Clinical characteristics and ultrasonographic features for predicting central lymph node metastasis in clinically node-negative papillary thyroid carcinoma without capsule invasion

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Risk factors of cervical central lymph node metastasis in stage T1a unifocal papillary thyroid carcinoma

To investigate the correlation of cervical central lymph node metastasis (CLNM) in stage T1a unifocal papillary thyroid carcinoma (PTC) with the clinicopathological characteristics, ultrasonography features and the number of lymph node dissection, and to analyze the risk factors of CLNM. Data from 493 unifocal PTC patients (T1a) who underwent partial or total thyroidectomy and pCLND at the Guizhou Provincial People's Hospital were collected and retrospectively analyzed. They were divided into two groups in accordance with cervical CLNM or not. Their information, including clinical characteristics, ultrasound (US) features, pathological results, and other characteristics of the groups, was analyzed and compared using univariate and multivariate logistic regression analyses. A total of 493 patients were eligible in this study. Among them, 33.7% (166/493) of PTC patients had cervical CLNM, and 66.3% (327/493) did not. The two groups were compared using a univariate analyses, and there were no significant differences between the two groups in age, maximum tumor size, tumor location, aspect ratio, boundary, morphology, echogenicity, BRAFV600E and HT (P > 0.05), and there were significant differences between gender, capsule contact, microcalcifications, rich vascularity, and number of lymph node dissection (P < 0.05). A multivariate logistic regression analyses was performed to further clarify the correlation of these indices. However, only male (OR = 1.770, P = 0.009), microcalcifications (OR = 1.791, P = 0.004), capsule contact (OR = 1.857, P = 0.01), and number of lymph node dissection (OR = 2.274, P < 0.001) were independent predictors of cervical CLNM. In conclusion, four independent predictors of cervical CLNM, including male, microcalcifications, capsule contact, and number of lymph node dissection, were screened out. Therefore, a comprehensive assessment of these risk factors should be conducted when designing individualized treatment regimens for PTC patients.

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

BACKGROUND

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSIONS

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

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.

Predictive Values of Clinical Features and Multimodal Ultrasound for Central Lymph Node Metastases in Papillary Thyroid Carcinoma

Papillary thyroid carcinoma (PTC), the predominant pathological type among thyroid malignancies, is responsible for the sharp increase in thyroid cancer. Although PTC is an indolent tumor with good prognosis, 60-70% of patients still have early cervical lymph node metastasis, typically in the central compartment. Whether there is central lymph node metastasis (CLNM) or not directly affects the formulation of preoperative surgical procedures, given that such metastases have been tied to compromised overall survival and local recurrence. However, detecting CLNM before operation can be challenging due to the limited sensitivity of preoperative approaches. Prophylactic central lymph node dissection (PCLND) in the absence of clinical evidence of CLNM poses additional surgical risks. This study aims to provide a comprehensive review of the risk factors related to CLNM in PTC patients. A key focus is on utilizing multimodal ultrasound (US) for accurate prognosis of preoperative CLNM and to highlight the distinctive role of US-based characteristics for predicting CLNM.

Prediction of lymph node metastasis in patients with papillary thyroid cancer based on radiomics analysis and intraoperative frozen section analysis: A retrospective study

INTRODUCTION

To evaluate the diagnostic efficiency among the clinical model, the radiomics model and the nomogram that combined radiomics features, frozen section (FS) analysis and clinical characteristics for the prediction of lymph node (LN) metastasis in patients with papillary thyroid cancer (PTC).

METHODS

A total of 208 patients were randomly divided into two groups randomly with a proportion of 7:3 for the training groups (n = 146) and the validation groups (n = 62). The Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for the selection of radiomics features extracted from ultrasound (US) images. Univariate and multivariate logistic analyses were used to select predictors associated with the status of LN. The clinical model, radiomics model and nomogram were subsequently established by logistic regression machine learning. The area under the curve (AUC), sensitivity and specificity were used to evaluate the diagnostic performance of the different models. The Delong test was used to compare the AUC of the three models.

RESULTS

Multivariate analysis indicated that age, size group, Adler grade, ACR score and the psammoma body group were independent predictors of lymph node metastasis (LNM). The results showed that in both the training and validation groups, the nomogram showed better performance than the clinical model, albeit not statistically significant (p > .05), and significantly outperformed the radiomics model (p < .05). However, the nomogram exhibits a slight improvement in sensitivity that could reduce the incidence of false negatives.

CONCLUSION

We propose that the nomogram holds substantial promise as an effective tool for predicting LNM in patients with PTC.

Development and Validation of Clinical-Radiomics Nomogram for Preoperative Prediction of Central Lymph Node Metastasis in Papillary Thyroid Carcinoma

BACKGROUND

This investigation sought to create and verify a nomogram utilizing ultrasound radiomics and crucial clinical features to preoperatively identify central lymph node metastasis (CLNM) in patients diagnosed with papillary thyroid carcinoma (PTC).

METHODS

We enrolled 1069 patients with PTC between January 2022 and January 2023. All patients were randomly divided into a training cohort (n = 748) and a validation cohort (n = 321). We extracted 129 radiomics features from the original gray-scale ultrasound image. Then minimum Redundancy-Maximum Relevance and Least Absolute Shrinkage and Selection Operator regression were used to select the CLNM-related features and calculate the radiomic signature. Incorporating the radiomic signature and clinical risk factors, a clinical-radiomics nomogram was constructed using multivariable logistic regression. The predictive performance of clinical-radiomics nomogram was evaluated by calibration, discrimination, and clinical utility in the training and validation cohorts.

RESULTS

The clinical-radiomics nomogram which consisted of five predictors (age, tumor size, margin, lateral lymph node metastasis, and radiomics signature), showed good calibration and discrimination in both the training (AUC 0.960; 95% CI, 0.947-0.972) and the validation (AUC 0.925; 95% CI, 0.895-0.955) cohorts. Discrimination of the clinical-radiomics nomogram showed better discriminative ability than the clinical signature, radiomics signature, and conventional ultrasound model in both the training and validation cohorts. Decision curve analysis showed satisfactory clinical utility of the nomogram.

CONCLUSION

The clinical-radiomics nomogram incorporating radiomic signature and key clinical features was efficacious in predicting CLNM in PTC patients.

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

Objective

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

Methods

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

Results

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

Conclusion

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

Quantitative parameters of contrast-enhanced ultrasound effectively promote the prediction of cervical lymph node metastasis in papillary thyroid carcinoma

Papillary thyroid carcinoma (PTC), the most common endocrine tumor, often spreads to cervical lymph nodes metastasis (CLNM). Preoperative diagnosis of CLNM is important when selecting surgical strategies. Therefore, we aimed to explore the effectiveness of quantitative parameters of contrast-enhanced ultrasound (CEUS) in predicting CLNM in PTC. We retrospectively analyzed 193 patients with PTC undergoing conventional ultrasound (CUS) and CEUS. The CUS features and quantitative parameters of CEUS were evaluated according to PTC size ≤ 10 or > 10 mm, using pathology as the gold standard. For the PTC ≤ 10 mm, microcalcification and multifocality were significantly different between the CLNM (+) and CLNM (-) groups (both P < 0.05). For the PTC > 10 mm, statistical significance was noted between the two groups with respect to the margin, capsule contact, and multifocality (all P < 0.05). For PTC ≤ 10 mm, there was no significant difference between the CLNM (+) and CLNM (-) groups in all quantitative parameters of CEUS (all P > 0.05). However, for PTC > 10 mm, the peak intensity (PI), mean transit time, and slope were significantly associated with CLNM (all P < 0.05). Multivariate analysis showed that PI > 5.8 dB was an independent risk factor for predicting CLNM in patients with PTC > 10 mm (P < 0.05). The area under the curve of PI combined with CUS (0.831) was significantly higher than that of CUS (0.707) or PI (0.703) alone in the receiver operator characteristic curve analysis (P < 0.05). In conclusion, PI has significance in predicting CLNM for PTC > 10 mm; however, it is not helpful for PTC ≤ 10 mm.

Ultrasound-Based Nomogram for Predicting the Aggressiveness of Papillary Thyroid Carcinoma in Adolescents and Young Adults

RATIONALE AND OBJECTIVES

Assessing the aggressiveness of papillary thyroid carcinoma (PTC) preoperatively might play an important role in guiding therapeutic strategy. This study aimed to develop and validate a nomogram that integrated ultrasound (US) features with clinical characteristics to preoperatively predict aggressiveness in adolescents and young adults with PTC.

MATERIALS AND METHODS

In this retrospective study, a total of 2373 patients were enrolled and randomly divided into two groups with 1000 bootstrap sampling. The multivariable logistic regression (LR) analysis or least absolute shrinkage and selection operator LASSO regression was applied to select predictive US and clinical characteristics in the training cohort. By incorporating most powerful predictors, two predictive models presented as nomograms were developed, and their performance was assessed with respect to discrimination, calibration, and clinical usefulness.

RESULTS

The LR_model that incorporated gender, tumor size, multifocality, US-reported cervical lymph nodes (CLN) status, and calcification demonstrated good discrimination and calibration with an area under curve (AUC), sensitivity and specificity of 0.802 (0.781-0.821), 65.58% (62.61%-68.55%), and 82.31% (79.33%-85.46%), respectively, in the training cohort; and 0.768 (0.736-0.797), 60.04% (55.62%-64.46%), and 83.62% (78.84%-87.71%), respectively, in the validation cohort. Gender, tumor size, orientation, calcification, and US-reported CLN status were combined to build LASSO_model. Compared with LR_model, the LASSO_model yielded a comparable diagnostic performance in both cohorts, the AUC, sensitivity, and specificity were 0.800 (0.780-0.820), 65.29% (62.26%-68.21%), and 81.93% (78.77%-84.91%), respectively, in the training cohort; and 0.763 (0.731-0.792), 59.43% (55.12%-63.93%), and 84.98% (80.89%-89.08%), respectively, in the validation cohort. The decision curve analysis indicated that using the two nomograms to predict the aggressiveness of PTC provided a greater benefit than either the treat-all or treat-none strategy.

CONCLUSION

Through these two easy-to-use nomograms, the possibility of the aggressiveness of PTC in adolescents and young adults can be objectively quantified preoperatively. The two nomograms may serve as a useful clinical tool to provide valuable information for clinical decision-making.

A nomogram based on multimodal ultrasound and clinical features for the prediction of central lymph node metastasis in unifocal papillary thyroid carcinoma

OBJECTIVES

To build a predictive model for central lymph node metastasis (CLNM) in unifocal papillary thyroid carcinoma (UPTC) using a combination of clinical features and multimodal ultrasound (MUS).

METHODS

This retrospective study, included 390 UPTC patients who underwent MUS between January 2017 and October 2022 and were divided into a training cohort (n = 300) and a validation cohort (n = 90) based on a cut-off date of June 2022. Independent indicators for constructing the predictive nomogram models were identified using multivariate regression analysis. The diagnostic yield of the 3 predictive models was also assessed using the area under the receiver operating characteristic curve (AUC).

RESULTS

Both clinical factors (age, diameter) and MUS findings (microcalcification, virtual touch imaging score, maximal value of virtual touch tissue imaging and quantification) were significantly associated with the presence of CLNM in the training cohort (all P < .05). A predictive model (MUS + Clin), incorporating both clinical and MUS characteristics, demonstrated favourable diagnostic accuracy in both the training cohort (AUC = 0.80) and the validation cohort (AUC = 0.77). The MUS + Clin model exhibited superior predictive performance in terms of AUCs over the other models (training cohort 0.80 vs 0.72, validation cohort 0.77 vs 0.65, P < .01). In the validation cohort, the MUS + Clin model exhibited higher sensitivity compared to the CLNM model for ultrasound diagnosis (81.2% vs 21.6%, P < .001), while maintaining comparable specificity to the Clin model alone (62.3% vs 47.2%, P = .06). The MUS + Clin model demonstrated good calibration and clinical utility across both cohorts.

CONCLUSION

Our nomogram combining non-invasive features, including MUS and clinical characteristics, could be a reliable preoperative tool to predict CLNM treatment of UPTC.

ADVANCES IN KNOWLEDGE

Our study established a nomogram based on MUS and clinical features for predicting CLNM in UPTC, facilitating informed preoperative clinical management and diagnosis.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Combining Clinicopathologic and Ultrasonic Features for Predicting Skip Metastasis of Lateral Lymph Nodes in Papillary Thyroid Carcinoma

Background

Skip metastasis, regarded as lateral lymph node metastasis (LLNM) without involving the central lymph node metastasis (CLNM), in papillary thyroid carcinoma (PTC) patients is commonly unpredictable. The purpose of the present research was to investigate the independent risk factors of skip metastasis in patients with PTC.

Methods and Materials

In the present research, 228 consecutive PTC patients who experienced total thyroidectomy coupled with central and lateral lymph node dissection from May 2020 to September 2022 at the Affiliated hospital of Jiangsu University were included in our research. Univariate and multivariate analysis were then applied to investigate the risk factors of skip metastasis in patients with PTC. Furthermore, a predictive model of skip metastasis was then constructed based on risk factors.

Results

The skip metastasis rate was 11.8% (27/228) in the current research. After the univariate and multivariate analysis, tumor size ≤ 10 mm, unilaterality, microcalcification, and upper tumor location were determined to be predictive factors of skip metastasis. The risk score of skip metastasis was calculated: risk score = 1.229 × (if tumor nodule ≤ 10mm) + 1.518 × (if unilaterality nodule) + 1.074 × (if microcalcification in nodule) + 2.332 × (if nodule in upper location).

Conclusion

Tumor size ≤ 10 mm, unilaterality, microcalcification, and upper tumor location can increase the occurrence of skip metastasis in patients with PTC, which is expected to provide useful information to guide the suitable intraoperative window.

Diagnostic value of contrast-enhanced ultrasonography in the preoperative evaluation of lymph node metastasis in papillary thyroid carcinoma: a single-center retrospective study

BACKGROUND

Contrast-enhanced ultrasound (CEUS) has been recently used for the assessment of cervical lymph node metastasis (LNM) to guide surgical operation in patients with papillary thyroid carcinoma (PTC). However, the specificity and sensitivity of CEUS reported from previous studies are not consistent. The objective of this study was to evaluate the diagnostic value of CEUS for the metastasis of cervical lymph nodes in PTC patients based on data from one regional central hospital.

METHODS

The diagnostic value of CEUS in preoperative LNM of PTC patients was concluded by comparing the results of CEUS on lymph node status with postoperative pathology examination. In addition, this study conducted hierarchical analysis of PTC patients to explore whether tumor size, different lymph node regions, and Hashimoto's thyroiditis influence the assessment of CEUS.

RESULTS

This research study ultimately enrolled 965 PTC patients, including 266 males and 699 females with a mean age of 42.27 ± 11.34 years. A total of 527 patients were considered clinical-node negative, and 438 were clinical-node positive before surgery. The specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV) and accuracy of CEUS in the assessment of LNM in PTC patients were 56.00%, 71.00%, 57.06%, 69.76% and 62.59%, respectively. For central and lateral lymph nodes, the accuracy of CEUS in PTC patients was 49.43% and 54.30%, respectively. In addition, it was shown that the accuracy of CEUS in PTC patients with Hashimoto's thyroiditis (HT) slightly decreased to 58.44%, and the accuracy of CEUS in PTC patients with non-HT in turn increased to 64.17%. The accuracy of CEUS in non-papillary thyroid microcarcinoma (PTMC) and PTMC patients was 65.68% and 61.24%, respectively. The accuracy of CEUS in predicting central LNM was significantly different between PTC patients with or without HT (P < 0.001) in this study but not for lateral lymph nodes (P = 0.114).

CONCLUSION

The accuracy of CEUS in the assessment of LNM in PTC is not consistently satisfactory, especially for central lymph nodes, small tumor diameters, or patients with HT. More diagnostic technologies for abnormal lymph nodes should be considered in PTC patients.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Risk factors and prediction models of lymph node metastasis in papillary thyroid carcinoma based on clinical and imaging characteristics

BACKGROUND

Papillary thyroid carcinoma (PTC) commonly presents with lymph node metastasis, which may be associated with worsened prognosis. This study aimed to comprehensively evaluate the risk factors of lymph node metastasis in PTC based on preoperative clinical and imaging data and to construct a nomogram model to predict the risk of lymph node metastasis.

METHODS

A total of 989 patients with PTC were enrolled and randomly divided into training and validation cohorts in an 8:2 ratio. Independent risk factors for lymph node metastasis in PTC were analyzed using univariate and stepwise multivariate logistic regression. An importance analysis of independent risk factors affecting lymph node metastasis was performed according to the random forest method. Subsequently, a nomogram to predict lymph node metastasis was constructed, and the predictive effect of the nomogram was evaluated using receiver operating characteristic analysis and calibration curves.

RESULTS

Univariate regression analysis revealed that age, sex, body weight, systolic blood pressure, free triiodothyronine, nodule location, nodule number, Thyroid Imaging Reporting and Data System (TI-RADS) grade on color Doppler ultrasound, enlarged lymph node present on imaging, and nodule diameter could affect lymph node metastasis in PTC. Stepwise multivariate regression analysis showed that sex, age, enlarged lymph node present on imaging, nodule diameter, and color Doppler TI-RADS grade were independent risk factors for lymph node metastasis in PTC. Combining these five independent risk factors, a nomogram prediction model was constructed. The area under the curve (AUC) of the nomogram in the training and validation cohorts was 0.742 and 0.765, respectively, with a well-fitted calibration curve.

CONCLUSION

Our study showed that independent risk factors for lymph node metastasis in PTC were sex, age, enlarged lymph node present on imaging, nodule diameter, and color Doppler TI-RADS grade. The nomogram constructed based on these independent risk factors can better predict the risk of lymph node metastasis.

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

BACKGROUND AND AIM

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

METHOD

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

RESULTS

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

CONCLUSION

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

Construction of prediction models for determining the risk of lateral lymph node metastasis in patients with thyroid papillary carcinoma based on gender stratification

BACKGROUND

Lateral lymph node metastasis (LLNM) is associated with poor prognosis in patients with papillary thyroid cancer (PTC). The purpose of this study was to determine the risk factors for LLNM and establish prediction models that could individually assessed the risk of LLNM.

METHODS

A total of 619 PTC patients were retrospectively analyzed in our study. Univariate and multivariate analysis were performed for male and female patients, respectively, to assess relationships between clinicopathological features and LLNM. By integrating independent predictors selected by binary logistic regression modeling, preoperative and postoperative nomograms were developed to estimate the risk of LLNM.

RESULTS

LLNM was detected in 80 of 216 male patients. Of 403 female patients, 114 had LLNM. The preoperative nomogram of male patients included three clinical variables: the number of foci, tuner size, and echogenic foci. In addition to the above three variables, the postoperative nomogram of male patients included extrathyroidal extension (ETE) detected in surgery, central lymph node metastasis (CLNM) and high-volume CLNM. The preoperative nomogram of female patients included the following variables: age, chronic lymphocytic thyroiditis (CLT), BRAF V600E, the number of foci, tumor size and echogenic foci. Variables such as CLT, BRAF V600E, the number of foci, tumor size, ETE detected in surgery, CLNM, high-volume CLNM and central lymph node ratio were included in the postoperative nomogram. Above Nomograms show good discrimination.

CONCLUSIONS

Considering the difference in the incidence rate of LLNM between men and women, a separate prediction system should be established for patients of different genders. These nomograms are helpful in promoting the risk stratification of PTC treatment decision-making and postoperative management.

Nomogram based on preoperative conventional ultrasound and shear wave velocity for predicting central lymph node metastasis in papillary thyroid carcinoma

OBJECTIVE

To establish a nomogram for predicting cervical lymph node metastasis (CLNM) based on the preoperative conventional ultrasound (US) and shear wave velocity (SWV) features of papillary thyroid carcinoma (PTC).

METHODS

A total of 101 patients with pathologically confirmed thyroid nodules were enrolled. These patients were divided into the CLNM-positive (n = 40) and CLNM-negative groups (n = 61). All patients underwent the preoperative conventional US and shear wave elastography (SWE) evaluation, and the US parameters and SWV data were collected. The association between SWV ratio and CLNM was compared to assess the diagnostic efficacy of SWV ratio alone as opposed to SWV ratio in combination with the conventional US for predicting CLNM.

RESULTS

There were significant differences in shape, microcalcification, capsule contact, SWV mean, and SWV ratio between the CLNM-positive and CLNM-negative groups (P < 0.05). Logistic regression analysis showed that taller-than-wide shape, microcalcification, capsule contact, and SWV ratio > 1.3 were risk factors for CLNM; Logistic(P)=-6.93 + 1.647 * (microcalcification)+1.138 * (taller-than-wide-shape)+1.612 * (capsule contact)+2.933 * (SWV ratio > 1.3). The area under the curve (AUC) of the receiver operating characteristic (ROC) of the model for CLNM prediction was 0.87, with 81.19% accuracy, 77.5% sensitivity, and 85.25% specificity.

CONCLUSION

The nomogram based on conventional US imaging in combination with SWV ratio has the potential for preoperative CLNM risk assessment. This nomogram serves as a useful clinical tool for active surveillance and treatment decisions.

Incorporation size of lymph node metastasis focus and pre-ablation stimulated Tg could more effectively predict clinical outcomes in differentiated thyroid cancer patients without distant metastases

BACKGROUND

The size of lymph node metastasis (LNM) and pre-ablation stimulated Tg (ps-Tg) were key predictors of clinical prognosis in differentiated thyroid cancer (DTC) patients, however, very few studies combine the above two as predictors of clinical prognosis of DTC patients.

METHODS

Persistent/recurrent disease and clinicopathologic factors were analyzed in 543 DTC patients without distant metastases who underwent LN dissection, near-total/total thyroidectomy, and radioiodine ablation.

RESULTS

In the multivariate analysis, size of LNM, ps-Tg, and the activity of ¹³¹I significantly correlated with long-term remission. The optimal cutoff size of LNM 0.4 cm-1.4 cm (intermediate-risk patients) and >1.4cm (high-risk patients) increased the recurrence risk (hazard ratio [95% CI], 4.674 [2.881-7.583] and 13.653 [8.135-22.913], respectively). Integration of ps-Tg into the reclassification risk stratification showed that ps-Tg ≤ 10.0 ng/mL was relevant to a greatly heightened possibility of long-term remission (92.2%-95.4% in low-risk patients, 67.3%-87.0% in intermediate-risk patients, and 32.3%-57.7% in high-risk patients).

CONCLUSION

The cutoff of 0.4 cm and 1.4 cm for a definition of size of LNM in DTC patients without distant metastases can reclassify risk assessment, and incorporating ps-Tg could more effectively predict clinical outcomes and modify the postoperative management plan.

Preoperative strain ultrasound elastography can predict occult central cervical lymph node metastasis in papillary thyroid cancer: a single-center retrospective study

Objective

To determine whether preoperative ultrasound elastography can predict occult central cervical lymph node metastasis (CCLNM) in patients with papillary thyroid cancer.

Methods

This retrospective study included 541 papillary thyroid cancer patients with clinically negative lymph nodes prior to surgery between July 2019 and December 2021. Based on whether CCLNM was present on postoperative pathology, patients were categorized as CCLNM (+) or CCLNM (-). Preoperative clinical data, conventional ultrasound features, and ultrasound elastography indices were compared between the groups. Univariate and multivariate logistic regression analysis were performed to identify the independent predictors of occult CCLNM.

Results

A total of 36.60% (198/541) patients had confirmed CCLNM, while 63.40% (343/541) did not. Tumor location, bilaterality, multifocality, echogenicity, margin, shape, vascularity, capsule contact, extrathyroidal extension, aspect ratio, and shear wave elasticity parameters were comparable between the groups (all P > 0.05). Univariate analysis showed statistically significant differences between the two groups in age, sex, tumor size, calcification, capsule invasion, and strain rates ratio in strain ultrasound elastography (all P < 0.05). In multivariate logistic regression analysis, the independent predictors of occult CCLNM were age (OR = 0.975, 95% CI = 0.959-0.991, P = 0.002), sex (OR = 1.886, 95% CI = 1.220-2.915, P = 0.004), tumor size (OR = 1.054, 95% CI = 1.014-1.097, P = 0.008), and strain rates ratio (OR = 1.178, 95% CI = 1.065-1.304, P = 0.002).

Conclusion

Preoperative strain ultrasound elastography can predict presence of occult CCLNM in papillary thyroid cancer patients and help clinicians select the appropriate treatment strategy.

Risk factor score for the prediction of central compartment lymph node metastasis in papillary thyroid carcinoma and its clinical significance

Objective

To establish the criteria for a risk factor score (RFS) for predicting the probability of central compartment lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) and to explore the clinical significance of the RFS.

Methods

The data of 412 patients with PTC who underwent surgical resection between May 2013 and July 2016 were retrospectively analysed and divided into two groups: a central LNM group and a non-central LNM group. In each group, the frequency of six risk factors was documented: sex, age, tumour size, extracapsular spread (ECS), tumour multifocality, and tumour location. The maximum likelihood method of discriminant analysis was adopted to calculate patient scores for the six risk indicators. In addition, the data of 104 patients with PTC admitted between July 2016 and December 2016 were prospectively analysed using this method and these six risk factors. A higher score represented one certain possibility that was the more appropriate for one patient.

Results

In the retrospective group, the result was as follows: 129 patients with positive (+) lymph nodes in the central compartment and 168 patients with negative (-) lymph nodes in the central compartment, which was in line with the actual results. In the prospective group, there were 28 patients with positive lymph nodes in the central compartment and 48 patients with negative lymph nodes in the central compartment. The coincidence rates using the RFS were 71.9% for the retrospective group and 73.1% for the prospective group.

Conclusion

By simple and quantitative analyses of the presence of central LNM, the RFS is of great significance when choosing surgical approaches and postoperative individual-based treatment plans, as well as when determining the prognosis of central compartment LNM in patients with PTC.

HIF-1α and Caspase-3 expression in aggressive papillary thyroid carcinoma

Background

Tumor cells adapt to hypoxia by regulating transcription factors that involved in regulation of metabolism, angiogenesis, cell proliferation, and apoptosis. Under hypoxic conditions, hypoxia-inducible factor-1 (HIF-1), consisting of HIF-1α and HIF-1β subunits, acts as a key transcription factor mediating the adaptive cellular responses. Caspase-3 is a key apoptosis-related protease that plays a role in tumor growth and development. Studies have shown that caspase-3 could be regulated by HIF-1α under pathological conditions. Therefore, HIF-1α and caspase-3 expression may be related to the poor prognosis of tumors. In this study, we analyzed the possible relationships between these two signaling factors in correlation with the clinical behavior of PTC.

Methods

We detected the expression levels of HIF-1α and caspase-3 in 70 samples of PTC and para-cancerous tissues (control group) by immunohistochemistry (IHC). Furthermore, various clinicopathological parameters were assessed to determine their correlations with HIF-1α and caspase-3 expressions.

Results

First, HIF-1α and caspase-3 expressions (60% and 37.1%, respectively) increased significantly in the PTC samples as compared to normal tissues (2.9% for both HIF-1α and caspase-3) (p < 0.05) as determined by IHC. Second, although there was no significant difference between the expression of HIF-1α and caspase-3 in regard to gender, age distribution, tumor size, lymph node metastasis, and BRAF^V600E mutation (all p > 0.05), HIF-1α and caspase-3 expressions were associated with capsule invasion and cell subtypes of PTC (p < 0.05). The percent positivity of caspase-3 expression in tall-cell variant (TCV) was the highest (63.6%). Third, HIF-1α expression was positively correlated with that of caspase-3 (r_s = 0.326; p < 0.05).

Conclusions

Overexpression of HIF-1α and caspase-3 is associated with carcinogenesis. These factors might serve as promising predictors of aggressive PTC. These findings also suggest their potential as therapeutic targets.

Prediction of Central Lymph Node Metastasis in cN0 Papillary Thyroid Carcinoma by CT Radiomics

RATIONALE AND OBJECTIVES

To explore the feasibility of the preoperative prediction of pathological central lymph node metastasis (CLNM) status in patients with negative clinical lymph node (cN0) papillary thyroid carcinoma (PTC) using a computed tomography (CT) radiomics signature.

MATERIALS AND METHODS

A total of 97 PTC cN0 nodules with CLNM pathology data (pN0, with CLNM, n = 59; pN1, without CLNM, n = 38) in 85 patients were divided into a training set (n = 69) and a validation set (n = 28). For each lesion, 321 radiomic features were extracted from nonenhanced, arterial and venous phase CT images. Minimum redundancy and maximum relevance and the least absolute shrinkage and selection operator were used to find the most important features with which to develop a radiomics signature in the training set. The performance of the radiomics signature was evaluated by receiver operating characteristic curves, calibration curves and decision curve analysis .

RESULTS

Three nonzero the least absolute shrinkage and selection operator coefficient features were selected for radiomics signature construction. The radiomics signature for distinguishing the pN0 and pN1 groups achieved areas under the curve of 0.79 (95% CI 0.67, 0.91) in the training set and 0.77 (95% CI 0.55, 0.99) in the validation set. The calibration curves demonstrated good agreement between the radiomics score-predicted probability and the pathological results in the two sets (p= 0.399, p = 0.191). The decision curve analysis curves showed that the model was clinically useful.

CONCLUSION

This radiomic signature could be helpful to predict CLNM status in cN0 PTC patients.

Ultrasound-based radiomics analysis for preoperative prediction of central and lateral cervical lymph node metastasis in papillary thyroid carcinoma: a multi-institutional study

Background

An accurate preoperative assessment of cervical lymph node metastasis (LNM) is important for choosing an optimal therapeutic strategy for papillary thyroid carcinoma (PTC) patients. This study aimed to develop and validate two ultrasound (US) nomograms for the individual prediction of central and lateral compartment LNM in patients with PTC.

Methods

A total of 720 PTC patients from 3 institutions were enrolled in this study. They were categorized into a primary cohort, an internal validation, and two external validation cohorts. Radiomics features were extracted from conventional US images. LASSO regression was used to select optimized features to construct the radiomics signature. Two nomograms integrating independent clinical variables and radiomics signature were established with multivariate logistic regression. The performance of the nomograms was assessed with regard to discrimination, calibration, and clinical usefulness.

Results

The radiomics scores were significantly higher in patients with central/lateral LNM. A radiomics nomogram indicated good discrimination for central compartment LNM, with an area under the curve (AUC) of 0.875 in the training set, the corresponding value in the validation sets were 0.856, 0.870 and 0.870, respectively. Another nomogram for predicting lateral LNM also demonstrated good performance with an AUC of 0.938 and 0.905 in the training and internal validation cohorts, respectively. The AUC for the two external validation cohorts were 0.881 and 0.903, respectively. The clinical utility of the nomograms was confirmed by the decision curve analysis.

Conclusion

The nomograms proposed here have favorable performance for preoperatively predicting cervical LNM, hold promise for optimizing the personalized treatment, and might greatly facilitate the decision-making in clinical practice.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12880-022-00809-2.

Integrating BRAFV600E mutation, ultrasonic and clinicopathologic characteristics for predicting the risk of cervical central lymph node metastasis in papillary thyroid carcinoma

Background

The advantages of prophylactic central lymph node dissection (CLND) for clinically node-negative patients remained a great deal of controversies. Our research was aimed to analyze the relationship between cervical central lymph node metastasis (CLNM) and BRAF^V600E mutation, ultrasonic and clinicopathologic characterizes in papillary thyroid carcinoma (PTC).

Methods and materials

In current study, a total of 112 consecutive PTC patients who experienced thyroidectomy plus cervical central neck dissection were included in our research. All PTC were pre-operatively analyzed by ultrasonic features, including tumor size, multifocality or not, tumor location, internal components, echogenicity, microcalcification, margins, orientation, taller than wide shape, and internal vascularity. The presence of clinicopathologic factors, including age, sex, T stage, Hashimoto’s thyroiditis, and BRAF^V600E mutation was then investigated. Univariate and multivariate analysis were conducted to check into the relationship between predictive factors and cervical CLNM in PTC patients, and then a predictive model was also established.

Results

Pathologically, 58.0% (65/112) of the PTC patients harbored cervical CLNM. Univariate and multivariate analysis were conducted to identify age < 55 years, tumor size > 10 mm, microcalcification, non-concomitant Hashimoto’s thyroiditis and BRAF^V600E mutation were predictive factors for cervical CLNM in PTC. The risk score for cervical CLNM in PTC patients was calculated: risk score = 1.284 × (if age < 55 years) + 1.241 × (if tumor size > 10 mm) + 1.143 × (if microcalcification) – 2.097 × (if concomitant Hashimoto’s thyroiditis) + 1.628 × (if BRAF^V600E mutation).

Conclusion

Age < 55 years old, PTC > 10 mm, microcalcification, non-concomitant Hashimoto’s thyroiditis and BRAF^V600E mutation are predictive factors for cervical CLNM. BRAF^V600E mutation by pre-operative US-FNA technology synergized with clinicopathologic and ultrasonic features is expected to guide the appropriate surgical management for PTC patients.

Predicting papillary thyroid carcinoma cervical lymph node metastases: an algorithm using the American College of Radiology Thyroid Imaging, Reporting and Data System

BACKGROUND

It is important to predict lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) preoperatively; however, the relationship between the American College of Radiology Thyroid Imaging, Reporting and Data System (ACR TI-RADS) score and cervical LNM remains unclear.

PURPOSE

To evaluate the association between the ACR TI-RADS score and cervical LNM in patients with PTC.

MATERIAL AND METHODS

This retrospective study consisted of 474 patients with 548 PTCs. Cervical LNM including central LNM (CLNM) and lateral LNM (LLNM) were confirmed by pathology. Univariate and multivariate analyses were performed to investigate the risk factors of CLNM and LLNM.

RESULTS

Multivariate logistic regression analyses indicated that younger age and multifocality were risk factors for CLNM in PTCs with TR5. In addition, younger age, larger tumor size, and Hashimoto's thyroiditis (HT) were risk factors for LLNM in PTCs ≥ 10 mm with TR5. In PTCs with TR4, ACR TI-RADS scores 5-6 conferred risks for LNM. In PTCs ≥ 10 mm with TR5, ACR TI-RADS scores ≥9 were risk factors for LLNM.

CONCLUSION

A higher ACR TI-RADS score is a predictor for cervical LNM in PTCs with TR4 and PTCs ≥ 10 mm with TR5.

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.

A comparative analysis of eight machine learning models for the prediction of lateral lymph node metastasis in patients with papillary thyroid carcinoma

BACKGROUND

Lateral lymph node metastasis (LLNM) is a contributor for poor prognosis in papillary thyroid cancer (PTC). We aimed to develop and validate machine learning (ML) algorithms-based models for predicting the risk of LLNM in these patients.

METHODS

This is retrospective study comprising 1236 patients who underwent initial thyroid resection at our institution between January 2019 and March 2022. 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, Random Forest (RF), Decision Tree, Neural Network, Support Vector Machine and Bayesian Network were used to evaluate the risk of LLNM. The performance of ML models was evaluated by the area under curve (AUC), sensitivity, specificity, and decision curve analysis.

RESULTS

Among the eight ML algorithms, RF had the highest AUC (0.975), with sensitivity and specificity of 0.903 and 0.959, respectively. It was therefore used to develop as prediction model. The diagnostic performance of RF algorithm was dependent on the following nine top-rank variables: central lymph node ratio, size, central lymph node metastasis, number of foci, location, body mass index, aspect ratio, sex and extrathyroidal extension.

CONCLUSION

By combining clinical and sonographic characteristics, ML algorithms can achieve acceptable prediction of LLNM, of which the RF model performs best. ML algorithms can help clinicians to identify the risk probability of LLNM in 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.

Ultrasound-Based Radiomic Nomogram for Predicting Lateral Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma

RATIONALE AND OBJECTIVES

Accurate preoperative identification of lateral cervical lymph node metastasis (LNM) is important for decision-making and clinical management of patients with papillary thyroid carcinoma (PTC). The aim of this study was to develop an ultrasound (US)-based radiomic nomogram to preoperatively predict the lateral LNM in PTC patients.

METHODS

In this retrospective study, a total of 886 patients were enrolled and randomly divided into 2 groups. Radiomic features were extracted from the preoperative US images. A radiomic signature was constructed using the least absolute shrinkage and selection operator algorithm in the training set. Multivariate logistic regression was performed to develop the radiomic nomogram, which incorporating the radiomic signature and the selected clinical characteristics. The performance of the nomogram was assessed by its discrimination, calibration, and clinical usefulness in both the training and validation sets.

RESULTS

The radiomic signature was significantly associated with the lateral LNM in both cohorts (p< 0.001). The nomogram that consisted of radiomic signature, US-reported cervical lymph node (CLN) status, and CT-reported CLN status demonstrated good discrimination and calibration in the training and validation sets with an AUC of 0.946 and 0.914, respectively. The decision curve analysis indicated that the radiomic nomogram was worthy of clinical application.

CONCLUSION

The radiomic nomogram proposed here has good performance for noninvasively predicting the lateral LNM and might be used to facilitate clinical decision-making and potentially improve the survival outcome in selected patients.

A Comparative Analysis of Six Machine Learning Models Based on Ultrasound to Distinguish the Possibility of Central Cervical Lymph Node Metastasis in Patients With Papillary Thyroid Carcinoma

Current approaches to predict central cervical lymph node metastasis (CLNM) in patients with papillary thyroid carcinoma (PTC) have failed to identify patients who would benefit from preventive treatment. Machine learning has offered the opportunity to improve accuracy by comparing the different algorithms. We assessed which machine learning algorithm can best improve CLNM prediction. This retrospective study used routine ultrasound data of 1,364 PTC patients. Six machine learning algorithms were compared to predict the possibility of CLNM. Predictive accuracy was assessed by sensitivity, specificity, positive predictive value, negative predictive value, and the area under the curve (AUC). The patients were randomly split into the training (70%), validation (15%), and test (15%) data sets. Random forest (RF) led to the best diagnostic model in the test cohort (AUC 0.731 ± 0.036, 95% confidence interval: 0.664–0.791). The diagnostic performance of the RF algorithm was most dependent on the following five top-rank features: extrathyroidal extension (27.597), age (17.275), T stage (15.058), shape (13.474), and multifocality (12.929). In conclusion, this study demonstrated promise for integrating machine learning methods into clinical decision-making processes, though these would need to be tested prospectively.

Nomogram to assess risk of central cervical lymph node metastasis in patients with cN0 papillary thyroid carcinoma

OBJECTIVE

This study developed and validated an individualized prediction risk model for the need for central cervical lymph node dissection in patients with cN0 papillary thyroid carcinoma (PTC) diagnosed by ultrasound.

METHODS

Upon retrospective review, derivation and internal validation cohorts comprised 1585 consecutive patients with PTC treated from January 2017 to December 2019 at Hospital A. The external validation cohort consisted of 406 consecutive patients treated at Hospital B from January 2016 to June 2020. Independent risk factors for central cervical lymph node metastasis (CLNM) were determined through univariable and multivariable logistic regression analysis. An individualized risk prediction model was constructed and illustrated as a nomogram, which was internally and externally validated.

RESULTS

The following risk factors of CLNM were established: the solitary primary thyroid nodule's diameter, shape, calcification, and capsular abutment-to-lesion perimeter ratio. The areas under the receiver operating characteristic curves of the risk prediction model for the internal and external validation cohorts were 0.921 and 0.923, respectively. The calibration curve showed good agreement between the nomogram-estimated probability of CLNM and the actual CLNM rate in the three cohorts. The decision curve analysis confirmed the clinical usefulness of the nomogram.

CONCLUSION

This study developed and validated a model for predicting risk of CLNM in the individual patient with cN0 PTC, which should be an efficient tool for guiding clinical treatment.

Dual-energy computed tomography could reliably differentiate metastatic from non-metastatic lymph nodes of less than 0.5 cm in patients with papillary thyroid carcinoma

BACKGROUND

Dual-energy computed tomography (DECT) has been widely applied to detect lymph node (LN) and lymph node metastasis (LNM) in various cancers, including papillary thyroid carcinoma (PTC). This study aimed to quantitatively evaluate metastatic cervical lymph nodes (LNs) <0.5 cm in patients with PTC using DECT, which has not been done in previous studies.

METHODS

Preoperative DECT data of patients with pathologically confirmed PTC were retrospectively collected and analyzed between May 2016 and June 2018. A total of 359 LNs from 52 patients were included. Diameter, iodine concentration (IC), normalized iodine concentration (NIC), and the slope of the energy spectrum curve (λHU) of LNs in the arterial and the venous phases were compared between metastatic and non-metastatic LNs. The optimal parameters were obtained from the receiver operating characteristic (ROC) curves. The generalized estimation equation (GEE) model was used to evaluate independent diagnostic factors for LNM.

RESULTS

A total of 139 metastatic and 220 non-metastatic LNs were analyzed. There were statistical differences of quantitative parameters between the two groups (P value 0.000-0.007). The optimal parameter for diagnosing LNM was IC in the arterial phase, and its area under the curve (AUC), sensitivity, and specificity were 0.775, 71.9%, and 73.6%, respectively. When the three parameters of diameter, IC in the arterial phase, and NIC in the venous phase were combined, the prediction efficiency was better, and the AUC was 0.819. The GEE results showed that LNs located in level VIa [odds ratio (OR) 2.030, 95% confidence interval (CI): 1.134-3.634, P=0.017], VIb (OR 2.836, 95% CI: 1.597-5.038, P=0.000), diameter (OR 2.023, 95% CI: 1.158-3.532, P=0.013), IC in the arterial phase (OR 4.444, 95% CI: 2.808-7.035, P=0.000), and IC in the venous phase (OR 5.387, 95% CI: 3.449-8.413, P=0.000) were independent risk factors for LNM in patients with PTC.

CONCLUSIONS

DECT had good diagnostic performance in the differentiation of cervical metastatic LNs <0.5 cm in patients with PTC.

Enemy Beyond the Wall: Clinicopathological Features of Intrathyroidal Papillary Thyroid Carcinoma (T1-T3a) That Metastasize to Lymph Nodes Without Extrathyroidal Extension

The aim of this study was to determine the clinicopathological features of papillary thyroid carcinomas (PTC) without extrathyroidal extension (ETE) and with lymph node metastasis (LNM). PTC > 1 cm increased the risk of LNM by 2.161 times compared to papillary thyroid microcarcinoma. The risk increased by 3.774 times in males and 1.553 times in the presence of multifocality. Presence of vascular invasion (VI) increased the risk of LNM by 3.093 times in patients without capsular invasion (CI). Clinicians should be careful about possible LNM in patients with large primary tumor diameter, multifocal tumors, CI and VI.

Thyroid Parenchyma Microcalcifications on Ultrasound for Predicting Lymph Node Metastasis in Papillary Thyroid Carcinoma: A Prospective Multicenter Study in China

Objective: Cervical lymph node metastasis (LNM) was found to be clinically significant prognostic factors of patients with papillary thyroid carcinomas (PTC). Ultrasound (US) characteristics of thyroid nodules and thyroid parenchyma may be used to predict LNM. To investigate the value of nodular US features as well as thyroid parenchymal microcalcifications on US in predicting LNM in patients with PTC. Methods: This prospective study was approved by the Institutional Review Board. From January 2018 to June 2019, 971 consecutive patients with solitary PTC who underwent preoperative neck US evaluation were included from six hospitals in China. The US features of thyroid nodules as well as thyroid parenchyma microcalcifications were carefully evaluated based on the static images and dynamic clips. Univariate and multivariate analyses were performed to determine independent predictors of LNM. Results: Of the 971 patients, 760 were female, 211 were male. According to the pathological examination, 241(24.82%) patients were found with cervical LNM (LNM positive group), while 730 (75.18%) patients were not (LNM negative group). Multiple logistic regression analysis showed that young age (<55 years old) (OR = 1.522, P = 0.047), large size (>10 mm) (OR = 1.814, P < 0.001), intratumoral microcalcifications (OR = 1.782, P = 0.002) and thyroid parenchyma microcalcifications (OR = 1.635, P = 0.046) were independent risk factors for LNM of PTC. Conclusions: Young age, large nodule size, intratumoral microcalcifications, as well as thyroid parenchyma microcalcifications on US are independent predictors of cervical LNM for patients with PTC.

Predictors and a Prediction Model for Central Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma (cN0)

OBJECTIVES

To screen out the predictors of central cervical lymph node metastasis (CLNM) for papillary thyroid carcinoma (PTC) and establish a prediction model to guide the operation of PTC patients (cN0).

METHODS

Data from 296 PTC patients (cN0) who underwent thyroid operation at the Second Affiliated Hospital of Chongqing Medical University were collected and retrospectively analyzed. They were divided into two groups in accordance with central CLNM or not. Their information, including ultrasound (US) features, BRAF^V600E status, and other characteristics of the two groups, was analyzed and compared using univariate and multivariate logistic regression analyses, and the independent predictors were selected to construct a nomogram. The calibration plot, C-index, and decision curve analysis were used to assess the prediction model's calibration, discrimination, and clinical usefulness.

RESULTS

A total of 37.8% (112/296) of PTC patients had central CLNM, and 62.2% (184/296) did not. The two groups were compared using a univariate logistic regression analysis, and there were no significant differences between the two groups in sex, aspect ratio, boundary, morphology, hypoechoic nodule, thyroid peroxidase antibody, or tumor location (P>0.05), and there were significant differences between age, tumor size, capsule contact, microcalcifications, blood flow signal, thyroglobulin antibodies (TgAb), and BRAF gene status (P<0.05). A multivariate logistic regression analysis was performed to further clarify the correlation of these indices. However, only tumor size (OR=2.814, 95% Cl=1.634~4.848, P<0.001), microcalcifications (OR=2.839, 95% Cl=1,684~4.787, P<0.001) and TgAb (OR=1.964, 95% Cl=1.039~3,711, P=0.038) were independent predictors of central CLNM and were incorporated and used to construct the prediction nomogram. The model had good discrimination with a C-index of 0.715. An ROC curve analysis was performed to evaluate the accuracy of this model. The decision curve analysis showed that the model was clinically useful when intervention was decided in the threshold range of 16% to 80%.

CONCLUSION

In conclusion, three independent predictors of central CLNM, including tumor size (> 1.0 cm), US features (microcalcifications), and TgAb (positive), were screened out. A visualized nomogram model was established based on the three predictors in this study, which could be used as a basis of central cervical lymph node dissection (CLND) for PTC patients (cN0).

Clinical Relevance and Management of Recurrent Laryngeal Nerve Inlet Zone Lymph Nodes Metastasis in Papillary Thyroid Cancer

BACKGROUND

Recurrent nodal disease often occurs in recurrent laryngeal nerve inlet zone (RLNIZ), leading to difficult surgical management.

METHODS

Medical records of 947 patients with PTC and 33 patients with recurrent PTC were retrospectively reviewed. Totally 169 sides of RLNIZ dissection in 152 patients (17 cases were bilateral and 135 cases were unilateral) with primary surgery and 4 patients with structural recurrent disease were included for the analysis.

RESULTS

The rate of lymph node metastasis in RLNIZ was 31.3% (47/150). The incidence of transient hypoparathyroidism was 5.9% and no RLN injury and permanent hypoparathyroidism occurred. RLNIZ lymph nodes metastasis (LNM) was significantly associated with age <45 years, larger tumor size, number of CNLNM, and lateral node metastasis. CNLNM and lateral node metastasis were independent risk factors for RLNIZ LNM. Recurrent nodal disease in RLNIZ was identified in four of 33 patients and permanent recurrent laryngeal nerve (RLN) injury was observed in one of four patients.

CONCLUSION

Lymph nodes in RLNIZ are usually involved in patients with heavy tumor burden and can be removed safely at initial surgery. Once central or lateral LNM was confirmed preoperatively or intraoperatively, RLNIZ lymph node dissection should be carefully performed to reduce the rate of structural recurrence in the central compartment.

Shear wave elastography combined with gray-scale ultrasound for predicting central lymph node metastasis of papillary thyroid carcinoma

OBJECTIVE

To investigate the relationship between shear wave elastography (SWE) elasticity indices of papillary thyroid carcinoma (PTC) and central lymph node metastasis (CLNM) and to evaluate the value of SWE combined with gray-scale ultrasound (US) for predicting preoperative CLNM.

METHOD

This study included 172 patients with a pathology diagnosis of PTC who underwent preoperative gray-scale US and SWE evaluation. Patients were divided into CLNM-positive and CLNM-negative groups. We analyzed the association between SWE elasticity indices (Emax, Emean and Emin) and CLNM, compared the diagnostic efficacy of gray-scale US alone versus SWE combined with gray-scale US for predicting CLNM, and analyzed the influence of Hashimoto's thyroiditis (HT) on the diagnostic efficacy of CLNM.

RESULTS

SWE elasticity values Emax, Emean and Emin were significantly higher in CLNM-positive patients (P=0.000, 0.000 and 0.003, respectively). The AUC of Emax was higher than that of other SWE indices for predicting CLNM (AUC = 0.749; 95% CI = 0.676-0.822). In multivariate analysis, microcalcification (OR = 5.254; 95% CI = 2.496-11.061), extrathyroidal extension (OR = 4.210; 95% CI = 1.423-12.456), multifocality (OR = 3.084; 95% CI = 1.190-7.991) and Emax >59.0 kpa (OR = 4.934; 95% CI = 2.318-10.500) were independent risk factors for predicting CLNM. The AUC of SWE combined with gray-scale US for predicting CLNM (AUC = 0.825; 95% CI = 0.760-0.879) was significantly higher (P = 0.011) than that for gray-scale US alone (AUC = 0.774; 95% CI = 0.704-0.834). There was no significant difference in AUC between the HT and non-HT subgroups in predicting CLNM (0.798 vs. 0.833, P = 0.640).

CONCLUSIONS

SWE can be used to predict CLNM in PTC patients. SWE combined with gray-scale US can improve the prediction of CLNM.

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 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.

Total thyroidectomy versus thyroid lobectomy in the treatment of papillary carcinoma

Extent of thyroidectomy for papillary thyroid carcinoma is still matter of debate. Indeed, recently, international guidelines endorsed thyroid lobectomy as initial surgical approach for low risk, small medium-sized (T1-T2), N0 papillary thyroid carcinoma in absence of extrathyroidal extension. When dealing with a conservative surgery for oncologic disease is of utmost importance to exclude effectively more advanced disease, which could benefit from a more aggressive initial operation. However, in the setting of surgery for papillary thyroid carcinoma, despite an accurate preoperative work up could led to identify some suspicious characteristics as macroscopic evidence of multifocality or extrathyroidal extension, and/or evidence of lateral neck lymph node metastases, it is difficult to reliably assess the central neck nodal status both pre- and intra-operatively. Frozen section examination of the central neck nodes ipsilateral to the side of the tumor has been proposed in patients scheduled for thyroid lobectomy, in order to modulate the extension of both thyroidectomy and central neck dissection. Future molecular and genetic evidences are needed to establish high-risk patients with small papillary thyroid carcinoma in which thyroid lobectomy could be not and adequate surgical treatment.