Value of dynamic contrast-enhanced ultrasound in predicting cervical lymph node metastasis in papillary thyroid carcinoma patients with Hashimoto’s thyroiditis

Introduction

Hashimoto’s thyroiditis (HT), the predominant etiology of hypothyroidism, is an autoimmune condition characterized by extensive lymphocytic infiltration and persistent inflammatory responses, which may lead to chronic inflammation and thyroid dysfunction. Patients with HT often have a high prevalence of papillary thyroid carcinoma (PTC) with lymph node features like those observed in PTC metastases (1-3). Thus, the presence of HT in PTC cases may exacerbate the preoperative detection of cervical lymph node metastasis (CLNM) (4). Conventional ultrasound techniques exhibit less than 50% sensitivity in identifying metastatic cervical nodes in patients with PTC (5,6), and there are few studies on the impact of HT on ultrasound assessment for CLNM in PTC (3). The choice of surgical approach is directly influenced by the presence or absence of CLNM. Accurately evaluating CLNM before surgery is crucial for establishing the appropriate extent of lymph node dissection (7). Studies have demonstrated that patients with PTC and HT exhibit a reduced occurrence of CLNM compared to those with PTC exclusively (8,9). Nonetheless, PTC patients with HT who undergo surgical intervention typically have a greater number of lymph nodes excised, resulting in an increased incidence of comorbidities such as recurrent laryngeal nerve damage and hypoparathyroidism (10,11). Consequently, enhancing CLNM assessment in patients with PTC and HT is essential for improving patient outcomes.

In recent years, significant advancements have been made in contrast-enhanced ultrasound (CEUS) technology (12). Compared to color Doppler imaging, CEUS exhibits superior sensitivity to blood vessels and has been proven effective in differentiating between malignant and benign thyroid nodules (TNs) (13,14). However, the diagnostic potential of ultrasound imaging is often limited by the restricted ultrasound features discernible to the human eye, which constrains its widespread application as a diagnostic tool in clinical practice. Some researchers have evaluated the biological behavior of PTC with CLNM in association with its ultrasound features.

Dynamic contrast-enhanced ultrasound (DCE-US) is a quantitative imaging method that builds on CEUS images to derive reproducible perfusion parameters, thereby assessing TN microvascular perfusion dynamics (15). This technique facilitates easier interpretation and application, regardless of the operator’s experience level (16,17). DCE-US has been used to determine the perfusion characteristics of thyroid adenomas and carcinomas (18). However, there is limited research on the efficacy of DCE-US in identifying metastatic CLNM from PTC in patients with HT. The integration of clinical information with DCE-US may enhance its diagnostic performance. Consequently, this study aimed to develop and validate a nomogram model that integrates clinical ultrasound data and DCE-US to predict CLNM in this population. We present this article in accordance with the TRIPOD reporting checklist (available at https://gs.amegroups.com/article/view/10.21037/gs-2024-510/rc).

Methods

Our study retrospectively examined patients diagnosed with malignant TNs who were treated at The First Affiliated Hospital of Soochow University between January 2022 and December 2023. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The First Affiliated Hospital of Soochow University granted approval for this study (No. 2025227). Owing to the retrospective nature of the study, the requirement for informed consent was waived.

Participants were selected according to the following criteria: (I) existence of solitary-focus PTC; (II) postoperative histopathological confirmation of PTC accompanied by HT; (III) provision of unambiguous thyroid B-mode ultrasound (BMUS) images; and (IV) comprehensive clinical data, including patient demographics, ultrasound characteristics, thyroid function parameters [including total triiodothyronine (TT3), free triiodothyronine (FT3), total thyroxine (TT4), free thyroxine (FT4), thyroid-stimulating hormone (TSH), thyroid peroxidase antibody (TPO-Ab), thyroglobulin antibody (TG-Ab), and thyrotropin receptor antibody (TR-Ab)]. The exclusion criteria were as follows: (I) multifocal PTC; (II) substandard quality ultrasound imaging; (III) individuals who had received preoperative interventions, such as radiofrequency or microwave ablation, and (IV) patients with inadequate clinical data. Figure 1 illustrates the detailed selection process. Ultimately, a total of 180 patients were included in the study. These patients were randomly assigned to either the training set (n=108) or the validation set (n=72) following a 6:4 ratio.

Figure 1 Flow chart of the study selection process. HT, Hashimoto’s thyroiditis; PTC, papillary thyroid carcinoma; US, ultrasound.

BMUS and CEUS procedure

The examinations were conducted by two radiologists with over 15 years of expertise in CEUS thyroid imaging, utilizing either the Mindray Resona7 or Resona9 ultrasound devices equipped with high-frequency linear array probes. Patients were positioned supine with their arms abducted to ensure adequate exposure of the thyroid. Initial grayscale ultrasonography examinations were performed, with documentation of the largest segments. Following this, 1.2–2.4 mL of sulfur hexafluoride microbubbles (Sono Vue^®; Bracco, Milan, Italy) were administered, followed by a 5 mL normal saline flush. The timer and dynamic storage functions were activated simultaneously with the injection of the contrast agent. The probe was held in a fixed position for 2 minutes to capture the thyroid lesion in its maximal plane. The CEUS footage was archived in DICOM format as a cine loop, allowing for the recording and analysis of the nodules’ strength. For conclusive analysis, a single frame depicting the maximum contrast intensity of the lesion during CEUS was selected.

Nodules were categorized according to the 2020 Chinese Thyroid Image Reporting and Data System classification (19). Two radiologists with over 15 years of experience in thyroid ultrasound image assessment retrospectively evaluated the ultrasound features without knowledge of the patients’ clinical data. In cases of disagreement regarding the conventional ultrasound and CEUS features, a third radiologist reviewed the images to reach a consensus. The BMUS features assessed included lesion size (maximal diameter on BMUS), composition (cystic or almost completely cystic, solid, or almost completely solid), echogenicity (isoechoic, hyperechoic, hypoechoic relative to adjacent thyroid parenchyma, or markedly hypoechoic), shape (wider-than-tall, taller-than-wide), margin (smooth, ill-defined, or irregular), extrathyroidal extension (ETE) (yes, no), and echogenic foci (none, comet-tail artifacts, punctate echogenic foci of uncertain significance, microcalcifications, or macrocalcifications).

For CEUS, each nodule’s enhancement direction (scattered, centripetal, or centrifugal), enhancement pattern (homogeneous or heterogeneous), peak intensity (non-enhancement, hypo-enhancement, iso-enhancement, or hyper-enhancement relative to adjacent thyroid parenchyma at peak), and enhancement defects (absent or present) were recorded.

DCE-US analysis

CEUS data in DICOM format were imported into the VueBox software for offline analysis using the GI-Perfusion tool. A radiologist with a decade of expertise in thyroid CEUS, without knowledge of the patients’ clinical, imaging, and pathological results, analyzed the digitally archived DICOM cine loops. CEUS clips were subjected to dynamic observations. For each nodule, two regions of interest (ROIs) were selected based on the greatest visual enhancement: a boundary ROI that included the nodule and adjacent thyroid tissue, and an analysis ROI that encompassed the area of maximal enhancement while excluding major vasculature or necrotic regions. Time-intensity curves (TICs) were generated and linearized to illustrate the average intensity in the ROIs over time, reflecting the movement of the contrast microbubbles. The following quantitative parameters were derived from the TIC: peak enhancement (PE), rise time (RT), time to peak (TTP), mean transit time local (mTTl), fall time (FT), wash-in area under the curve (WiAUC), wash-in perfusion index (WiPI), wash-in rate (WiR), wash-out area under the curve (WoAUC), combined wash-in and wash-out area under the curve (WiWoAUC), and wash-out rate (WoR). The fit quality between the echo-power signal and f(t) should be ≥75% (20). Respiratory compensation adjustments were implemented on curves that were substantially influenced by respiration to guarantee parameter reliability.

Statistical analysis

Statistical analyses were conducted using R (version 3.6.1), SPSS (version 27.0), and MedCalc (version 20.027). The Mann-Whitney U test or Student’s t-test (for normally distributed variables) was used to compare quantitative parameters. In contrast, the chi-squared test was applied to categorical data and rates. To perform univariate analysis, t-tests, non-parametric tests, and chi-squared tests were utilized. Following this, multivariable logistic regression analysis was applied to create models for predicting CLNM in PTC with HT, using variables identified through the least absolute shrinkage and selection operator (LASSO) regression model. Receiver operating characteristic (ROC) curves were then generated to determine the cutoff value and Youden index. The DeLong test was used to assess the variations in the area under the curve (AUC) among the three models in the training and validation cohorts. A nomogram was created using multivariate logistic regression. Calibration curves and decision curve analyses (DCAs) were used to evaluate the validity and clinical effectiveness of the nomogram, respectively. The threshold for statistical significance was set at a two-sided P value of less than 0.05.

Results

Patient characteristics

This study enrolled 180 patients with a mean age of 41.88 years, including those diagnosed with PTC with and without CLNM. The training set consisted of 32 patients with CLNM and 76 patients without CLNM, whereas the validation set included 22 patients with CLNM and 50 patients without CLNM. The baseline characteristics of the training and validation sets were well-balanced (Table S1).

Model construction

The optimal cutoff points were determined using the Mean Line, PE, Wi AUC, RT, mTTl, TTP, WiR, Wi PI, Wo AUC, Wi Wo AUC, FT, Wo R, and Area values corresponding to the maximum of the Youden index. The cutoff values for these metrics are listed in Table 1. The univariate analysis of the training set indicated that TG-Ab, TR-Ab, Echogenic foci, ETE, lesion size, enhancement defects, Mean Line, Wi AUC, WiR, mTTl, Wi PI, Wo AUC, Wi Wo AUC, Wo R, and Area were substantially correlated with positive LNM (P<0.05), as presented in Table 2. A LASSO regression analysis was performed for further feature selection utilizing 10-fold cross-validation, resulting in the retention of eight features with non-zero coefficients, signifying their significance in predicting CLNM in PTC patients (Figure 2). Subsequently, multivariable logistic regression analysis was employed to develop three predictive models by incorporating the selected features: TR-Ab, TG-Ab, ETE, enhancement defects, mTTl, and WiR (Table 2). These models included the clinical model (Model 1), comprising TR-Ab and TG-Ab; the Clinical and US model (Model 2), consisting of ETE, enhancement defects, TR-Ab, and TG-Ab; and the combined model (Model 3), which integrated the clinical and US models with DCE-US (Figure 3).

Figure 2 Feature selection using the LASSO method in the training set. LASSO, least absolute shrinkage and selection operator.

Figure 3 ROC curves of three models in the training (A) and validation (B) sets. AUC, area under the curve; ROC, receiver operating characteristic.

The predictive performances of the models are presented in Table 3, and the ROC curves are illustrated in Figure 3. In the training set, the combined model demonstrated a AUC of 0.924 (95% CI: 0.857–0.966), outperforming both the clinical model with an AUC of 0.724 (95% CI: 0.630–0.806) and the clinical–ultrasound model with an AUC of 0.854 (95% CI: 0.773–0.915). In the training set, the combined model achieved a sensitivity, specificity, and accuracy of 78.12 %, 96.05%, and 88.89 %, respectively. The validation set showed a sensitivity of 86.36%, specificity of 82.00%, and accuracy of 87.50%. The results align with the data presented in Table 4.

The DeLong test confirmed that the combined model significantly outperformed the clinical and clinical-ultrasound models in both the training and validation cohorts (P<0.05, Table 3). Specifically, in the training cohort, substantial differences in AUC values were observed: 0.924 vs. 0.724 (P<0.001) and 0.924 vs. 0.854 (P<0.001). Similarly, in the validation cohort, statistically significant differences were detected: 0.905 vs. 0.677 (P=0.002) and 0.905 vs. 0.797 (P=0.03). These results suggest that DCE-US parameters (mTTl and WiR) may be significant predictors of lymph node status.

Furthermore, Model 2 exhibited a significantly better discriminatory capacity than Model 1 in both the training (AUC 0.854 vs. 0.724, P=0.008) and the validation cohort (0.797 vs. 0.677, P=0.048, Table 3). This indicates that ultrasound factors may serve as valuable markers for predicting CLNM in patients with PTC and HT. These findings are consistent with the data in Table 3.

Construction and validation of nomogram

A nomogram was constructed from the combined model in the training set to visualize the prediction results and the contribution of each factor (Figure 4). The calibration curves of the nomogram indicated a high concordance between the predicted and actual probabilities of CLNM in both the training and validation cohorts (Figure 5A,5B). The Hosmer-Lemeshow test further confirmed the model’s good fit, with P values of 0.8502 in the training set and 0.7025 in the validation set. Additionally, DCA showed that Model 3 added more net benefits in predicting CLNM than Models 1 and 2, indicating that our developed nomogram based on Model 3 was a valuable method to solve the actual difficulty of predicting CLNM for clinicians in a real-world clinical diagnosis (Figure 5C,5D).

Figure 4 Nomogram for the Model 3 of CLNM. CLNM, cervical lymph node metastasis; ETE, extrathyroidal extension; mTTl, mean transit time local; TG-Ab, thyroglobulin antibody; TR-Ab, thyrotropin receptor antibody; WiR, wash-in rate.

Figure 5 Efficacy of the Model 3 in PTC patients with HT. (A,B) Calibration curves of the Model 3 for predicting CLNM in PTC patients with HT in the training and validation sets. (C,D) Decision curves of three models for predicting CLNM in the training and validation sets. CLNM, cervical lymph node metastasis; HT, Hashimoto’s thyroiditis; PTC, papillary thyroid carcinoma.

Discussion

The association between HT and PTC is complex (21). HT is regarded as both a risk factor for the development of PTC (22,23) and a protective factor for PTC prognosis (24,25). HT-induced inflammation may result in thyroid fibrosis and atrophy, thereby hindering lymphatic dissemination and decreasing the probability of CLNM by compromising adjacent lymphatic arteries. Consequently, PTC patients with concomitant HT may exhibit improved prognosis and disease-free survival (26,27). Paradoxically, although having a more favorable prognosis, these patients frequently undergo more extensive cervical compartment neck dissection and lymphadenectomy than those with PTC alone (28).

Currently, identifying PTC patients with HT at risk for CLNM is challenging due to the lack of dependable diagnostic blood indicators and inadequate thyroid ultrasound imaging. Conversely, predictive variables typically depend on individual assessments, whereas disease prognosis is frequently influenced by factors imperceptible to human observation (29). Unlike other investigations, our predictive model integrated DCE-US, which has garnered considerable interest in the medical field. TICs produced by DCE-US can visibly depict nodule perfusion. Quantitative parameters produced from these curves yield objective data regarding blood perfusion and washout on CEUS, which can be more readily acquired and evaluated by operators, irrespective of their level of expertise. Consequently, the formulation of an integrated model utilizing clinical data and DCE-US is advantageous in clinical practice.

Thyroid peroxidase antibody (TPO-Ab) and thyroglobulin antibody (TG-Ab) are autoantibodies that specifically target thyroid antigens and are essential clinical markers for HT, with prevalence rates of approximately 75% and 90% in patients with HT, respectively (30). Consistent with the study by Januś et al., no significant connections were observed between the two groups for TPO-Ab (P=0.08) in PTC patients with HT (31). Weng et al. investigated the significance of TG-Ab levels in CLNM in patients with differentiated thyroid cancer, indicating that preoperative TG-Ab levels serve as a protective predictor of CLNM (32), corroborating our findings (P=0.01). Our analysis also identified TR-Ab, a form of thyroid-stimulating hormone receptor capable of converting to stimulating and blocking antibodies, as an independent predictor. This antibody was detected in PTC cells of patients with HT and played a crucial role in the occurrence, progression, and immune evasion of PTC. Our findings revealed a negative correlation between TRAb and CLNM (P=0.009), which is consistent with the results reported by Weng et al. (32).

Additionally, our study sheds light on the relationship between ultrasound features and CLNM. We evaluated both BMUS and CEUS, with the latter providing information on lesion perfusion (33). CEUS, an innovative imaging technique, has recently garnered considerable attention for its application in thyroid lesions. This non-invasive method allows real-time visualization of blood flow in lesions, enhancing our ability to accurately characterize tumor vascular features and effectively differentiate between benign and malignant growths (34). A recent study has demonstrated the efficacy of this method in identifying metastatic lymph nodes. This research underscored the significance of ETE and enhancement defects as critical indicators of CLNM (35). Our findings revealed that CLNM was more common in patients with ETE and enhancement defects. Lesions displaying ETE and enhancement defects likely indicate a more aggressive tumor behavior, collectively increasing the metastatic potential and possibly facilitating lymphatic spread (36). Moreover, a previous study indicated that quantitative measures are beneficial for distinguishing between benign and malignant TNs. The study identified the perfusion time parameter mTTl in individuals with CLNM as significant, indicating a reduced overall perfusion time within the lesion, which may be attributable to a decreased blood supply (37). Our investigation demonstrated that accelerated mTTl in initial lesions constituted a risk factor for CLNM in patients, corroborating the results of Kong et al., who reported that mTTl may occur throughout the entire process in patients with CLNM (38). This finding reveals a heterogeneous spatial pattern of tumor blood vessels, a characteristic that plays a pivotal role in driving tumor progression (39). The study indicated that elevated WiR readings signify an increased risk of CLNM in PTC patients with HT. These results align with those of Huang et al. (15), who indicated that quantitative CEUS characteristics offer valuable supplementary information beyond subjective human visual and qualitative diagnostic criteria.

To evaluate the predictive value of different data modalities, we developed three distinct models: one based solely on clinical data, a second incorporating both clinical and ultrasound data, and a third integrating clinical, ultrasound, and DCE-US data. The clinical-only and clinical-ultrasound models demonstrated moderate performance, achieving AUCs of 0.738 and 0.863 in the training cohort and 0.730 and 0.817 in the validation cohort, respectively. In contrast, the comprehensive model, which incorporated DCE-US information, exhibited superior performance, with AUCs of 0.925 and 0.915 in the training and validation sets, respectively. This performance marginally surpassed that of the clinical ultrasound and clinical-only models. DCA further corroborated these findings, demonstrating that the integrated model significantly improved the prediction of lymph node metastasis compared to the other two models. These results suggest that the utilization of VueBox^® perfusion analysis to extract ultrasound parameters from DCE-US images offers a tangible benefit in predicting CLNM in patients with PTC and HT, potentially informing novel treatment strategies.

There are several limitations in this study. First, as a retrospective study with a limited sample size, selection bias was inevitable. Extensive prospective research using standardized methodologies is essential to corroborate these findings and provide more robust data. The absence of standardization of DCE-US across various manufacturers constrains the generalizability of our findings. This study exclusively examined unifocal PTC; however, multifocal PTCs are recognized risk factors for CLNM and require further investigation. Finally, our study focused solely on TNs and lacked a comparative analysis of the ultrasound characteristics of cervical lymph nodes with and without metastasis, indicating the need for further investigation in this domain.

Conclusions

In conclusion, the present study indicates that the VueBox^® is an effective instrument for assessing the dynamic microvascularization of TNs. DCE-US employing VueBox^® perfusion analysis can enhance the prediction of CLNM in PTC patients with HT. The integration of clinical information with DCE-US can yield superior diagnostic efficacy for detecting CLNM in these individuals.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://gs.amegroups.com/article/view/10.21037/gs-2024-510/rc

Data Sharing Statement: Available at https://gs.amegroups.com/article/view/10.21037/gs-2024-510/dss

Peer Review File: Available at https://gs.amegroups.com/article/view/10.21037/gs-2024-510/prf

Funding: This work was partially supported by the Suzhou Clinical Key Diseases Diagnosis and Treatment Technology Special Project (No. LCZX202104), The First Affiliated Hospital of Soochow University Natural Science Foundation Bo Xi Cultivation Program (No. BXQN202235), and The First Affiliated Hospital of Soochow University Bo Xi Clinical Research Project (No. BXLC002).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://gs.amegroups.com/article/view/10.21037/gs-2024-510/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The First Affiliated Hospital of Soochow University granted approval for this study (No. 2025227). Owing to the retrospective nature of the study, the requirement for informed consent was waived.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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