were eligible for the analysis (125 and 110 mPTCc and mFVPTC, respectively). Compared with mPTCc, mFVPTCs were more often incidental and significantly smaller (4 vs 7 mm) (P < .001 all), possibly influenced by the higher rate of incidental detection. mFVPTC and incidental (P < .001 both) tumors were significantly more often allocated within the low-risk class. A logistic regression model, with ATA risk class as the dependent variable, showed that both mFVPTC (OR 0.465 [0.235-0.922]; P = .028]) and incidental diagnosis (OR 0.074 [0.036-0.163]; P < .001) independently predicted ATA risk stratification.

Conclusion

mFVPTC shows some differences in diagnostic presentation compared with mPTCc, and seems to retain a significant number of favorable features, including a prevalent onset as incidental diagnosis.

Nomogram for preoperative estimation risk of cervical lymph node metastasis in medullary thyroid carcinoma

Objectives

Cervical lymph node metastasis (CLNM) is common in medullary thyroid carcinoma (MTC), but how to manage cervical lymph node involvement of clinically negative MTC is still controversial. This study evaluated the preoperative features and developed an ultrasound (US)-based nomogram to preoperatively predict the CLNM of MTC.

Materials and methods

A total of 74 patients with histologically confirmed MTC were included in this retrospective study and assigned to the CLNM-positive group and CLNM-negative group based on the pathology. The associations between CLNM and preoperative clinical and sonographic characteristics (size, location, solid component, shape, margin, echogenicity, calcification, and extracapsular invasion of the tumor) were evaluated by the use of univariable and multivariable logistic regression analysis. A nomogram to predict the risk of the CLNM of MTC was built and assessed in terms of discrimination, calibration, and clinical usefulness.

Results

The nomogram was based on three factors (tumor margin, US-reported suspicious lymph node, and extracapsular invasion US features) and exhibited good discrimination with an area under the curve (AUC) of 0.919 (95% CI, 0.856-0.932). The calibration curves of the nomogram displayed a good agreement between the probability as predicted by the nomogram and the actual CLNM incidence.

Conclusions

We constructed and validated a US-based nomogram to predict the risk of CLNM in MTC patients, which can be easily evaluated before surgery. This model is helpful for clinical decision-making.

Preoperative prediction of central neck metastasis in patients with clinical T1-2N0 papillary thyroid carcinoma

BACKGROUND

Central neck metastasis (CNM) is common in patients with papillary thyroid carcinoma (PTC). However, the prediction of CNM risk remains poorly defined, especially for patients with clinically negative lymph nodes. We developed a preoperative clinical nomogram to predict CNM risk in patients with clinical T1-2N0 (cT1-2N0) PTC.

METHODS

Data from 436 patients with unifocal cT1-2N0 PTC were available. We analyzed the association between preoperative variables and CNM using univariate and multivariate logistic regression and developed a clinical nomogram based on the multivariate regression model. The nomogram was validated externally using an independent dataset.

RESULTS

The CNM rate was 25.5%. Three clinical variables were associated with CNM, including age, gender, and tumor size. We built a CNM nomogram integrating these three variables. It had a poor index of internal discrimination (C-index, 0.655; 95% CI 0.596-0.715) and a poor index of external discrimination (C-index, 0.690; 95% CI 0.611-0.769).

CONCLUSIONS

We developed a preoperative nomogram to quantify the risk of CNM in unifocal cT1-2N0 PTC patients. However, our data showed that preoperative clinical parameters were not able to accurately predict the likelihood of CNM. Other variables need to be investigated to improve the prediction capability of this nomogram.

Ultrasound-based radiomics XGBoost model to assess the risk of central cervical lymph node metastasis in patients with papillary thyroid carcinoma: Individual application of SHAP

Objectives

A radiomics-based explainable eXtreme Gradient Boosting (XGBoost) model was developed to predict central cervical lymph node metastasis (CCLNM) in patients with papillary thyroid carcinoma (PTC), including positive and negative effects.

Methods

A total of 587 PTC patients admitted at Binzhou Medical University Hospital from 2017 to 2021 were analyzed retrospectively. The patients were randomized into the training and test cohorts with an 8:2 ratio. Radiomics features were extracted from ultrasound images of the primary PTC lesions. The minimum redundancy maximum relevance algorithm and the least absolute shrinkage and selection operator regression were used to select CCLNM positively-related features and radiomics scores were constructed. Clinical features, ultrasound features, and radiomics score were screened out by the Boruta algorithm, and the XGBoost model was constructed from these characteristics. SHapley Additive exPlanations (SHAP) was used for individualized and visualized interpretation. SHAP addressed the cognitive opacity of machine learning models.

Results

Eleven radiomics features were used to calculate the radiomics score. Five critical elements were used to build the XGBoost model: capsular invasion, radiomics score, diameter, age, and calcification. The area under the curve was 91.53% and 90.88% in the training and test cohorts, respectively. SHAP plots showed the influence of each parameter on the XGBoost model, including positive (i.e., capsular invasion, radiomics score, diameter, and calcification) and negative (i.e., age) impacts. The XGBoost model outperformed the radiologist, increasing the AUC by 44%.

Conclusions

The radiomics-based XGBoost model predicted CCLNM in PTC patients. Visual interpretation using SHAP made the model an effective tool for preoperative guidance of clinical procedures, including positive and negative impacts.

Cervical Lymph Node Metastasis Differences in Patients with Unilateral or Bilateral Papillary Thyroid Microcarcinoma: A Multi-Center Analysis

Purposes: To quantitatively predict the risk of neck lymph node metastasis for unilateral and bilateral papillary thyroid microcarcinomas (PTMC) that may guide individual treatment strategies for the neck region. Methods: A total of 717 PTMC patients from three medical centers were enrolled for analysis. Results: Bilateral PTMCs were demonstrated to be more aggressive with a much higher cervical lymph node metastasis rate including for both central (CLNM) and lateral lymph node metastasis (LLNM) when being compared to unilateral PTMCs. In unilateral PTMC, five (age < 55 years old, male, maximum tumor diameter (MTD) ≥ 0.5 cm, and the presence of thyroid capsular invasion (TCI) and multifocality) and three (maximum diameter of positive CLN (MDCLN) > 0.5 cm, the presence of multifocality and nodular goiter, iNG) factors were identified as independent risk factors for CLNM and LLNM, respectively. In bilateral PTMC, three (age < 55 and presence of TCI and multifocality in at least one side of thyroid lobe) and two (MDCLN > 0.5 cm and presence of nodular goiter (iNG)) factors were identified as independent factors for CLNM and LLNM, respectively. Predictive models of CLNM and LLNM for patients with unilateral disease and of CLNM for patients with the bilateral disease were established based on the described risk factors. Bilateral patients with positive CLNM were also stratified into different subgroups according to the presence and absence of independent risk factors. Conclusion: An evaluation system based on independent factors of CLNM and LLNM for PTMC patients with bilateral and unilateral disease was established. Our newly established evaluation system can efficaciously quantify risks of CLNM and LLNM for PTMC patients with bilateral and unilateral disease and may guide individual treatment strategy including both surgical and postoperative adjuvant treatment of the neck region for these patients.

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

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

A Mathematical Model to Assess the Effect of Residual Positive Lymph Nodes on the Survival of Patients With Papillary Thyroid Microcarcinoma

Background

Active surveillance (AS) has been considered the first-line management for patients with clinical low-risk papillary thyroid microcarcinoma (PTMC) who often have lymph node micrometastasis (m-LNM) when diagnosed. The "low-risk" and "high prevalence of m-LNM" paradox is a potential barrier to the acceptance of AS for thyroid cancer by both surgeons and patients.

Methods

Patients diagnosed with PTMC who underwent thyroidectomy with at least one lymph node (LN) examined were identified from a tertiary center database (n = 5,399). A β-binomial distribution was used to estimate the probability of missing nodal disease as a function of the number of LNs examined. Overall survival (OS) probabilities of groups with adequate and inadequate numbers of LNs examined were estimated using the Kaplan-Meier method in the Surveillance, Epidemiology, and End Results (SEER) database (n = 15,340). A multivariable model with restricted cubic splines was also used to verify the association of OS with the number of LNs examined.

Results

The risk of residual m-LNM (missed nodal disease) ranged from 31.3% to 10.0% if the number of LNs examined ranged from 1 and 7 in patients with PTMC. With 7 LNs examined serving as the cutoff value, the intergroup comparison showed that residual positive LNs did not affect OS across all patients and patients aged ≥55 years (P = 0.72 and P = 0.112, respectively). After adjusting for patient and clinical characteristics, the multivariate model also showed a slight effect of the number of LNs examined on OS (P = 0.69).

Conclusions

Even with the high prevalence, OS is not significantly compromised by persistent m-LNM in the body of patients with low-risk PTMC. These findings suggest that the concerns of LNM should not be viewed as an obstacle to developing AS for thyroid cancer. For patients with PTMC who undergo surgery, prophylactic central LN dissection does not provide a survival benefit.

The Prognosis of Skip Metastasis in Papillary Thyroid Microcarcinoma Is Better Than That of Continuous Metastasis

CONTEXT

A few papillary thyroid microcarcinomas (PTMCs) may have skip metastasis (SLNM), but the risk factors remain controversial and the prognosis is unclear.

OBJECTIVES

To investigate the incidence, lymph node metastasis (LNM) patterns, risk factors, and prognosis of SLNM in PTMCs.

METHODS

We reviewed the medical records of PTMC patients who underwent thyroid surgery in our institution. Analyses of risk factors were performed for SLNM. Recurrence-free survival (RFS) of SLNM, central lymph node metastasis (CLNM), and continuous metastasis (CLNM and lateral lymph node metastasis [CLNM + LLNM]) were compared after propensity score matching (PSM).

RESULTS

SLNM was detected in 1.7% (50/3923) and frequently involved level III (66.7%). Compared with CLNM + LLNM, SLNM had more LNM at a single level (P < 0.01) and less LNM at 2 levels (P < 0.05). A tumor size of 0.5 to 1 cm (odds ratio [OR], 2.26; 95% CI, 1.27-4.00) and location in the upper pole (OR, 3.30; 95% CI, 2.02-5.40) were independent risk factors for SLNM. A total of 910 (23.2%) PTMCs with LNM were included in the prognostic analysis. At a median follow-up of 60 months, the RFS of SLNM did not differ from that of CLNM (P = 0.10) but was significantly higher than that of CLNM + LLNM (P < 0.01) after using PSM.

CONCLUSIONS

When the tumor size is 0.5 to 1 cm or its location is in the upper pole, we must remain vigilant to SLNM in PTMC. Because its prognosis is comparable to that of only CLNM and better than that of CLNM + LLNM, less intensive treatment should be considered.

Relationship between subgroups of central and lateral lymph node metastasis in clinically node-negative papillary thyroid carcinoma

BACKGROUND

Lymph node metastasis (LNM) of papillary thyroid carcinoma (PTC) has a certain regularity and occurs first to the central lymph node and then to the lateral lymph node. The pathway of PTC LNM can guide surgical prophylactic lymph node dissection (LND) for clinical surgeons.

AIM

To investigate the relationship between subgroups of central LNM and lateral LNM in unilateral clinically node-negative PTC (cN0-PTC).

METHODS

Data were collected for 1089 PTC patients who underwent surgical treatment at the Department of Endocrine and Breast Surgery of the First Hospital of Chongqing Medical University from January 2016 to December 2017. A total of 388 unilateral cN0-PTC patients met the inclusion criteria and were enrolled in this study. The clinical and pathological data for these 388 patients who underwent total thyroidectomy + central LND + lateral LND were retrospectively analyzed. The relationship between the central LNM and lateral LNM subgroups was investigated.

RESULTS

The coincidence rate of cN0-PTC was only 30.0%.Optimal scaling regression analysis showed that sex (57.1% vs 42.9%, P = 0.026), primary tumor size (68.8% vs 31.2%, P = 0.008), tumor location (59.7% vs 40.3%, P = 0.007), extrathyroid extension (ETE) (50.6% vs 49.9%, P = 0.046), and prelaryngeal LNM (57.1% vs 42.9%, P = 0.004) were significantly associated with ipsilateral level-II LNM. Their importance levels were 0.122, 0.213, 0.172, 0.110, and 0.227, respectively. Primary tumor size (74.6% vs 30.2%, P = 0.016), pretracheal LNM (67.5% vs 32.5%, P < 0.001), and paratracheal LNM (71.4% vs 28.6%, P < 0.001) were significantly associated with ipsilateral level-III LNM. Their importance levels were 0.120, 0.408, and 0.351, respectively. Primary tumor size (72.1% vs 27.9%, P = 0.003), ETE (70.4% vs 29.6%, P = 0.016), pretracheal LNM (68.3% vs 31.7%, P=0.001), and paratracheal LNM (80.8% vs 19.2%, P < 0.001) were significantly associated with ipsilateral level-IV LNM. Their importance levels were 0.164, 0.146, 0.216, and 0.472, respectively.

CONCLUSION

The LNM pathway of thyroid cancer has a certain regularity. For unilateral cN0-PTC patients with a tumor diameter > 2 cm and pretracheal or ipsilateral paratracheal LNM, LND at ipsilateral level III and level IV must be considered. When there is a tumor in the upper third of the thyroid with prelaryngeal LNM, LND at level II, level III and level IV must be considered.

Preoperative and pathological predictive factors of central lymph node metastasis in papillary thyroid microcarcinoma

OBJECTIVE

The aim of this study was to identify clinical and pathological markers of CLNM in persons with clinical lymph node-negative papillary thyroid microcarcinoma(PTMC).

MATERIALS AND METHODS

Retrospective data were analyzed from 804 PTMC clinically negative patients who were receiving thyroid surgery in the First Affiliated Hospital at Chongqing Medical University from January 2017 to December 2018. The CLNM-positive and CLNM-negative groups were categorised according to histological evidence of the central lymph node involvement, statistically, risk variables for CLNM were found.

RESULTS

324 (40.3%) individuals were diagnosed with CLNM. Sex (P=0.001), age at diagnosis (P<0.001), tumour size(P=0.029), microcaccificities presence (P=0.003), capsules discontinuity(P=0.002), multi-focality(P=0.001) and (ETE)extrathyroidal extension (P < 0.001) differed substantially from one positive CLNM group to the next. For multivariate analyses, women (odds ratio [OR] = 0.489), age [OR = 0.540] are the independent protective factors for CLNM; micro-cacification presence (OR = 1.511), discontinuity of capsules (OR= 2.056), multifocality(OR=1.486) and ETE(OR=10.613) are the independent risk factors for CLNM. Feature curves of the receiver were built and the AUC is 0.763. 32.1% percent (80 patients) of the 249 patients who did not have any of the four risk variables got CLNM. This contrasted with the incidence of CLNM in this research, which was as high as 49.1%.

CONCLUSIONS

CLNM has been connected with female sex, age - within 45 years, microcacification occurrences, capsule discontinuity, multifocality and extrathyroid expansion. The patients may benefit from the surgical decision of pCLND whether there are risk factors combined.

Development of an Active Surveillance or Surgery Model to Predict Lymph Node Metastasis in cN0 Papillary Thyroid Microcarcinoma

OBJECTIVE

Involvement of multiple lymph node (LN) metastasis in papillary thyroid microcarcinoma (PTMC) may indicate a progressive disease. To assist treatment decision, we conducted a clinical study to develop and validate a prediction model for the preoperative evaluation of LN metastasis involving more than five lymph nodes in patients with clinical N0 (cN0) PTMC.

MATERIAL AND METHODS

Using data from 6,337 patients with cN0 PTMCs at Tianjin Medical University Cancer Institute and Hospital from 2013 to 2017, we identified and integrated risk factors for the prediction of multiple LN metastasis to build a nomogram. The predictive accuracy and discriminative ability of the nomogram were evaluated by the concordance index (C-index) and calibration curve. The model was validated using bootstrap resampling of the training cohort and an independent temporal validation cohort at the same institution.

RESULTS

In the training cohort (n = 3,209 patients), six independent risk factors were identified and included the prediction model (PTMC Active Surveillance or Surgery (ASOS) Model), including age, gender, multifocality, tumor size, calcification, and aspect ratio. The PTMC ASOS model was validated both internally and through the temporal validation cohort (n = 3,128 patients) from the same institute. The C-indexes of the prediction model in the training cohort were 0.768 (95% CI, 0.698-0.838), 0.768 and 0.771 in the internal validation and external validation cohorts, respectively. The area under the receiver operating characteristic curve (AUC) was 0.7068 and 0.6799. The calibration curve for probability of large-LN metastasis showed good agreement between prediction by nomogram and actual observation. DCA curves were used for comparison with another model, and IDI and NRI were also calculated. The cutoff value of our model was obtained by the ROC curve. Based on this model and cut point, a web-based dynamic nomogram was developed (https://tjmuch-thyroid.shinyapps.io/PTMCASOSM/).

CONCLUSION

We established a novel nomogram that can help to distinguish preoperatively cN0 PTMC patients with or without metastasis of multiple lymph nodes. This clinical prediction model may be used in decision making for both active surveillance and surgery.

Ultrasonic Characteristics Improve Prediction of Central Lymph Node Metastasis in cN0 Unifocal Papillary Thyroid Cancer

BACKGROUND

Prediction of central lymph node metastasis (CLNM) is vital for clinical decision-making processes in clinically N0 (cN0) unifocal papillary thyroid carcinoma (PTC), but the sensitivity of preoperative detection of CLNM is limited. The aim of the present study was to determine whether there are ultrasonic (US) characteristics associated with CLNM.

METHODS

In total, 1657 PTC patients (514 men and 1143 women) were enrolled in the present study between January 2018 and May 2021. The patients met the following inclusion criteria based on preoperative detection: suspected nodule confirmed as PTC by biopsy; the nodule was unifocal and less than 4 cm in diameter; no prior neck radiation exposure; no extrathyroidal extension; and no CLNM or distant metastases on imaging. All the enrolled patients underwent total thyroidectomy with prophylactic central lymph node dissection (CLND). A postoperative pathological diagnosis was made.

RESULTS

CLNM was found in 58.4% of male patients and 36.9% of female patients. In univariate analysis, size, adjacent anterior capsule, distance to the lower pole and color Doppler flow imaging (CDFI) were considered risk factors for the male and female groups (p < 0.05). In multivariate analyses, size, adjacent anterior capsule, distance to the lower pole and CDFI were independent risk factors for male patients. For females, the independent risk factors included size, adjacent anterior capsule, distance to the lower pole and CDFI.

CONCLUSION

In the present cohort, US imaging characteristics, including size, adjacent anterior capsule, distance to the lower pole and CDFI, were identified to be potentially beneficial in preoperative clinical decision-making processes for cN0 unifocal PTC patients.

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.

Risk probability model for residual metastatic lymph node in patients with papillary thyroid microcarcinoma undergoing cervical central lymph node dissection

To establish a risk probability model for residual metastatic lymph nodes in patients with papillary thyroid microcarcinoma (PTMC) after cervical central lymph node dissection (CLND). The clinical data of patients with PTMC treated in the First Affiliated Hospital of Kunming Medical University from 2007 to 2020 were retrospectively reviewed. All patients underwent thyroidectomy with CLND, and at least one lymph node was examined. Based on the distribution characteristics of metastatic lymph nodes from this retrospective cohort, a probabilistic model for the risk of residual metastatic lymph node was established. β-Binomial distribution was used to estimate the probability of residual metastatic lymph node as a function of the number of lymph nodes examined. Among 5399 patients included in the probabilistic model, central lymph node metastases were observed in 1664 cases (30.8%). After model correction, the real lymph node metastasis rate increased from 30.8% to 38.9%. The probability of false negative of central lymph node was estimated to be 31.3% for patients with a single node examined, while decreased to 10.0% and 4.9% when 7 and 12 nodes were examined, respectively. In the sensitivity analysis limited to patients with or without Hashimoto thyroiditis, the performance of probability model was also satisfactory. The established risk probability model in this study quantifies the risk of residual metastatic lymph nodes after CLND in patients with PTMC, which can be used as complementary indicators for the risk of recurrence/persistence disease at postoperative evaluation. The study also provides a new method to evaluate the impact of residual metastatic lymph nodes on the prognosis of tumor patients through retrospective data.

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.

Influencing Factors and Cumulative Risk Analysis of Cervical Lymph Node Metastasis of Papillary Thyroid Microcarcinoma

Objective

To explore the influencing factors and cumulative risk of lymph node metastasis (LNM) in papillary thyroid microcarcinoma (PTMC) patients.

Methods

607 patients confirmed PTMC pathologically after thyroidectomy were enrolled in this retrospective study. The rate of LNM was calculated. Different clinicopathological characteristics were compared in PTMC patients with and without LNM and in different subgroups of LNM, respectively. Correlation between clinicopathological characteristics and LNM was analyzed and the cumulative risk of LNM according to different clinicopathological characteristics was calculated.

Results

(1) There were 228 cases (37.56%) of PTMC combined with LNM. Compared with the non-lymph node metastasis group, the proportion of age <55 years, male, multiple foci, bilateral foci, diameter>0.5cm, extracapsular invasion, HT and intermediate-to-high risk stratification for recurrence of the LNM group was significantly increased (all p<0.05);(2) Multivariate logistic regression analysis showed that age <55years, male, multiple foci, diameter>0.5cm,HT were independently positively correlated with LNM (all p <0.05); (3) Subgroup analysis showed that women aged <55 years combined with HT and aged≥55 years combined with BMI≥25 kg/m² were independently positively associated with LNM; (4) With the increase of the tumor diameter, the cumulative risk of LNM in group of age <55 years, males, and multiple foci increased gradually, and was higher than those of age≥55 years, females and single foci, respectively. (5) Among the 228 cases of LNM, the proportion of lymph nodes (LN) >5 and the positive rate of LN were both higher in male group than that in the female group. The proportion of multiple foci and HT in LLNM group was higher than that in CLNM group (all P<0.05).

Conclusion

Age <55 years, males, multiple foci, diameter >0.5cm and HT were independent risk factors of LNM; HT was an independent risk factor for LNM in female <55 years old, and BMI≥25 kg/m² was an independent risk factor for LNM in female ≥55 years old; The increase of tumor diameter in age <55 years, males, multiple foci, and bilateral foci increased the cumulative risk of LNM, respectively; The number of LNM and the positive rate of LNM were both higher in male, and patients with multiple foci or HT were more likely to develop into LLNM.

Prediction Model of Pathologic Central Lymph Node Negativity in cN0 Papillary Thyroid Carcinoma

Background

Most patients with papillary thyroid carcinoma (PTC) have an excellent prognosis. Although central lymph node invasion is frequent, management via central lymph node dissection (CLND) remains controversial. The present study retrospectively investigated independent predictors of pathologic central lymph node negativity (pCLN-) and established a prediction model for pCLN- in clinical lymph node negativity (cN0) PTC.

Methods

A total of 2,687 patients underwent thyroid surgery for cN0 PTC from 2013 to 2018 at the First Affiliated Hospital of Chongqing Medical University, and lobectomy plus ipsilateral CLND was the basic surgical extent. Clinicopathological characteristics were reviewed and analyzed. Univariate and multivariate analyses were performed to identify factors related to pCLN-. A prediction model was established based on the results of multivariate analyses.

Results

The pCLN- rate was 51.5% (1,383/2,687). Multivariate analysis revealed that sex, age, thyroid stimulating hormone (TSH), size, location, laterality, unifocality and extrathyroidal extension negativity (ETE-) were independent predictors of pCLN-. The nomogram showed good discriminative ability (C-index: 0.784 and 0.787 in derivation and validation groups, respectively) and was well calibrated. We quantified the clinical usefulness of the nomogram by decision curve analysis. The median length of follow-up was 30 (range 12– 83) months, and 190 cases were lost, with a follow-up rate of 92.9% (2,497/2,687). Of the 2,687 patients included, 21 (0.8%) experienced recurrence.

Conclusion

This nomogram, which integrates available preoperative clinicopathological features and intraoperative frozen biopsy outcomes, is a reliable tool with high accuracy to predict pCLN- in cN0 PTC.

Total Tumor Diameter and Unilateral Multifocality as Independent Predictor Factors for Metastatic Papillary Thyroid Microcarcinoma

Simple Summary

Papillary thyroid microcarcinoma is currently the most frequent endocrine cancer at this time. Usually, this form of cancer is indolent, but there are situations in which it metastasizes. The current classification guidelines are rather simplistic and do not comprehend the whole disease spectrum. Studies that have addressed this issue have evaluated various stages of papillary thyroid carcinoma, considering the scarcity of studies based on European demographic data. We aim to further investigate whether total tumor diameter and multifocality are directly correlated with metastatic forms of papillary thyroid microcarcinoma. The results of this study could validate the confidence with which current guidelines are used or could open new avenues in using the total tumor diameter instead of the size of the largest tumor.

Abstract

The purpose of this study was to assess whether total tumor diameter (TTD) and multifocality are predictors for metastatic disease in papillary thyroid microcarcinomas (PTMC). Eighty-two patients with histologically proven PTMC were retrospectively included. Patients were divided according to the presence of metastatic disease in the metastatic (n = 41) and non-metastatic (n = 41) demographic-matched group. The morphological features of PTMCs (primary tumor diameter, multifocality, TTD, number of foci, and tumor site) were compared between groups using univariate, multivariate, and receiver operating characteristic analyses. TTD (p = 0.026), TTD > 10 mm (p = 0.036), and Unilateral Multifocality (UM) (p = 0.019) statistically differed between the groups. The combination of the two independent predictors (TTD and UM) was able to assess metastatic risk with 60.98% sensitivity and 75.61% specificity. TTD and UM can be used to predict metastatic disease in PTMC, which may help to better adapt the RAI therapy decision. We believe that TTD and multifocality are tumor features that should be considered in future guidelines.

Bilateral multifocality, a marker for aggressive disease, is not an independent prognostic factor for papillary thyroid microcarcinoma: A propensity score matching analysis

CONTEXT

Multifocality and bilaterality are common in patients with papillary thyroid microcarcinoma (PTMC). However, their clinical behaviours and prognostic implications remain controversial.

OBJECTIVE

To investigate the relationship between multifocality and classically aggressive characteristics and outcomes in patients with PTMC.

METHODS

Clinical data of 3005 patients with PTMC were retrospectively reviewed at a tertiary medical centre. The role of unilateral and bilateral multifocality in aggressive characteristics and clinical outcomes of PTMC was evaluated using propensity score matching (PSM).

RESULTS

A total of 573 patients had bilateral multifocal disease (B-MFD), 272 had unilateral multifocal disease (U-MFD), and 2160 had unifocal disease (UFD). Univariate analysis showed that patients in the multifocal disease (MFD) groups showed significantly different characteristics compared to patients in the UFD group in terms of age, chronic lymphocytic thyroiditis (CLT), follicular variant PTMC, tumour diameter, aggressive growth, including extrathyroidal extension (ETE), central lymph node metastasis (CLNM) and lateral lymph node metastasis (LLNM), and TNM stage, and underwent radioactive iodine (RAI) therapy. Further stratified analysis revealed that patients in the B-MFD group reflected the differences between the MFD and UFD groups. However, those in the U-MFD group showed slight differences only in sex, CLT and cell subtypes, compared to the UFD group. In addition, PSM indicated differences in ETE, CLNM and LLNM between the B-MFD and UFD groups (p < .001), while only ETE differed between the U-MFD and UFD groups (p < .001). After a median follow-up period of 60 months, no difference was observed in recurrence-free survival between the UFD and B-MFD (p = .294) or U-MFD (p = .603) groups using PSM.

CONCLUSION

This propensity score matching analysis provides strong evidence that bilateral multifocality, rather than unilateral multifocality, should be considered as an aggressive marker at presentation, and neither is an independent prognostic factor for clinical outcome in PTMC.

Cystic lymph node metastasis of papillary thyroid cancer. Clinical facts

BACKGROUND

The therapy planning for cystic cervical lesions is dizzying. Although it is mostly a benign disease, it can also be a cystic lymph node metastasis with the origin of papillary thyroid cancer (PTC).

METHODS

Included were all patients with histological confirmed PTC, who underwent a thyroid resection from January 2012 to December 2017 (n=680). Analyzed were demographic data including family history and radiation exposure, preoperative workup including thyroid ultrasound and laboratory test of the TSH value complimented by fine-needle aspiration in case of suspected malignancy, and clinicopathologic features. This study aimed to specify preoperative findings and patients´ clinical presentation with cystic lymph node metastasis of PTC.

RESULTS

In 0.7% (5/680) of all patients with PTC a cystic cervical lesion was histologically confirmed as cystic lymph node metastasis. Preoperatively, only two of these patients were suspected of lymph node metastasis with unknown origin. In three patients, the resected cystic lymph node metastases were the only lymphatic metastasis. Interestingly 80% (4/5) of the patients suffered from papillary microcarcinoma (MPTC).

CONCLUSIONS

Cervical cystic lesions may be challenging in diagnostics and therapy. Although the recommended thyroid ultrasound may detect no pathological findings, a papillary microcarcinoma can be the primary tumor.

Development and Validation of a Nomogram for Preoperative Prediction of Central Compartment Lymph Node Metastasis in Patients with Papillary Thyroid Carcinoma and Type 2 Diabetes Mellitus

Purpose

To develop and validate a nomogram to predict central compartment lymph node metastasis in PTC patients with Type 2 Diabetes.

Patients and Methods

The total number of enrolled patients was 456. The optimal cut-off values of continuous variables were obtained by ROC curve analysis. Significant risk factors in univariate analysis were further identified to be independent variables in multivariable logistic regression analysis, which were then incorporated and presented in a nomogram. The ROC curve analysis was performed to evaluate the discrimination of the nomogram, calibration curves and Hosmer-Lemeshow test were used to visualize and quantify the consistency. Decision curve analysis (DCA) was performed to evaluate the net clinical benefit patients could get by applying this nomogram.

Results

ROC curve analysis showed the optimal cutoff values of NLR, PLR, and tumor size were 2.9204, 154.7003, and 0.95 (cm), respectively. Multivariate logistic regression analysis indicated that age, multifocality, largest tumor size, and neutrophil-to-lymphocyte ratio were independent prognostic factors of CLNM. The C-index of this nomogram in the training data set was 0.728, and 0.618 in the external validation data set. When we defined the predicted possibility (>0.5273) as high-risk of CLNM, we could get a sensitivity of 0.535, a specificity of 0.797, a PPV(%) of 67.7, and an NPV(%) of 68.7. Great consistencies were represented in the calibration curves. DCA showed that applying this nomogram will help patients get more clinical net benefit than having all of the patients or none of the patients treated with central compartment lymph node dissection (CLND).

Conclusion

A high level of preoperative NLR was an independent predictor for CLNM in PTC patients with T2DM. And the verified optimal cutoff value of NLR in this study was 2.9204. Applying this nomogram will help stratify high-risk CLNM patients, consequently enabling these patients to be treated with appropriate measures. What is more, we hope to find more sensitive indicators in the near future to further improve the sensitivity and specificity of our nomogram.

Application of Machine Learning Algorithms to Predict Central Lymph Node Metastasis in T1-T2, Non-invasive, and Clinically Node Negative Papillary Thyroid Carcinoma

Purpose: While there are no clear indications of whether central lymph node dissection is necessary in patients with T1-T2, non-invasive, clinically uninvolved central neck lymph nodes papillary thyroid carcinoma (PTC), this study seeks to develop and validate models for predicting the risk of central lymph node metastasis (CLNM) in these patients based on machine learning algorithms.

Methods: This is a retrospective study comprising 1,271 patients with T1-T2 stage, non-invasive, and clinically node negative (cN0) PTC who underwent surgery at the Department of Endocrine and Breast Surgery of The First Affiliated Hospital of Chongqing Medical University from February 1, 2016, to December 31, 2018. We applied six machine learning (ML) algorithms, including Logistic Regression (LR), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), Random Forest (RF), Decision Tree (DT), and Neural Network (NNET), coupled with preoperative clinical characteristics and intraoperative information to develop prediction models for CLNM. Among all the samples, 70% were randomly selected to train the models while the remaining 30% were used for validation. Indices like the area under the receiver operating characteristic (AUROC), sensitivity, specificity, and accuracy were calculated to test the models' performance.

Results: The results showed that ~51.3% (652 out of 1,271) of the patients had pN1 disease. In multivariate logistic regression analyses, gender, tumor size and location, multifocality, age, and Delphian lymph node status were all independent predictors of CLNM. In predicting CLNM, six ML algorithms posted AUROC of 0.70–0.75, with the extreme gradient boosting (XGBoost) model standing out, registering 0.75. Thus, we employed the best-performing ML algorithm model and uploaded the results to a self-made online risk calculator to estimate an individual's probability of CLNM (https://jin63.shinyapps.io/ML_CLNM/).

Conclusions: With the incorporation of preoperative and intraoperative risk factors, ML algorithms can achieve acceptable prediction of CLNM with Xgboost model performing the best. Our online risk calculator based on ML algorithm may help determine the optimal extent of initial surgical treatment for patients with T1-T2 stage, non-invasive, and clinically node negative PTC.

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.

A Nomogram Based on Clinical and Ultrasound Characteristics to Predict Central Lymph Node Metastasis of Papillary Thyroid Carcinoma

BACKGROUND

The status of lymph nodes in the central compartment is crucial to determining the surgical strategies for papillary thyroid carcinoma (PTC). We aimed to develop a nomogram for predicting central lymph node metastasis (CLNM).

METHODS

A total of 886 PTC patients who underwent total thyroidectomy or lobectomy with central neck dissection (CND) from July 2019 to June 2020 were retrospectively retrieved. Clinical and ultrasound features were collected. Univariate and multivariate analysis were performed to determine risk factors of CLNM. A nomogram for predicting CLNM was developed, internal and external calibration was performed for the established model.

RESULTS

Variables (sex, chronic lymphocytic thyroiditis, tumor size, the number of foci, tumor location, margin) significantly associated with CLNM were included in the nomogram. The nomogram showed excellent calibration in the training group and validation group, with area under curves of 0.806 (95% CI, 0.771 to 0.825), and 0.799 (95% CI, 0.778-0.813) respectively.

CONCLUSION

Through this accurate and easy-to-use nomogram, the possibility of CLNM can be objectively quantified preoperatively. Clinicians can use this nomogram to evaluate the status of lymph nodes in PTC patients and consider prophylactic CND for those with high scores.

Nomogram for Preoperative Estimation of Cervical Lymph Node Metastasis Risk in Papillary Thyroid Microcarcinoma

OBJECTIVE

Accurate preoperative identification of cervical lymph node metastasis (CLNM) is essential for clinical management and established of different surgical protocol for patients with papillary thyroid microcarcinoma (PTMC). Herein, we aimed to develop an ultrasound (US) features and clinical characteristics-based nomogram for preoperative diagnosis of CLNM for PTMC.

METHOD

Our study included 552 patients who were pathologically diagnosed with PTMC between January 2015 and June 2019. All patients underwent total thyroidectomy or lobectomy and divided into two groups: CLNM and non-CLNM. Univariate and multivariate analysis were performed to examine risk factors associated with CLNM. A nomogram comprising the prognostic model to predict the CLNM was established, and internal validation in the cohort was performed.

RESULTS

CLNM and non-CLNM were observed in 216(39.1%) and 336(60.9%) cases, respectively. Seven variables of clinical and US features as potential predictors including male sex (odd ratio [OR] = 1.974, 95% confidence interval [CI], 1.243-2.774; P =0.004), age < 45 years (OR = 4.621, 95% CI, 2.160-9.347; P < 0.001), US-reported CLN status (OR = 1.894, 95% CI, 0.754-3.347; P =0.005), multifocality (OR = 1.793, 95% CI, 0.774-2.649; P =0.007), tumor size ≥ 0.6cm (OR = 1.731, 95% CI,0.793-3.852; P =0.018), ETE (OR = 3.772, 95% CI, 1.752-8.441;P< 0.001) and microcalcification (OR = 2.316, 95% CI, 1.099-4.964; P < 0.001) were taken into account. The predictive nomogram was established by involving all the factors above used for preoperative prediction of CLNM in patients with PTCM. The nomogram model showed an AUC of 0.839 and an accuracy of 77.9% in predicting CLNM. Furthermore, the calibration curve demonstrated a strong consistency between nomogram and clinical findings in prediction CLNM for PTMC.

CONCLUSIONS

The nomogram achieved promising results for predicting preoperative CLNM in PTMC by combining clinical and US risk factor. Our proposed prediction model is able to help determine an individual's risk of CLNM in PTMC, thus facilitate reasonable therapy decision making.

Nomogram Including Elastography for Prediction of Contralateral Central Lymph Node Metastasis in Solitary Papillary Thyroid Carcinoma Preoperatively

Background

It is controversial whether contralateral prophylactic central neck dissection (PCND) should be performed for patients with solitary and clinical lymph node negative (cN₀) papillary thyroid carcinoma (PTC) although routine ipsilateral PCND is required.

Objective

The aim of this study was to develop an improved nomogram including clinical features, ultrasound, and acoustic radiation force impulse (ARFI) elastography for the prediction of contralateral central lymph node metastasis (CLNM) in patients with solitary and cN₀ PTC in the preoperative period.

Materials and Methods

A total of 340 patients were retrospectively included as the training cohort and 170 patients as the external validation cohort. Patients were grouped according to the pathological results of contralateral CLNM. The association between the clinical characteristics, ultrasound, and ARFI elastography and the risk for contralateral CLNM were analyzed. A nomogram was established based on the result of multivariable logistic analysis to predict the risk of contralateral CLNM, which was assessed by internal and external validation.

Results

CLNM was found in 213 patients (41.8%), among whom 142 (27.8%) had ipsilateral CLNM and 95 (18.6%) had contralateral CLNM (including 68 (13.3%) with bilateral CLNM). Multivariable analysis revealed that patients with younger age, male gender, larger tumor size, closer distance from the capsule, microcalcification, and larger SWV_mean were independent predictors associated with the contralateral CLNM (P < 0.05), which was served as the basis of the nomogram. It showed good discrimination (C-index: 0.856) and calibration (χ² = 9.028, P = 0.340, Hosmer–Lemeshow test) in the training cohort, and good discrimination was maintained in the external validation cohort (C-index: 0.792).

Conclusion

The nomogram utilizing the features of ultrasound combined with ARFI elastography in preoperatively predicting the risk of contralateral CLNM in patients with solitary and cN₀ PTC was established, which showed superior performance both in internal and external validation.

An Ultrasound Radiomics Nomogram for Preoperative Prediction of Central Neck Lymph Node Metastasis in Papillary Thyroid Carcinoma

Purpose: This study aimed to establish and validate an ultrasound radiomics nomogram for the preoperative prediction of central lymph node (LN) metastasis in patients with papillary thyroid carcinoma (PTC).

Patients and Methods: The prediction model was developed in 609 patients with clinicopathologically confirmed unifocal PTC who received ultrasonography between Jan 2018 and June 2018. Radiomic features were extracted after the ultrasonography of PTC. Lasso regression model was used for data dimensionality reduction, feature selection, and radiomics signature building. The predicting model was established based on the multivariable logistic regression analysis in which the radiomics signature, ultrasonography-reported LN status, and independent clinicopathologic risk factors were incorporated, and finally a radiomics nomogram was established. The performance of the nomogram was assessed with respect to the discrimination and consistence. An independent validation was performed in 326 consecutive patients from July 2018 to Sep 2018.

Results: The radiomics signature consisted of 23 selected features and was significantly associated with LN status in both primary and validation cohorts. The independent predictors in the radiomics nomogram included the radiomics signature, age, TG level, TPOAB level, and ultrasonography-reported LN status. The model showed good discrimination and consistence in both cohorts: C-index of 0.816 (95% CI, 0.808–0.824) in the primary cohort and 0.858 (95% CI, 0.849–0.867) in the validation cohort. The area under receiver operating curve was 0.858. In the validation cohort, the accuracy, sensitivity, specificity and AUC of this model were 0.812, 0.816, 0.810, and 0.858 (95% CI, 0.785–0.930), respectively. Decision curve analysis indicated the radiomics nomogram was clinically useful.

Conclusion: This study presents a convenient, clinically useful ultrasound radiomics nomogram that can be used for the pre-operative individualized prediction of central LN metastasis in patients with PTC.

Prophylactic central lymph node dissection informs the decision of radioactive iodine ablation in papillary thyroid cancer

BACKGROUND

Prophylactic central lymph node dissection (CLND) in papillary thyroid cancer (PTC) is controversial. We aimed to investigate if prophylactic CLND aids risk stratification and contributes to the decision for postoperative RAI ablation.

METHODS

Patients undergoing thyroidectomy for PTC and prophylactic CLND were identified from an endocrine surgical unit database. Pathology reports where reviewed for number and size of lymph nodes and patients stratified by risk according to the ATA guidelines.

RESULTS

426 patients were identified with PTC ≤4 cm and prophylactic CLND. 96 patients (23%) had central lymph node metastasis (CLNM) that qualified them for the intermediate risk group. In 17 patients (4%), the CLNM data led to upgrading independently of other histopathological characteristics. Correcting for multiple variables, CLNM was an independent factor contributing to RAI treatment.

CONCLUSION

Prophylactic CLND provides information to aid the selection of RAI ablation independent of primary cancer histology for risk stratification in 4% of patients. This benefit should be carefully balanced with the risk of CLND and patient treatment choice when deciding on management of PTC ≤4 cm.

Papillary thyroid carcinoma: an ultrasound-based nomogram improves the prediction of lymph node metastases in the central compartment

OBJECTIVES

To develop a nomogram based on postoperative clinical and ultrasound findings to quantify the probability of central compartment lymph node metastases (CLNM).

METHODS

A total of 952 patients with histologically confirmed papillary thyroid carcinoma (PTC) were included in this retrospective study and assigned to three groups based on sex and age. The strongest predictors for CLNM were selected according to ultrasound imaging features, and an ultrasound (US) signature was constructed. By incorporating clinical characteristics, a predictive model presented as a nomogram was developed, and its performance was assessed with respect to calibration, discrimination and clinical usefulness.

RESULTS

Predictors contained in the nomogram included US signature, US-reported LN status and age. The US signature was constructed with tumour size and microcalcification. The nomogram showed excellent calibration in the training dataset, with an AUC of 0.826 (95% CI, 0.765-0.887) for male patients, 0.818 (95% CI, 0.746-0.890) for young females and 0.808 (95% CI, 0.757-0.859) for elder females. For male and young female patients, application of the nomogram to the validation cohort revealed good discrimination, with AUCs of 0.813 (95% CI, 0.722-0.904) and 0.814 (95% CI, 0.712-0.915), respectively. Conversely, for elderly female patients, the nomogram failed to show good performance with an AUC of 0.742 (95% CI, 0.661-0.823).

CONCLUSION

This ultrasound-based nomogram may serve as a useful clinical tool to provide valuable information for treatment decisions, especially for male and younger female patients.

KEY POINTS

• Age, gender, US-reported LN status and US signature were the strongest predictors of CLNM in PTC patients and informed the development of a predictive nomogram. • Microcalcification was the strongest predictor in the US signature, as CLMN was identified in approximately 92% of patients characterised by diffuse microcalcification. • Stratified by sex and age, this nomogram achieved good performance in predicting CLNM, especially in male and young female patients. This prediction tool may be useful as an imaging marker for identifying CLNM preoperatively in PTC patients and as a guide for personalised treatment.

Nomogram for Predicting Central Lymph Node Metastasis in Papillary Thyroid Cancer: A Retrospective Cohort Study of Two Clinical Centers

Purpose

Central lymph node metastasis (CNM) are highly prevalent but hard to detect preoperatively in papillary thyroid carcinoma (PTC) patients, while the significance of prophylactic compartment central lymph node dissection (CLND) remains controversial as a treatment option. We aim to establish a nomogram assessing risks of CNM in PTC patients, and explore whether prophylactic CLND should be recommended.

Materials and Methods

One thousand four hundred thirty-eight patients from two clinical centers that underwent thyroidectomy with CLND for PTC within the period 2016–2019 were retrospectively analyzed. Univariate and multivariate analysis were performed to examine risk factors associated with CNM. A nomogram for predicting CNM was established, thereafter internally and externally validated.

Results

Seven variables were found to be significantly associated with CNM and were used to construct the model. These were as follows: thyroid capsular invasion, multifocality, creatinine > 70 μmol/L, age < 40, tumor size > 1 cm, body mass index < 22, and carcinoembryonic antigen > 1 ng/mL. The nomogram had good discrimination with a concordance index of 0.854 (95% confidence interval [CI], 0.843 to 0.867), supported by an external validation point estimate of 0.825 (95% CI, 0.793 to 0.857). A decision curve analysis was made to evaluate nomogram and ultrasonography for predicting CNM.

Conclusion

A validated nomogram utilizing readily available preoperative variables was developed to predict the probability of central lymph node metastases in patients presenting with PTC. This nomogram may help surgeons make appropriate surgical decisions in the management of PTC, especially in terms of whether prophylactic CLND is warranted.

Ultrasound validation of predictive model for central cervical lymph node metastasis in papillary thyroid cancer on BRAF

Aim: To compare the value of predictive power of the models for central cervical lymph node metastasis (CLNM) in papillary thyroid carcinomas (PTCs). Patients & methods: 220 PTCs were prospectively enrolled into the study with pathological examination. We established a new risk model with univariate and multivariate analyses and receiver-operating characteristic curves were plotted. Z-test was performed to compare the area under two curves and validated the predictive model for central CLNM in PTCs. The comparison of previous and new predictive model was analyzed. Results: Microcalcification, capsule contact or involvement, internal flow and BRAF^V600E mutation were four independent risk factors for PTCs with central CLNMs. The area under the curves for the new and the previous model were 0.948 and 0.934 (p = 0.572), respectively. Conclusion: Two predictive models showed strong consistency in predicting central CLNM in PTCs. The predictive model may be helpful in selecting appropriate treatment method in PTCs.

Nomogram Based on Shear-Wave Elastography Radiomics Can Improve Preoperative Cervical Lymph Node Staging for Papillary Thyroid Carcinoma

Background: Accurate preoperative prediction of cervical lymph node (LN) metastasis in patients with papillary thyroid carcinoma (PTC) provides a basis for surgical decision-making and the extent of tumor resection. This study aimed to develop and validate an ultrasound radiomics nomogram for the preoperative assessment of LN status. Methods: Data from 147 PTC patients at the Wuhan Tongji Hospital and 90 cases at the Hunan Provincial Tumor Hospital between January 2017 and September 2019 were included in our study. They were grouped as the training and external validation set. Radiomics features were extracted from shear-wave elastography (SWE) images and corresponding B-mode ultrasound (BMUS) images. Then, the minimum redundancy maximum relevance algorithm and the least absolute shrinkage and selection operator regression were used to select LN status-related features and construct the SWE and BMUS radiomics score (Rad-score). Multivariate logistic regression was performed using the two radiomics scores together with clinical data, and a nomogram was subsequently developed. The performance of the nomogram was assessed with respect to discrimination, calibration, and clinical usefulness in the training and external validation set. Results: Both the SWE and BMUS Rad-scores were significantly higher in patients with cervical LN metastasis. Multivariate analysis indicated that the SWE Rad-scores, multifocality, and ultrasound (US)-reported LN status were independent risk factors associated with LN status. The radiomics nomogram, which incorporated the three variables, showed good calibration and discrimination in the training set (area under the receiver operator characteristic curve [AUC] 0.851 [CI 0.791-0.912]) and the validation set (AUC 0.832 [CI 0.749-0.916]). The significantly improved net reclassification improvement and index-integrated discrimination improvement demonstrated that SWE radiomics signature was a very useful marker to predict the LN metastasis in PTC. Decision curve analysis indicated that the SWE radiomics nomogram was clinically useful. Furthermore, the nomogram also showed favorable discriminatory efficacy in the US-reported LN-negative (cN0) subgroup (AUC 0.812 [CI 0.745-0.860]). Conclusions: The presented radiomics nomogram, which is based on the SWE radiomics signature, shows a favorable predictive value for LN staging in patients with PTC.

Predicting factors of central lymph node metastases in patients with unilateral multifocal papillary thyroid microcarcinoma

Background

Unilateral multifocal papillary thyroid microcarcinoma (UM-PTMC) is not rare, and the suitable extent of operation is controversial. Total thyroidectomy is associated with a high risk of hypoparathyroidism and recurrent laryngeal nerve injury. Hemithyroidectomy should be considered in patients without lymph node metastases. This study aimed to identify factors predicting central lymph node metastases (CLNM) in patients with UM-PTMC.

Methods

A total of 1,453 patients with thyroid cancer underwent surgery at our hospital from January 2018 to June 2018, and 115 patients with UM-PTMC were selected and assessed retrospectively. The relationship between CLNM and clinicopathologic features of UM-PTMC were analyzed by both univariate and multivariate analyses.

Results

Of the 115 patients with UM-PTMC, CLNM were confirmed by pathology in 57 patients. Univariate and multivariate analyses identified the sum diameter of all tumors (≥1.0 cm) [odds ratio (OR) =2.295; 95% confidence interval (CI): 1.036-5.086; P=0.041] and positive CLNM via ultrasonography (OR =5.882; 95% CI: 1.562-22.153; P=0.009) as independent predictors of CLNM.

Conclusions

CLNM are common in patients with UM-PTMC. The sum diameter of all tumors (≥1.0 cm) and positive CLNM via ultrasonography are independent risk factors for CLNM. Our data should be considered in the decision process relating to performing total thyroidectomy or hemithyroidectomy in patients with UM-PTMC.

Predictive factors for central lymph node metastases in papillary thyroid microcarcinoma

Papillary thyroid microcarcinoma (PTMC) measures 1 cm or less in its longest dimension. The incidence of PTMC is increasing worldwide. Surgery is the primary treatment; however, prophylactic central lymph node dissection is controversial, and discrepancies between different guidelines have been noted. Routine prophylactic central lymph node dissection may result in hypoparathyroidism and recurrent laryngeal nerve injury in some patients without lymph node metastasis, while simple thyroidectomy may leave metastatic lymph nodes in high-risk patients. To selectively perform prophylactic lymph node dissections in high-risk patients, it is important to identify predictive factors for lymph node metastases in patients with PTMC. Several studies have reported on this, but their conclusions are not entirely consistent. Several clinicopathologic characteristics have been identified as risk factors for central lymph node metastases, and the most commonly reported factors include age, gender, tumor size and location, multifocality, bilaterality, extrathyroidal extension, and abnormal lymph node found using ultrasound. Here, we provide an overview of previous studies along with a favorable opinion on or against these factors, with the aim of increasing the understanding of this topic among the medical community. In addition, current opinions about prophylactic central lymph node dissection are reviewed and discussed.

Ultrasound Elastic Parameters Predict Central Lymph Node Metastasis of Papillary Thyroid Carcinoma

BACKGROUND

This study aimed to explore the new factors that can predict central lymph node metastasis (CLNM) of papillary thyroid carcinoma (PTC) independently from ultrasound characteristics, elastic parameters, and endocrine indicators.

METHODS

A total of 391 patients with PTC undergoing thyroidectomy and prophylactic central lymph node dissection from January 2017 to June 2019 were collected to determine the independent predictors of CLNM by single-factor and multivariate logistic regression analysis.

RESULTS

Multivariate logistic regression analysis showed 9 independent predictors of CLNM, age, male, tumors in the middle or lower poles (without tumors in the isthmus), tumors in the isthmus, multiple tumors, and maximum tumor diameter measured by ultrasound, microcalcification, visible surrounding blood flow signal, and the maximum value of elastic modulus (Emax).We used the aforementioned factors to establish a scoring prediction model: predictive score Y(P) = 1/[1 + exp (1.444 + 0.084 ∗ age - 0.834 ∗ men - 0.73 ∗ multifocality - 2.718 ∗ tumors in the isthmus - 0.954 ∗ tumors in the middle or lower poles - 0.086 ∗ tumor maximum diameter - 1.070 ∗ microcalcification - 0.892 ∗ visible surrounding blood flow signal - 0.021 ∗ Emax)]. The area under the curve of the receiver operating characteristic was 0.827. It was found that 0.524 was the highest index of Youden, and the best cutoff value for predicting CLNM. When Y(P)≥0.524, the risk of CLNM in patients with PTC is predicted to be high. Predictive accuracy was 78.5% and 72.4% in the internal validation group and 78.6% in the external validation group.

CONCLUSIONS

These data indicate that the scoring prediction model could provide a scientific and quantitative way to predict CLNM in patients with PTC.

Clinical Analysis of Risk Factors for Cervical Lymph Node Metastasis in Papillary Thyroid Microcarcinoma: A Retrospective Study of 3686 Patients

Purpose

To investigate the risk factors for cervical lymph node metastasis (LNM) in papillary thyroid microcarcinoma (PTMC).

Patients and Methods

In total, 3686 patients with PTMC who underwent initial surgery in Shanghai Jiao Tong University affiliated Sixth People’s Hospital from January 2010 to December 2019 were retrospectively analyzed. Univariate and multivariate analyses were conducted to identify risk factors associated with cervical LNM.

Results

Male gender [odds ratio (OR) =1.420, P <0.001], age <55 years (OR =2.128, P <0.001), tumor size >6.5 mm (OR =2.112, P <0.001), lymphovascular invasion (LVI) (OR =2.110, P =0.016), multifocality (OR =1.358, P =0.022), extrathyroidal extension (ETE) (OR =1.598, P <0.001), and lateral LNM (LLNM) (OR =6.383, P <0.001) served as independent risk factors for central LNM (CLNM). Moreover, male gender (OR =1.668, P =0.001), tumor size >6.5 mm (OR =2.223, P <0.001), chronic lymphocytic thyroiditis (OR =1.402, P =0.021), LVI (OR =4.582, P <0.001), ETE (OR =1.393, P=0.023), and CLNM (OR =6.212, P <0.001) served as independent risk factors for LLNM. Furthermore, solitary PTMC with lesions in the upper third of the thyroid gland were more associated with LLNM than lesions in the other regions.

Conclusion

This study suggests that meticulous evaluation of risk factors associated with LNM is required in order to guide the surgical treatment of PTMC patients in clinical practice.

Select Group of Patients Might Benefit From Prophylactic Central Lymph Node Dissection in Clinically Node Negative Papillary Thyroid Microcarcinoma

Objective

The aim of this study was to identify predictive factors for pathologic central lymph node metastasis (CLNM) in patients with clinically negative CLNM (cN0) papillary thyroid microcarcinoma (PTMC) to suggest a specific group of cN0 PTMC patients who benefit from prophylactic central lymph node dissection (CLND).

Background

PTMC have excellent prognosis, however, 8% have recurrent disease. Recurrence is associated with regional lymph node metastasis. It has been reported that pathologic CLNM is found in 30 to 65% of cN0 PTMC patients. However, prophylactic CLND for cN0 PTMC remains controversial.

Methods

We reviewed the records of papillary thyroid carcinoma patients with a tumor size on preoperative ultrasonography (US) of ≤10 mm and negative CLNM on preoperative physical examination, US, and positron emission tomography/computed tomography (PET/CT), who underwent total thyroidectomy or thyroid lobectomy with prophylactic CLND between April 2010 and February 2013. In total, 163 patients were included. We evaluated the relationship between preoperatively assessable factors including age, gender, body mass index, preoperative metformin use, primary tumor size through preoperative US, the maximum standardized uptake value (SUVmax) of the primary tumor through preoperative PET/CT, and the bilaterality of the primary tumor and pathologic CLNM.

Results

A SUVmax of the primary tumor of &gt;3 was an independent preoperatively assessable factor predictive of pathologic CLNM in cN0 PTMC patients.

Conclusions

Prophylactic CLND might be considered in cN0 PTMC patients with a SUVmax of the primary tumor of &gt; 3 to reduce the risks of residual CLNM and recurrence.

Development and validation of a nomogram for preoperative prediction of cervical lymph node involvement in thyroid microcarcinoma

Cervical regional lymph node involvement (CRLNI) is common in papillary thyroid microcarcinoma (PTMC), but the way to deal with cervical lymph node involvement of clinically negative PTMC is controversial. We studied data of patients histologically confirmed PTMC in the Surveillance, Epidemiology, and End Results (SEER) Program and Department of Surgical Oncology in Hangzhou First People's Hospital (China). We screened 6 variables of demographic and clinicopathological characteristics as potential predictors and further constructed a lymph node involvement model based on the independent predictors including age, race, sex, extension, multifocality and tumor size. The model was validated by both the internal and the external testing sets, and the visual expression of the model was displayed by a nomogram. As a result, the C-index of this predictive model in the training set was 0.766, and the internal and external testing sets through cross-validation were 0.753 and 0.668, respectively. The area under the receiver operating characteristic curve (AUC) was 0.766 for the training set. We also performed a Decision Curve Analysis (DCA), which showed that predicting the cervical lymph node involvement risk applying this nomogram would be better than having all patients or none patients use this nomogram.