Establishment and validation of the scoring system for preoperative prediction of central lymph node metastasis in papillary thyroid carcinoma

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.

Preoperative prediction of central lymph node metastasis in follicular variant of papillary thyroid carcinoma using clinical and ultrasound features

Background

The most common metastatic site of follicular variant of papillary thyroid carcinoma (FVPTC) is the central lymph nodes, which may be associated with the prognosis and survival of patients. In the present study, we establish a combined model based on preoperative clinical and ultrasound (US) features of FVPTC to predict the risk of central lymph node metastasis (CLNM).

Methods

From January 2013 to December 2022, 315 patients with FVPTC were enrolled and randomly divided into the training and validation cohorts in a ratio of 7:3. The independent risk factors for CLNM in FVPTC were analysed using univariate and multivariate logistic regression analyses. Then, three different models were established based on clinical and US data. Subsequently, a nomogram was constructed to predict CLNM. Its predictive effect was evaluated via receiver operating characteristic and calibration curve analyses.

Results

Backward multivariate regression analysis revealed that age (P=0.001), thyroid peroxidase antibody (TPOAb) (P=0.11), diameter (P=0.047), irregular/lobulated margin (P=0.15), extrathyroidal extension (P=0.001), nodules with macrocalcifications (P=0.009), nodules with microcalcification (P=0.003) and Thyroid Imaging Reporting and Data System (ACR-TI-RADS) category 5 (P=0.33) were independent risk factors for CLNM in FVPTC. The areas under the curve of the matching nomogram in the training (N=221) and validation cohorts (N=94) were 0.841 [95% confidence interval (CI): 0.788-0.895] and 0.735 (95% CI: 0.621-0.872), respectively.

Conclusions

Preoperative thyroid US provides useful features for prediction of CLNM. The nomogram constructed based on combining US and clinical features can better predict the risk of CLNM and may facilitate decision-making in clinical settings.

Cervical lymph node metastasis prediction from papillary thyroid carcinoma US videos: a prospective multicenter study

BACKGROUND

Prediction of lymph node metastasis (LNM) is critical for individualized management of papillary thyroid carcinoma (PTC) patients to avoid unnecessary overtreatment as well as undesired under-treatment. Artificial intelligence (AI) trained by thyroid ultrasound (US) may improve prediction performance.

METHODS

From September 2017 to December 2018, patients with suspicious PTC from the first medical center of the Chinese PLA general hospital were retrospectively enrolled to pre-train the multi-scale, multi-frame, and dual-direction deep learning (MMD-DL) model. From January 2019 to July 2021, PTC patients from four different centers were prospectively enrolled to fine-tune and independently validate MMD-DL. Its diagnostic performance and auxiliary effect on radiologists were analyzed in terms of receiver operating characteristic (ROC) curves, areas under the ROC curve (AUC), accuracy, sensitivity, and specificity.

RESULTS

In total, 488 PTC patients were enrolled in the pre-training cohort, and 218 PTC patients were included for model fine-tuning (n = 109), internal test (n = 39), and external validation (n = 70). Diagnostic performances of MMD-DL achieved AUCs of 0.85 (95% CI: 0.73, 0.97) and 0.81 (95% CI: 0.73, 0.89) in the test and validation cohorts, respectively, and US radiologists significantly improved their average diagnostic accuracy (57% vs. 60%, P = 0.001) and sensitivity (62% vs. 65%, P < 0.001) by using the AI model for assistance.

CONCLUSIONS

The AI model using US videos can provide accurate and reproducible prediction of cervical lymph node metastasis in papillary thyroid carcinoma patients preoperatively, and it can be used as an effective assisting tool to improve diagnostic performance of US radiologists.

TRIAL REGISTRATION

We registered on the Chinese Clinical Trial Registry website with the number ChiCTR1900025592.

Trends of the prevalence rate of central lymph node metastasis and multifocality in patients with low-risk papillary thyroid carcinoma after delayed thyroid surgery

Background

Active surveillance has been an option for patients with low-risk papillary thyroid carcinoma (PTC). However, whether delayed surgery leads to an increased risk of local tumor metastasis remain unclear. We sought to investigate the impact of observation time on central lymph node metastasis (CLNM) and multifocal disease in patients with low-risk PTC.

Methods

Patients who were diagnosed with asymptomatic low-risk PTC, and with a pathological maximum tumor size ≤1.5 cm by were included. The patients were classified into observation group and immediate surgery group, and subgroup analyses were conducted by observation time period. The prevalence of CLNM, lymph node (LN) involved >5, multifocal PTC and bilateral multifocal PTC were considered as outcome variables. The changing trend and risk ratio of prevalence over observation time were evaluated by Mann-Kendall trend test and Logistics regression.

Results

Overall, 3,427 and 1,860 patients were classified to the observation group and immediate surgery group, respectively. Trend tests showed that decreasing trends both on the prevalence of CLNM and LN involved >5 over the observation time, but the difference was not statistically significant, and the prevalence of multifocal PTC and bilateral multifocal PTC showed the significant decreasing trends. After adjustment, multivariate analysis showed no statistically significant difference between observed and immediate surgery groups in the four outcome variables.

Conclusion

In patients with subclinical asymptomatic low-risk PTC, observation did not result in an increased incidence of local metastatic disease, nor did the increased surgery extent in patients with delayed surgery compared to immediate surgery. These findings can strengthen the confidence in the active surveillance management for both doctors and patients.

Multimodal predictive factors of metastasis in lymph nodes posterior to the right recurrent laryngeal nerve in papillary thyroid carcinoma

Objective

The lymph node posterior to the right recurrent laryngeal nerve (LN-prRLN) is a crucial component of the central lymph nodes (LNs). We aimed to evaluate multimodal predictive factors of LN-prRLN metastasis in patients with papillary thyroid carcinomas (PTCs), including the clinical data, pathologic data, and preoperative sonographic characteristics of PTCs.

Methods

A total of 403 diagnosed PTC patients who underwent unilateral, sub-total, or total thyroidectomy with central neck dissection were enrolled in this retrospective study. The clinical data, pathologic data, conventional ultrasound (US) and contrast-enhanced ultrasound (CEUS) characteristics of PTCs were collected and evaluated for predicting LN-prRLN metastasis.

Results

In this study, 96 PTC patients with LN-prRLN metastasis and 307 PTC patients without LN-prRLN metastasis were included. Univariate analysis demonstrated that PTC patients with LN-prRLN metastasis more often had younger age, larger size, multifocal cancers, A/T < 1, well-margins, microcalcification, petal-like calcification, internal vascularity, centripetal perfusion pattern and surrounding ring enhancement. Multivariate logistic regression analysis revealed that the CEUS centripetal perfusion pattern, central LN detected by ultrasound and LN-arRLN metastasis were independent characteristics for predicting LN-prRLN metastasis in PTC patients.

Conclusion

According to our research, it is essential for clinicians to thoroughly dissect central LNs, particularly LN-prRLNs.

Development and validation of a dynamic nomogram based on conventional ultrasound and contrast-enhanced ultrasound for stratifying the risk of central lymph node metastasis in papillary thyroid carcinoma preoperatively

Purpose

The aim of this study was to develop and validate a dynamic nomogram by combining conventional ultrasound (US) and contrast-enhanced US (CEUS) to preoperatively evaluate the probability of central lymph node metastases (CLNMs) for patients with papillary thyroid carcinoma (PTC).

Methods

A total of 216 patients with PTC confirmed pathologically were included in this retrospective and prospective study, and they were divided into the training and validation cohorts, respectively. Each cohort was divided into the CLNM (+) and CLNM (-) groups. The least absolute shrinkage and selection operator (LASSO) regression method was applied to select the most useful predictive features for CLNM in the training cohort, and these features were incorporated into a multivariate logistic regression analysis to develop the nomogram. The nomogram's discrimination, calibration, and clinical usefulness were assessed in the training and validation cohorts.

Results

In the training and validation cohorts, the dynamic nomogram (https://clnmpredictionmodel.shinyapps.io/PTCCLNM/) had an area under the receiver operator characteristic curve (AUC) of 0.844 (95% CI, 0.755-0.905) and 0.827 (95% CI, 0.747-0.906), respectively. The Hosmer-Lemeshow test and calibration curve showed that the nomogram had good calibration (p = 0.385, p = 0.285). Decision curve analysis (DCA) showed that the nomogram has more predictive value of CLNM than US or CEUS features alone in a wide range of high-risk threshold. A Nomo-score of 0.428 as the cutoff value had a good performance to stratify high-risk and low-risk groups.

Conclusion

A dynamic nomogram combining US and CEUS features can be applied to risk stratification of CLNM in patients with PTC in clinical practice.

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.

Quantitative analysis of contrast-enhanced ultrasound combined with ultrasound in the unifocal papillary thyroid micro-carcinoma

OBJECTIVE

To evaluate diagnostic value of ultrasound (US) combined with contrast-enhanced ultrasound (CEUS) in the invasiveness of unifocal papillary thyroid micro-carcinoma (UPTMC) without capsule-invasion.

METHODS

This retrospective study included data from patients with UPTMC who received US and CEUS examinations in the Ultrasound Department of the Central Hospital of Changsha, China between June 2019 and September 2021. Univariate and multivariate logistic regression analysis were used to evaluate the risk of US and CEUS parameters for UPTMC. Diagnostic performance was estimated by ROC analysis.

RESULTS

A total of 136 cases were enrolled, including invasive UPTMC (n = 47) and non-invasive UPTMC (n = 89), which were divided into test set (n = 109) and validation set (n = 27). The occurrence of microcalcification and the ratios (R) of each time-intensity curve (TIC) of CEUS parameter were significantly higher in patients with invasive UTPMC than non-invasive UPTMC (all P < 0.05). Additionally, nodular diameter was significantly longer in the invasive group (P < 0.05). Multivariate analysis showed that microcalcification (OR = 2.917, 95% CI: 1.002-8.491, P = 0.050), R-TTP > 1 (OR = 3.376, 95%CI: 1.267-8.994, P = 0.015), R-DS > 1 (OR = 6.558, 95% CI: 2.358-18.243, P < 0.010) were independently associated with invasive UPTMC. The sensitivities of US, CEUS and their combined application were 82.1%, 46.2% and 79.5%, respectively, and their specificities were 37.1%, 88.6% and 61.4%, respectively. The combination of the two methods had the best diagnostic efficiency (AUC=0.775)compared to US (AUC = 0.596) and CEUS (AUC = 0.750).

CONCLUSION

The combination of US and CEUS might have good diagnostic value for UPTMC with capsule non-invasion.

Nomograms Based on Serum N-glycome for Diagnosis of Papillary Thyroid Microcarcinoma and Prediction of Lymph Node Metastasis

Non-invasive biomarkers for the diagnosis and prognosis of papillary thyroid microcarcinoma (PTMC) are still urgently needed. We aimed to characterize the N-glycome of PTMC, and establish nomograms for the diagnosis of PTMC and the prediction of lymph node metastasis (LNM). N-glycome of PTMC (LNM vs. non-LNM, capsular invasion (CI) vs. non-CI (NCI)) and matched healthy controls (HC) were quantitatively analyzed based on mass spectrometry. N-glycan traits associated with PTMC/LNM were used to create binomial logistic regression models and were visualized as nomograms. We found serum N-glycome differed between PTMC and HC in high-mannose, complexity, fucosylation, and bisection, of which, four N-glycan traits (TM, CA1, CA4, and A2Fa) were significantly associated with PTMC. The nomogram based on four traits achieved good performance for the identification of PTMC. Two N-glycan traits (CA4 and A2F0S0G) showed strong associations with LNM. The nomogram based on two traits showed relatively good performance in predicting LNM. We also found differences between CI and NCI in several N-glycan traits, which were not the same as that associated with LNM. This study reported serum N-glycosylation signatures of PTMC for the first time. Nomograms constructed from aberrant glycans could be useful tools for PTMC diagnosis and stratification.

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.

Real-Time Elastography: A Web-Based Nomogram Improves the Preoperative Prediction of Central Lymph Node Metastasis in cN0 PTC

BACKGROUND

Given the difficulty of accurately determining the central lymph node metastasis (CLNM) status of patients with clinically node-negative (cN0) papillary thyroid carcinoma (PTC) before surgery, this study aims to combine real-time elastography (RTE) and conventional ultrasound (US) features with clinical features. The information is combined to construct and verify the nomogram to foresee the risk of CLNM in patients with cN0 PTC and to develop a network-based nomogram.

METHODS

From January 2018 to February 2020, 1,157 consecutive cases of cN0 PTC after thyroidectomy and central compartment neck dissection were retrospectively analyzed. The patients were indiscriminately allocated (2:1) to a training cohort (771 patients) and validation cohort (386 patients). Multivariate logistic regression analysis of US characteristics and clinical information in the training cohort was performed to screen for CLNM risk predictors. RTE data were included to construct prediction model 1 but were excluded when constructing model 2. DeLong's test was used to select a forecast model with better receiver operator characteristic curve performance to establish a web-based nomogram. The clinical applicability, discrimination, and calibration of the preferable prediction model were assessed.

RESULTS

Multivariate regression analysis showed that age, sex, tumor size, bilateral tumors, the number of tumor contacting surfaces, chronic lymphocytic thyroiditis, and RTE were risk predictors of CLNM in cN0 PTC patients, which constituted prediction model 1. Model 2 included the first six risk predictors. Comparison of the areas under the curves of the two models showed that model 1 had better prediction performance (training set 0.798 vs. 0.733, validation set 0.792 vs. 0.715, p < 0.001) and good discrimination and calibration. RTE contributed significantly to the performance of the prediction model. Decision curve analysis showed that patients could obtain good net benefits with the application of model 1.

CONCLUSION

A noninvasive web-based nomogram combining US characteristics and clinical risk factors was developed in the research. RTE could improve the prediction accuracy of the model. The dynamic nomogram has good performance in predicting the probability of CLNM in cN0 PTC patients.

Analysis of the Relevance of the Ultrasonographic Features of Papillary Thyroid Carcinoma and Cervical Lymph Node Metastasis on Conventional and Contrast-Enhanced Ultrasonography

Background

Preoperative prediction of lymph node metastases has a major impact on prognosis and recurrence for patients with papillary thyroid carcinoma (PTC). Thyroid ultrasonography is the preferred inspection to guide the appropriate diagnostic procedure.

Purpose

To investigate the relationship between PTC and cervical lymph node metastasis (CLNM, including central and lateral LNM) using both conventional ultrasound (US) and contrast-enhanced ultrasound (CEUS).

Material and Methods

Our study retrospectively analyzed 379 patients diagnosed with PTC confirmed by surgical pathology at our hospital who underwent US and CEUS examinations from October 2016 to March 2021. Individuals were divided into two groups: the lymph node metastasis group and the nonmetastasis group. The relationship between US and CEUS characteristics of PTC and CLNM was analyzed. Univariate and multivariable logistic regression methods were used to identify the high-risk factors and established a nomogram to predict CLNM in PTC. Furthermore, we explore the frequency of CLNM at each nodal level in PTC patients.

Results

Univariate analysis indicated that there were significant differences in gender, age, tumor size, microcalcification, contact with the adjacent capsule, multifocality, capsule integrity and enhancement patterns in CEUS between the lymph node metastasis group and the nonmetastasis group (all P<0.05). Multivariate regression analysis showed that tumor size ≥1 cm, age ≤45 years, multifocality, and contact range of the adjacent capsule >50% were independent risk factors for CLNM in PTC, which determined the nomogram. The diagnostic model had an area under the curve (AUC) of 0.756 (95% confidence interval, 0.707-0.805). And calibration plot analysis shown that clinical utility of the nomogram. In 162 PTC patients, the metastatic rates of cervical lymph nodes at levels I-VI were 1.9%, 15.4%, 35.2%, 34.6%, 15.4%, 82.1%, and the difference was statistically significant (P<0.001).

Conclusion

Our study indicated that the characteristics of PTC on ultrasonography and CEUS can be used to predict CLNM as a useful tool. Preoperative analysis of ultrasonographical features has important value for predicting CLNM in PTCs. The risk of CLNM is greater when tumor size ≥1 cm, age ≤45 years, multifocality, contact range of the adjacent capsule >50% are present.

Analysis of Risk Factors Associated With Central Lymph Node Metastasis in Papillary Thyroid Carcinoma With cT1N0 Stage

AIM

Annual T1 stage papillary thyroid carcinoma (PTC) incidence rates continue to rise, yet the optimal treatment for this cancer type remains controversial. Central lymph node metastasis (CLNM) is a critical determinant in the context of treatment decision-making. While several prior studies have evaluated patients with clinica l T1a(cT1a) stage PTC, there have been fewer analyses of clinical T1b(cT1b) disease to date. The present study was thus formulated to explore predictors of CLNM in patients with cT1a and cT1b stage PTC.

METHODS

A retrospective analysis of data including clinicopathological characteristics and BRAF^V600E mutation status was conducted for 452 PTC patients undergoing surgical treatment. Logistic univariate and multivariate analyses were performed to identify risk factors associated with CLNM in particular patients' characteristics and the accuracy of the established logistic regression models was evaluated using the R software platform.

RESULTS

Respective CLNM incidence rates in cT1a and cT1b disease were 39.39% and 67.21%. Factors associated with a higher risk of CLNM among PTC(cT1a) patients included male sex, young age, tumor size, contact with capsule, and multifocality as determined through comparisons of the area under the curve for logistic regression models. Whereas male sex and age were associated with CLNM risk in PTC(cT1b) patients in univariate and multivariate analyses, age was the only risk factor associated with CLNM incidence among women with PTC(cT1b).

CONCLUSION

Predictors of CLNM differ between PTC patients with cT1a and cT1b stage disease, and a comprehensive assessment of these risk factors should thus be conducted when designing individualized treatment regimens for PTC patients.

A model based on clinical data and multi-modal ultrasound for predicting cervical lymph node metastasis in patients with thyroid papillary carcinoma

OBJECTIVE

The aim of this study was to explore diagnostic performance based on clinical characteristics, conventional ultrasound, Angio PLUS (AP), shear wave elastography (SWE), and contrast-enhanced ultrasound (CEUS) for the preoperative evaluation of cervical lymph node metastasis (CLNM) in patients with papillary thyroid carcinoma (PTC) and to find a reliable predictive model for evaluating CLNM.

MATERIALS AND METHODS

A total of 206 thyroid nodules in 206 patients were included. AP, SWE, and CEUS were performed for all thyroid nodules. Univariate analysis and multivariate logistic regression analysis were performed to ascertain the independent risk factors. The sensitivity, specificity, and the area under the curve (AUC) of independent risk factors and the diagnostic model were compared.

RESULTS

Sex, age, nodule size, multifocality, contact extent with adjacent thyroid capsule, Emax, and capsule integrity at CEUS were independent risk predictors for CLNM in patients with PTC. A predictive model was established based on the following multivariate logistic regression: Logit (p) = -2.382 + 1.452 × Sex - 1.064 × Age + 1.338 × Size + 1.663 × multifocality + 1.606 × contact extent with adjacent thyroid capsule + 1.717 × Emax + 1.409 × capsule integrity at CEUS. The AUC of the predictive model was 0.887 (95% CI: 0.841-0.933), which was significantly higher than using independent risk predictors alone.

CONCLUSION

Our study found that male presence, age < 45 years, size ≥ 10 mm, multifocality, contact extent with adjacent thyroid capsule > 25%, Emax ≥ 48.4, and interrupted capsule at CEUS were independent risk predictors for CLNM in patients with PTC. We developed a diagnostic model for predicting CLNM, which could be a potentially useful and accurate method for clinicians; it might be beneficial to surgical decision-making and patient management and for improving prognosis.

The active surveillance management approach for patients with low risk papillary thyroid microcarcinomas: is China ready?

Due to exponential increases in incidences, low risk papillary thyroid microcarcinoma (PTMC) has become a clinical and social issue in recent years. An active surveillance (AS) management approach is an alternative to immediate surgery for patients with low risk PTMC. With decreased doubts about the safety and validity due to evidence from a large number of studies, the AS approach has become increasingly popular worldwide. However, Chinese thyroid surgeons still lag behind other countries in their knowledge of clinical practices and research related to AS. To promote the implementation of AS in China, thyroid surgeons should understand the implications, advantages, and disadvantages of management approaches for AS, and should also consider the willingness of Chinese patients, the impact on the medical billing system, and the enthusiasm of doctors. Thus, a management approach for AS based on the Chinese population should be developed to reduce the risk of disease progression and enhance patient adherence. Herein, we summarize the recent research achievements and deficiencies in AS approaches, and describe the initial experiences regarding AS in the Chinese population, in order to assist Chinese thyroid surgeons in preparing for AS management in the era of PTMC precision medicine.

Value of Combining Clinical Factors, Conventional Ultrasound, and Contrast-Enhanced Ultrasound Features in Preoperative Prediction of Central Lymph Node Metastases of Different Sized Papillary Thyroid Carcinomas

Purpose

Early and accurate preoperative diagnosis of central lymph node metastasis (CLNM) is crucial to improve surgical management of patients with clinical lymph node-negative papillary thyroid carcinoma (PTC). Towards improving diagnosis of CLNM, we assessed the value of combining preoperative clinical characteristics, conventional ultrasound, and contrast-enhanced ultrasound (CEUS) in preoperative prediction of CLNM of different sized PTCs.

Patients and Methods

Patients were divided according to tumor size: a PTC group (>10 mm) and a papillary thyroid microcarcinoma (PTMC) group (≤10 mm). We retrospectively analyzed the clinical and ultrasonographic features of 120 PTC patients and 165 PTMC patients. Multivariate logistic regression analysis was used to screen independent risk factors and establish prediction models. Receiver operating characteristic curves were used to determine the best cut-off values for continuous variables and assess the performance of prediction models.

Results

Independent risk predictors of CLNM for the PTC group were extrathyroidal extension in CEUS (OR=7.923), tumor size >14 mm (OR=5.491), and multifocality (OR=3.235). For the PTMC group, the independent risk factors were the distance from the thyroid capsule =0 mm (OR=4.629), male (OR=3.315), tumor size >5 mm (OR=3.304), and microcalcification (OR=2.560). The predictive model of combined method had better performance in predicting CLNM of PTC compared with models based on CEUS and conventional ultrasound alone (area under the curve: 0.832 vs 0.739, P=0.0011; 0.832 vs 0.678, P=0.0012). For PTMC, comparing with CEUS, the combined method and conventional ultrasound performed better than CEUS alone in predicting CLNM (area under the curve: 0.783 vs 0.636, P=0.0016; 0.738 vs 0.636, P=0.0196).

Conclusion

The predictive models of combined method obtained from significant preoperative clinical and ultrasonographic features can potentially improve the preoperative diagnosis and individual treatment of CLNM in patients with PTC and PTMC. CEUS may be helpful in predicting CLNM of PTC, but CEUS would be ineffective in predicting CLNM of PTMC.

Prediction model for cervical lymph node metastasis in human papillomavirus-related oropharyngeal squamous cell carcinomas

OBJECTIVES

To develop and validate a risk scoring system based on clinical and imaging findings to predict lymph node metastasis from HPV-related oropharyngeal squamous cell carcinomas.

METHODS

This study population who had undergone neck dissections or lymph node biopsies in patients with HPV+ OPSCC was obtained from a historical cohort from two tertiary referral hospitals. The training set from one hospital included 455 lymph nodes from 82 patients, and the test set from the other hospital included 150 lymph nodes from 42 patients. The baseline clinical and imaging findings on pretreatment CT or MR were investigated and the reference standards were the histopathologic results. A risk scoring system was constructed based on logistic regression and validated both internally and externally.

RESULTS

A 7-point risk scoring system was developed based on the following variables: central necrosis, infiltration of adjacent planes, lymph node level, and the maximal axial diameter of the lymph node. This risk scoring system showed good discriminative ability for metastasis in the training set (C-statistic 0.952; 95% CI, 0.931-0.972) and test set (C-statistic 0.968, 95% CI, 0.936-0.999) and good calibration ability in the training set (p = 0.723) and test set (p = 0.253).

CONCLUSIONS

We developed and validated a reliable risk scoring system that predicts lymph node metastasis from HPV+ OPSCCs based on the clinical data and pretreatment imaging findings. We expect this risk scoring system to be a useful guide for better decision-making in practice.

KEY POINTS

• It is important to diagnose lymph node metastasis from HPV+ OPSCC for treatment planning; however, there has been little research on that. • We developed and externally validated a new scoring system for stratifying the risk of lymph node metastasis from HPV+ OPSCC based on clinical and imaging data. • A predictive model combining both clinical and imaging data showed high diagnostic accuracy and efficiency for lymph node metastasis from HPV+ OPSCC.

Sonographic Characteristics of Papillary Thyroid Carcinoma With Coexistent Hashimoto's Thyroiditis in the Preoperative Prediction of Central Lymph Node Metastasis

The purpose of this study was to evaluate the usefulness of the sonographic characteristics of papillary thyroid carcinoma (PTC) with Hashimoto's thyroiditis (HT) for predicting central lymph node metastasis (CLNM). One hundred thirty-three patients who underwent thyroidectomy and central cervical lymph node dissection for PTC with coexistent HT were retrospectively analyzed. All PTCs with HT were preoperatively evaluated by ultrasound (US) regarding their nodular number, size, component, shape, margin, echogenicity, calcification, capsule contact with protrusion, vascularity and contrast enhanced ultrasound (CEUS) parameters. Univariate analysis demonstrated that patients with PTCs with HT and CLNM more frequently had age ≤ 45 years, size > 10 mm, a wider than tall shape, microcalcification, hypo-enhancement and peak intensity index < 1 than those without CLNM (all p<0.05). Binary logistic regression analysis demonstrated that size > 10 mm and CEUS hypo-enhancement were independent characteristics for the presence of CLNM. Our study indicated that preoperative US characteristics could offer help in predicting CLNM in PTCs with coexistent HT.

Preoperative Prediction of Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma via Conventional and Contrast-Enhanced Ultrasound

OBJECTIVES

To investigate the independent risk factors for cervical lymph node metastasis (CLNM) in patients with papillary thyroid carcinoma (PTC) and establish a prediction model via conventional ultrasound (US) and contrast-enhanced ultrasound (CEUS).

METHODS

A total of 275 patients with single PTC, who were admitted to our hospital from January 2016 to December 2018, were enrolled in this study. The clinical characteristics and features of the tumor on conventional US and CEUS were retrospectively analyzed. A binary logistic regression model was established, and the diagnostic efficacy of conventional US and CEUS was compared.

RESULTS

The binary logistic regression analysis showed that age younger than 38 years, size of 10.0 mm or greater, solid composition, peak of the nodule interior of 28.3750 or greater, and area under the curve (AUC) of the peripheral ring of less than 3.2500 were independent risk factors of CLNM (P < .05, for all). Prediction model: Logit (p) = -4.135 + 1.040 (age) + 1.920 (size) + 1.769 (composition) + 1.230 (peak of nodule interior) + 0.812 (AUC of peripheral ring). The model positively predicted CLNM at a value of L > -0.199. The AUC, sensitivity, and negative predictive value of the receiver operating characteristic curve for the model were 0.727, 71.7%, and 75.2%, respectively, which were significantly higher than those of conventional US (Z = -2.403; P = .016; Z = -5.330; P < .001; and Z = -2.390; P = .017). The specificity of the model was 73.7%, which was lower than that of conventional US (Z = 3.508; P < .001).

CONCLUSIONS

The preoperative prediction model established via conventional US and CEUS may be helpful to evaluate CLNM in patients with PTC and determine the appropriate treatment options.

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.

Continuing controversy regarding individualized surgical decision-making for patients with 1-4 cm low-risk differentiated thyroid carcinoma: A systematic review

Implementing high-quality randomized controlled trials is difficult for patients with 1-4 cm low-risk differentiated thyroid carcinoma (DTC). Controversy exists regarding whether lobectomy (LT) or total thyroidectomy (TT) is the optimal surgical approach over the short term and long term. Inconsistent recommendations have led to confusion amongst surgeons. Consequently, the outcomes of patients may be influenced. A great deal of new literature is published monthly, and there have been numerous studies supporting both LT and TT. Surgeons must spend considerable time and energy clarifying why controversy exists and which studies should be used as references. We selected 19 recent guidelines/consensuses for surgical approach in treating of 1-4 cm DTC. This study presents various topics relevant to the present debate, including disease-specific survival (DSS), persistence/recurrence, and complications between LT and TT, in patients with 1-4 cm low-risk DTC. This review includes a discussion of the background of those recommendations with regard to various medical, cultural and geographic environments. Additionally, recent technologies and future directions for current issues in risk identification were integrated into the review to provide a reference for individualized decision-making for patients with 1-4 cm low-risk DTC. Given different national conditions, there are different points of emphasis amongst the guidelines. Consideration of surgical approach should consider the character of both surgeons and patients. We should balance the relative benefits, risks and resulting quality of life in order to perform individualized surgical decision-making, and to make reasonable decisions in employing either TT or LT.

Development and validation of an ultrasound-based nomogram for preoperative prediction of cervical central lymph node metastasis in papillary thyroid carcinoma

Background

Preoperative prediction of central lymph node metastasis (CLNM) holds significant value in determining a patient's suitability for surgical resection and the need for adjuvant treatment, thereby contributing to better therapeutic strategies. This study aimed to build and confirm a nomogram that integrates ultrasound (US) characteristics with clinical features to predict CLNM in patients with papillary thyroid carcinoma (PTC) preoperatively.

Methods

The prediction model was set up with a training dataset that included 512 patients with histopathologically confirmed PTC. The least absolute shrinkage and selection operator (LASSO) regression method was applied to select US features in the development cohort. The patients' US characteristics and clinical features were incorporated into a multivariate logistic regression analysis to develop the nomogram. The clinical feasibility, calibration, and discriminatory ability of the nomogram were evaluated in an independent validation cohort of 306 patients.

Results

Age, sex, tumor size, multiple tumors, and US-based CLNM status were included as independent predictors in the personalized nomogram. The nomogram showed good calibration and discrimination in the training and validation datasets. The addition of the BRAF V600E mutation status did not improve the performance of the nomogram. The decision curve analysis showed the nomogram to have clinical feasibility.

Conclusions

A nomogram that integrates US characteristics with patients' clinical features was built. This US-based nomogram can be expediently applied to promote the personalized preoperative prediction of CLNM and to develop surgical strategies, such as tailored central compartment neck dissection, in patients with PTC.

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.

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.

Deep Learning in Thyroid Ultrasonography to Predict Tumor Recurrence in Thyroid Cancers

Purpose

Materials and Methods

Results

Conclusion