Sage Journals: Discover world-class research

Abstract

Background

The objective of this study is to develop a predictive model for the assessment of cervical lymph node metastasis risk in papillary thyroid carcinoma (PTC).

Methods

A retrospective study was conducted on 212 patients with PTC who underwent initial surgical treatment from August 2022 to April 2023 in 2 hospitals. Data were randomly split into 7:3 training-validation sets. Logistic regression was used for feature selection and predictive model creation. Model performance was assessed using receiver operating characteristic (ROC) and calibration curves. Clinical utility was determined using decision curves.

Results

Among the 212 patients with PTC, 104 cases (49.1%) exhibited cervical lymph node metastasis, while 108 cases (50.9%) did not. Multivariate logistic regression analysis revealed that age (OR = 0.95), FT3 (OR = 0.41), tumor maximum diameter ≥0.9 cm (OR = 1.85), intratumoral microcalcifications (OR = 1.84), and suspicious lymph node on ultrasound (OR = 2.96) were independent risk factors for lymph node metastasis in PTC patients (P < 0.05). The constructed model for predicting the risk of cervical lymph node metastasis demonstrated a training set ROC curve area under the curve (AUC) of 0.742 (95% CI: 0.664 - 0.821), with a cut-off value of 0.615, specificity of 87.8%, and sensitivity of 51.4%. The validation set exhibited an AUC of 0.648 (95% CI: 0.501 - 0.788), with a cut-off value of 0.644, specificity of 91.2%, and sensitivity of 43.3%. Including the BRAF V600 E mutation did not improve the model’s diagnostic performance significantly. Decision curve analysis indicated clinical feasibility of the model.

Conclusion

The predictive model developed in this study effectively predicts lymph node metastasis risk in PTC patients by incorporating ultrasound features, demographic characteristics, and serum parameters. However, including the BRAF V600 E mutation does not significantly improve the model’s diagnostic performance.

Plain language summary

Background

The objective of this study is to develop a predictive model for the assessment of cervical lymph node metastasis risk in papillary thyroid carcinoma.

Methods

A retrospective study was conducted on 212 patients with papillary thyroid carcinoma who underwent initial surgical treatment at the First Affiliated Hospital of Fujian Medical University from August 2022 to April 2023. Data randomly split into 7:3 training-validation sets. Logistic regression used for feature selection and predictive model creation. Model performance assessed using ROC and calibration curves. Clinical utility determined using decision curves.

Results

Among the 212 patients with PTC, 104 cases (49.1%) exhibited cervical lymph node metastasis, while 108 cases (50.9%) did not. Multivariate logistic regression analysis revealed that age (OR = 0.95), FT3 (OR = 0.41), tumor maximum diameter â‰ ¥ 0.9cm(OR = 1.85), intratumoral microcalcifications (OR = 1.84), and suspicious lymph node on ultrasound (OR = 2.96) were independent risk factors for LNM in PTC patients (P < 0.05). The constructed model for predicting the risk of cervical lymph node metastasis demonstrated a training set ROC curve area under the curve (AUC) of 0.742 (95% CI: 0.664 - 0.821), with a cut-off value of 0.615, specificity of 87.8%, and sensitivity of 51.4%. The validation set exhibited an AUC of 0.648 (95% CI: 0.501 - 0.788), with a cut-off value of 0.644, specificity of 91.2%, and sensitivity of 43.3%. Including BRAF V600E gene did not improve model's diagnostic performance significantly. Decision curve analysis indicated clinical feasibility of the model.

Conclusion

The predictive model developed in this study effectively predicts lymph node metastasis risk in PTC patients by incorporating ultrasound features, demographic characteristics, and serum parameters.However, Including the BRAF V600E mutation does not significantly improve diagnosis performance.

Keywords

papillary thyroid carcinoma lymph node metastasis nomogram BRAF V600E mutation

Introduction

Thyroid cancer has become 1 of the top ten common cancers worldwide.¹ In 2020, the global incidence rate was 10.1 per 100,000.² The most common pathological type is Papillary thyroid carcinoma (PTC). Although PTC progresses relatively slowly, with a 10 year survival rate of over 90%,³ it has a high propensity to metastasize to surrounding lymph nodes, with a metastasis rate ranging from 50% to 90%.^4,5 Moreover, within a certain range, as the number of lymph node metastases increases, the risk of recurrence for patients also rises.⁶ Such patients require aggressive treatment modalities, such as unilateral or total thyroidectomy and therapeutic neck lymph node dissection. In cases where clinical indications are met, patients with cN1 (clinical lymph node positive) routinely undergo therapeutic neck lymph node dissection.⁷ For clinically lymph node-negative (cN0) patients, a relatively conservative approach may be taken, including thyroid lobectomy, thyroid cancer ablation therapy, or follow-up observation. However, studies have found that approximately 20% to 80% of cN0 patients may have occult lymph node metastases identified in postoperative pathology.⁸ Failure to perform prophylactic lymph node dissection in such patients may increase the risk of secondary surgery. Nevertheless, if prophylactic central compartment neck lymph node dissection is performed for all cN0 patients, it may elevate the occurrence of postoperative complications (such as recurrent laryngeal nerve injury and permanent hypoparathyroidism) without significant improvement in patient prognosis.⁹ Therefore, accurately determining the cervical lymph node status of PTC patients is crucial for making rational and individualized surgical decisions.

Currently, imaging examinations such as ultrasound and computed tomography scans, as well as cytopathological examinations like fine-needle aspiration (FNA) or FNA with thyroglobulin measurement (FNA-Tg), are the main methods for preoperatively assessing cervical lymph nodes in PTC patients.¹⁰ However, routine ultrasound examination has limited sensitivity in detecting cervical lymph nodes, and a single preoperative examination may not provide sufficient evidence for surgical decision-making.¹¹ Thus, preoperative assessment of lymph node metastasis (LNM) in PTC patients remains a challenging clinical issue, and there is a need for further exploration of more effective predictive methods.

Research on the relationship between changes in thyroid function and the risk of PTC suggests a correlation between the 2.¹² Thyroid-stimulating hormone (TSH) plays a crucial role in regulating thyroid function, and the Gαs-adenylyl cyclase-protein kinase A-cyclic adenosine monophosphate (cAMP) pathway is the classical pathway of TSH action.¹³ Constitutive activation of the cAMP pathway may be associated with increased carcinogenic potential and decreased TSH levels. Lower TSH levels may lead to less differentiation of thyroid epithelial cells, increasing the tendency for malignant cell transformation.¹⁴ In addition, thyroid hormones may have an independent impact on the occurrence of thyroid cancer, and lower levels of FT3 and FT4 have been associated with thyroid cancer diagnosis.¹⁵ Some researchers have proposed TSH¹⁶ and serum thyroid hormones¹⁷ as new indicators for predicting PTC, but there are fewer studies using them to predict LNM in PTC patients. Their predictive value for LNM in PTC patients needs further investigation to be validated.¹⁸ Therefore, in this retrospective study, we combine demographic characteristics, serum parameters, and conventional ultrasound features to construct a predictive model, aiming to improve the accuracy of preoperative LNM diagnosis for patients and provide a basis for selecting personalized treatment modalities.

Methods

Patients

A retrospective collection of patients diagnosed with thyroid cancer was conducted from August 2022 to April 2023 from the First Affiliated Hospital of Fujian Medical University and National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University . All patients underwent either total or near-total thyroidectomy, unilateral lobectomy with isthmusectomy, and at least ipsilateral central neck lymph node dissection. Inclusion criteria were as follows: ① Initial surgery with postoperative pathological confirmation of PTC (excluding other pathological types of thyroid cancer, such as follicular carcinoma, medullary carcinoma, undifferentiated carcinoma, etc.); ② Complete availability of ultrasound images, laboratory data, and clinical pathological information. Exclusion criteria included: ① Concurrent presence of other malignant tumors; ② History of multiple surgeries in the thyroid region; ③ No neck lymph node dissection performed; ④ Prior use of thyroid hormone medication. This retrospective study utilized existing medical records and has been reviewed and approved by the Ethics Committee of the First Affiliated Hospital, Fujian Medical University (approval number: Minimally Invasive Ethical Review [2015]084-2). The requirement for informed consent was waived due to the retrospective nature of the study. This study complies with the TRIPOD guidelines.¹⁹

Data Collection

(1) Clinical basic information: Gender, age, Body Mass Index (BMI: weight/height²).

(2) Conventional ultrasound features of the primary lesion¹⁰: Multiplicity of nodules (selecting the largest nodule observed on ultrasound); Nodule size (maximum diameter of the largest nodule on ultrasound); Nodule location (left lobe, isthmus, right lobe, multiple locations); Nodule composition (solid or other), hypoechoic/very hypoechoic nodules (hypoechoic nodules defined by contrast with thyroid parenchyma, very hypoechoic nodules defined by contrast with adjacent neck muscles, indicating decreased echogenicity), longitudinal-to-transverse ratio greater than 1 (height greater than width), shape (irregular defined as serrated or lobulated), margin (indistinct defined as difficulty distinguishing the nodule edge from thyroid tissue), intranodular microcalcifications (point-like strong echoes with diameter ≤1 mm) and perinodular halo; Blood flow distribution within the nodule; Hashimoto’s thyroiditis.

(3) Any of the following features detected during routine ultrasound examination are suspected to be cervical lymph node metastasis²⁰: Rounded morphology (long diameter: short diameter <2:1), microcalcifications, cystic changes, hypoechoic areas, disappearance of the hilum structure, abundant or irregular blood flow within the lymph node (peripheral vascular formation).

(4) BRAF V600 E gene (determined by pathological department cell pathology gene testing).

(5) FT3, FT4, sTSH, PTH.

(6) Blood immune indicators: Neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), preoperative systemic immune-inflammation index (SII), platelet-to-lymphocyte ratio (PLR).

Statistical Analysis

The PTC patients were divided into a 70% training set and a 30% validation set to ensure that the outcome events were randomly distributed between the 2 sets. The training set was used for variable selection and model construction, while the validation set was used to verify the results obtained from the training set. Descriptive statistics were used to analyze baseline data. Normally distributed data were expressed as mean ± standard deviation, while non-normally distributed data were presented as median (interquartile range). The consistency between the 2 randomly grouped datasets was validated.

For univariate analysis, categorical variables were analyzed using chi-square tests, and continuous variables were assessed using t-tests or rank-sum tests. To achieve the analysis objectives, tumor size (continuous variable) was converted to a categorical variable using the optimal cut-off value. Univariate and multivariate logistic regression analyses were performed for all variables, with variables demonstrating P < 0.05 considered as independent risk factors.

A predictive model for predicting LNM in PTC patients was constructed using stepwise regression based on the minimum Akaike information criterion value, and a nomogram was created. The predictive performance of the model was assessed using receiver operating characteristic (ROC) curves by calculating the area under the curve (AUC). Calibration curves were plotted, and the Hosmer-Lemeshow goodness-of-fit test was conducted to evaluate the difference between predicted and actual outcomes, confirming the accuracy of the predictions. Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were used to assess the improvement in risk prediction and measure the utility of the new model. Decision curve analysis (DCA) quantified the net benefits of different threshold probabilities, reflecting the feasibility of using the predictive model in clinical practice. Statistical analysis was performed using RStudio 4.2.2 software, with a significance level set at P < 0.05.

Results

Basic Information

A total of 212 PTC patients were included and randomly divided into a training set (n = 148) and a validation set (n = 64) using a 70:30 split (Figure 1). Based on postoperative histopathological examination results, the positive rates of LNM in the training and validation sets were 50.0% and 46.8%, respectively, and the difference in LNM positive rates between the 2 sets was not statistically significant (P = 0.677). There were no statistically significant differences in gender (P = 0.960) or age (P = 0.278) between the 2 datasets, confirming the reliability of the data used (Table 1). For ease of logistic regression analysis, tumor size (continuous variable) was categorized. ROC curve analysis was performed, and a cutoff value of <0.9 cm (119 cases) and ≥0.9 cm (92 cases) was determined. The odds ratio (OR) was 2.333 (95% CI: 1.345-4.094), categorizing tumor size as <0.9 cm or ≥0.9 cm.

Figure 1.

Flowchart of patient selection in this study.

Table 1.

Clinicopathological and Ultrasonic Characteristics of Patients in the Training and Validation Cohorts.

Characteristic	Entire Population	Training Cohort	Validation Cohort	P-value
Characteristic	(N = 212)	(N = 148)	(N = 64)	P-value
Gender				0.96
Male	74 (34.9)	51 (34.5%)	23 (35.9%)
Female	138 (65.1)	97 (65.5%)	41 (64.1%)
Age	46.0 [37.0;54.0]	46.0 [38.8;54.0]	43.0 [33.8;54.0]	0.278
BMI	24.2 [21.7;26.5]	24.4 [22.2;26.5]	23.8 [20.8;26.3]	0.213
NLR	1.80 [1.41;2.38]	1.79 [1.37;2.40]	1.86 [1.48;2.36]	0.573
LMR	6.40 [5.10;7.84]	6.38 [5.08;7.88]	6.48 [5.23;7.54]	0.777
PLR	134 [107;169]	133 [103;172]	134 [116;160]	0.535
SII	461 [342;649]	454 [336;659]	468 [364;643]	0.762
FT3	5.27 [4.88;5.71]	5.29 [4.89;5.71]	5.22 [4.81;5.69]	0.822
FT4	15.8 [14.3;17.2]	15.5 [14.2;17.2]	16.1 [14.6;17.1]	0.275
sTSH	1.60 [1.11;2.25]	1.58 [1.14;2.29]	1.67 [0.94;2.21]	0.725
PTH	3.62 [2.84;4.51]	3.63 [2.94;4.48]	3.55 [2.79;4.95]	0.957
Hashimoto’s thyroiditis				0.006
Yes	49 (23.1%)	26 (17.6%)	23 (35.9%)
No	163 (76.9%)	122 (82.4%)	41 (64.1%)
Location				0.871
Left lobe	71 (33.5%)	52 (35.1%)	19 (29.7%)
Isthmus	20 (9.43%)	13 (8.78%)	7 (10.9%)
Right lobe	93 (43.9%)	64 (43.2%)	29 (45.3%)
Bilateral	28 (13.2%)	19 (12.8%)	9 (14.1%)
Multifocality				0.783
Yes	54 (25.5%)	39 (26.4%)	15 (23.4%)
No	158 (74.5%)	109 (73.6%)	49 (76.6%)
Tumor size				0.197
0.9 cm	119 (56.4%)	79 (53.4%)	41 (64.1%)
≥0.9 cm	92 (43.6%)	69 (46.6%)	23 (35.9%)
Taller-than-wide shape				0.699
Yes	102 (48.1%)	73 (49.3%)	29 (45.3%)
No	110 (51.9%)	75 (50.7%)	35 (54.7%)
Capsule contact				0.396
Yes	117 (55.2%)	85 (57.4%)	32 (50.0%)
No	95 (44.8%)	63 (42.6%)	32 (50.0%)
Composition				0.232
Solid	183 (86.3%)	131 (88.5%)	52 (81.2%)
Other	29 (13.7%)	17 (11.5%)	12 (18.8%)
Very hypoechoic/hypoechoic				0.494
Yes	202 (95.3%)	142 (95.9%)	60 (93.8%)
No	10 (4.72%)	6 (4.05%)	4 (6.25%)
Boundary				0.281
Clarity	126 (59.4%)	92 (62.2%)	34 (53.1%)
Obscure	86 (40.6%)	56 (37.8%)	30 (46.9%)
Margin				1
Well-defined	36 (17.0%)	25 (16.9%)	11 (17.2%)
Ill-defined	176 (83.0%)	123 (83.1%)	53 (82.8%)
Shape				0.975
Regular	45 (21.2%)	32 (21.6%)	13 (20.3%)
Irregular	167 (78.8%)	116 (78.4%)	51 (79.7%)
Halo around				0.831
Yes	23 (10.8%)	17 (11.5%)	6 (9.38%)
No	189 (89.2%)	131 (88.5%)	58 (90.6%)
Punctate echogenic foci				0.12
Yes	131 (61.8%)	97 (65.5%)	34 (53.1%)
No	81 (38.2%)	51 (34.5%)	30 (46.9%)
Internal blood flow signals				0.224
Yes	131 (61.8%)	87 (58.8%)	44 (68.8%)
No	81 (38.2%)	61 (41.2%)	20 (31.2%)
US- based LNM status				0.916
Yes	39 (18.4%)	28 (18.9%)	11 (17.2%)
No	173 (81.6%)	120 (81.1%)	53 (82.8%)
BRAF mutation				0.789
Yes	104 (49.1%)	74 (50.0%)	30 (46.9%)
No	108 (50.9%)	74 (50.0%)	34 (53.1%)

P value is derived from the univariable association analyses between each of the variables and CLNM status. Fisher’s exact test. SD, standard deviation; US, ultrasound; LNM, lymph node metastasis. Qualitative date were expressed as mean ± standard deviation or number percentages (%);quantitative date were expressed as median (25%-75% quantiles).

Variable Selection and Model Construction

Univariate logistic analysis was conducted on the data of 148 PTC patients in the training set. The comparison of various indicators showed that age, FT3, tumor maximum diameter, intratumoral microcalcifications, Hashimoto’s thyroiditis, and suspicious lymph nodes on ultrasound were influencing factors for the occurrence of LNM in PTC patients. All differences were statistically significant (P < 0.05). (Table 2). According to the stepwise regression results, the model including age, FT3, tumor maximum diameter, intratumoral microcalcifications, and suspicious lymph nodes on ultrasound had the lowest Akaike Information Criterion (AIC) value in the training set and was presented in the form of a nomogram (Figure 2).

Table 2.

Univariate and Multivariate Analyses of the Preoperative Predictors of LNM of PTCs in the Training Cohort.

Variable	Univariate analysis	P-value	Multivariate analysis	P-value
Variable	OR (95% CI)	P-value	OR (95% CI)	P-value
Age	0.96 (0.93-0.99)	0.011	0.95 (0.91-0.98)	0.002
FT3	0.57 (0.34-0.95)	0.032	0.41 (0.23-0.74)	0.003
Hashimoto’s thyroiditis
Yes	2.65 (1.07-6.56)	0.035
No	Reference	NA
Tumor size
<0.9 cm	Reference	NA
≥0.9 cm	2.04 (1.06-3.94)	0.033	1.85 (0.88-3.88)	0.104
Punctate echogenic foci
Yes	2.21 (1.1-4.42)	0.026	1.84 (0.85-4.02)	0.124
No	Reference	NA
US- based LNM status
Yes	3.06 (1.25-7.48)	0.014	2.96 (1.08-8.1)	0.035
No	Reference	NA

PTC, papillary thyroid carcinoma; LNM, lymph node metastasis; CI, confidence interval; OR, odds ratio.

Figure 2.

Performance of the nomogram. (a) ROC curves of US-reported LNM status and BRAF V600 E the combined nomogram for predicting lateral LNM in the training cohort; (b) in the validation cohort. DeLong’s test showed that the AUCs were not significantly different between the model 1 and the model 2 in both cohorts (P > 0.05).

Model Construction and Validation

Table 3 Based on the selected variables, a prediction model (Model 1) was constructed using ultrasound features. In the training set, Model 1 had an AUC value of 0.742 (95% CI: 0.664-0.821) with a cutoff value of 0.615 (sensitivity 0.878, specificity 0.514). In the validation set, the AUC value was 0.648 (95% CI: 0.509-0.788) with a cutoff value of 0.614 (sensitivity 0.912, specificity 0.433), confirming the reliability of the LNM prediction model (Table 4). Compared to single conventional ultrasound diagnosis of lymph node metastasis, the sensitivity (51% vs 27%) and positive predictive value (81% vs 71%) of the nomogram diagnosis were both improved. Calibration curves were used to verify the accuracy and repeatability of the nomogram, and the Hosmer-Lemeshow test showed no statistically significant differences between the predicted and actual values in both the training and validation sets (P > 0.05) (Figure 3). The DCA curve demonstrated that the nomogram had good predictive ability when the risk threshold was between 0.1 and 0.6, providing benefit to patients and proving to be more effective than treating all patients or no treatment at all (Figure 4).

Table 3.

Risk Factors for LNM in PTC.

Variable	US-Reported LNM status			US with BEAF V600 E
Variable	β	OR (95%CI)	P	β	OR (95%CI)	P
Intercept	6.303		0.001	6.661		0.001
Age	−0.055	0.95 (0.91-0.98)	0.002	−0.062	0.94 (0.91-0.98)	0.001
FT3	−0.889	0.41 (0.23-0.74)	0.003	−1.141	0.32 (0.17-0.61)	0.001
Tumor size	0.614	1.85 (0.88-3.88)	0.104	0.856	2.35 (1.07-5.17)	0.033
Punctate echogenic foci	0.612	1.84 (0.85-4.02)	0.124	0.425	1.57 (0.7-3.51)	0.270
US- based LNM status	1.084	2.96 (1.08-8.1)	0.035	1.805	2.96 (1.05-8.31)	0.040
BRAF mutation	NA	NA	NA	1.545	4.69 (1.48-14.81)	0.008

US-reported LNM status:model 1; US with BEAF V600 E:model 2.

Table 4.

Two Model Performances.

	Cut-Off	Sensitivity	Specificity	Accuracy	PPV	NPV	AUC (95%CI)
US-reported LNM status	0.615
Training cohort		0.514	0.878	0.696	0.809	0.644	0.742 (0.664-0.821)
Validation cohort		0.433	0.824	0.628	0.684	0.622	0.648 (0.509-0.788)
US with BEAF V600 E	0.460
Training cohort		0.743	0.676	0.709	0.696	0.725	0.764 (0.688-0.840)
Validation cohort		0.700	0.471	0.585	0.538	0.640	0.658 (0.519-0.796)

PPV, positive predictive value; NPV, negative predictive value.

Figure 3.

Calibration curve of the combined nomogram. (a) Calibration curve of the nomogram in the training cohort, the Hosmer-Lemeshow test yield a nonsignificant statistic (P = 0.773); (b) calibration curve of the nomogram in the validation cohort, the Hosmer-Lemeshow test yield a onsignificant statistic (P = 0.070). The calibration curve illustrates the calibration of the nomogram in terms of the agreement between the predicted risk of LNM and the observed outcomes of LNM.

Figure 4.

Decision curve analysis of the clinical multimodal nomogram. The blue and gray lines are the decision curves of our nomogram and all PTC patients with LNM who received treatment, respectively. The black line is the decision curve of all PTC patients without LNM who did not receive treatment. The decision curves show that the nomogram to predict LNM in patients with PTC provides a greater benefit than does the all or no treatment.

Comparison with the Ultrasound Features Combined with BRAF V600E Mutation Model (Model 2)

Comparison of changes in AUC, NRI, and IDI were used to assess the predictive performance of Model 1 and Model 2. ROC curve analysis in Model 2 showed an AUC of 0.764 (95% CI: 0.688-0.840) in the training set and an AUC of 0.658 (95% CI: 0.519-0.796) in the validation set (Table 4). DeLong's test indicated that there was no statistically significant difference in AUC values between the 2 models in both the training and validation sets (P > 0.05), with a P-value of 0.238 in the training set and 0.695 in the validation set (Figure 2). In the training set, NRI >zero (P > 0.05) and IDI was 0.037 (0.007-0.068) (P < 0.05); in the validation set, NRI >zero (P > 0.05) and IDI was 0.019 (−0.033-0.070) (P > 0.05), indicating that Model 2, while showing some improvement compared to Model 1, did not have statistically significant improvement in the validation set (P > 0.05) (Table 5).

Table 5.

NRI and IDI.

	NRI (95%CI)	P	IDI (95%CI)	P
Training cohort	0.027 (−0.037-0.091)	0.409	0.037 (0.007-0.068)	0.017
Validation cohort	0.084 (-0.042-0.210)	0.189	0.019 (-0.033-0.070)	0.482

NRI, net reclassification improvement, IDI, integrated discrimination improvement . P value is derived from the comparative analysis of the training and validation sets in Model 1 and Model 2.

Discussion

Currently, the surgical consensus for PTC follows the principle of “minimal effective treatment”.²¹ Routine therapeutic lymph node dissection is performed for cN + patients. However, for cN0 patients, individualized treatment is considered, and there is still no consensus among countries on whether to undergo prophylactic central compartment lymph node dissection (pCND).⁷ The controversy revolves around the limited improvement in prognosis for pCND patients and the higher risk of postoperative complications. Accurately assessing the status of neck lymph nodes in PTC patients before surgery is crucial. Therefore, this study aimed to integrate demographic characteristics, serum parameters, and conventional ultrasound features to construct a predictive model for preoperative assessment of LNM in PTC patients.

In this study, a total of 15 ultrasound features and 11 basic clinical data and laboratory indicators were included as potential predictors of LNM in PTC. Utilizing a stepwise regression based on the minimum value of Akaike’s information criterion, a predictive model was constructed. This model incorporated 5 variables: patient age, FT3 levels, maximum tumor diameter, presence of microcalcifications within the tumor, and suspicion of lymph node metastasis based on ultrasound characteristics. The model, based on ultrasound features, demonstrated excellent discrimination and calibration upon validation. The personalized treatment approach for patients holds clinical significance. For young PTC patients with low FT3 levels, tumor diameter ≥0.9 cm, presence of microcalcifications within the tumor, and suspected lymph node metastasis on ultrasound, a proactive treatment strategy is recommended. Similarly, for elderly cN0 patients with higher FT3 levels and smaller primary tumors, conservative follow-up observation or ablative therapy for the primary lesion may be considered.

Current research suggests that the most common genetic mutation in PTC is the BRAF V600 E gene mutation.²² However, its association with LNM remains inconclusive. Some studies^23,24 have reported a close correlation between the BRAF V600 E mutation and the prognosis, recurrence, and lymph node metastasis (LNM) of PTC patients. On the contrary, other studies^25,26 have suggested that the BRAF V600 E mutation may not be predictive of lymph node metastasis. The results of our univariate logistic regression analysis indicate that the BRAF V600 E mutation showed no statistically significant association with LNM (P = 0.085). It is possible that the influence of this important predictive factor may have been outweighed by subtle variations due to confounding factors. Therefore, we included the BRAF V600 E mutation in model 2; however, model 2 did not significantly improve the predictive performance of the nomogram.

Some PTC patients appear to have a relative state of thyroid dysfunction.¹² However, the relationship between serum thyroid hormones and TSH requires further validation. Some researchers¹⁵ have suggested that by excluding patients with thyroid function abnormalities, potential influences on PTC can be eliminated. They argue that both FT3 and FT4 have a direct impact on PTC. Additionally, certain studies have indicated that a decrease in FT3 levels may be a sign of dedifferentiation or malignant transformation of thyroid follicular cells,²⁷ suggesting that serum thyroid hormones may be helpful in predicting the malignant tumor risk in patients. The results of our multifactorial logistic regression analysis in this study demonstrate that FT3 is an independent risk factor for LNM. The nomogram illustrates that a decrease in FT3 levels is associated with an elevated risk of LNM. However, some studies have not found an association between serum thyroid hormones and LNM.²⁸ Further research is needed to explore the relationship between serum thyroid hormones and LNM.

One of the reference indicators for formulating individualized treatment and lymph node dissection plans for PTC patients is the maximum diameter of the tumor lesion.²⁹ Some researchers³⁰ believe that larger tumors are more likely to invade the capsule and spread to the surrounding area, potentially increasing the incidence of LNM. However, there is no unified standard for the diagnostic threshold of tumor size in existing research.³¹ Some researchers have defined the threshold as 1 centimeter,³² considering it may be a clinically significant cancer. However, if only nodules larger than 1 centimeter are evaluated, cancers within this critical range may go undetected. The results of this study indicate that a maximum nodule diameter of ≥0.9 cm holds significance in predicting LNM in PTC patients (OR: 2.35, 95% CI: 1.07-5.17, P = 0.033). This provides different options for patients with suspicious risks within the critical range, such as close observation or fine-needle aspiration (FNA).

Currently, suspected or confirmed thyroid cancer patients are required to undergo a comprehensive neck ultrasound examination, including both the thyroid gland and lymph nodes.³³ While ultrasound is a convenient, non-invasive, and rapid method for diagnosing lymph node metastasis, a meta-analysis based on retrospective studies suggests that routine ultrasound has limited capability in detecting LNM.³⁴ Any single ultrasound feature is not sensitive enough for detecting metastatic lymph nodes in PTC patients.³⁵ In this study, compared to a single conventional ultrasound examination, the Nomogram demonstrated an improvement in the accuracy, sensitivity, and specificity in diagnosing cervical lymph node metastasis. Additionally, combining suspicious features with other methods may be an effective approach.³⁶ The results of this study indicate that ultrasound suspicion of lymph node involvement is an independent risk factor for LNM (P < 0.05) and holds significance in predicting LNM in PTC patients (OR: 2.96, 95% CI: 1.05-8.31, P = 0.040).

This study reveals an increased risk of lymph node metastasis (LNM) in younger patients with PTC, aligning with previous research findings.³⁷ Some researchers have proposed that in PTC patients under 45 years of age, the occurrence of LNM is associated with a decreased overall survival rate, advocating for a rigorous assessment of lymph node involvement.⁶ Conversely, another relevant prognostic study has indicated that LNM in young patients under 45 years of age has a relatively minor impact on prognosis.³⁸ However, this study lacks patient survival data and did not conduct a survival prognosis analysis, which should be addressed in future experiments. Furthermore, the occurrence and development of PTC are closely related to the immune microenvironment.³⁹ In the cellular ecosystem of PTC, immune-related factors significantly influence the tumor microenvironment.⁴⁰ Research indicates that PTC associated with neutrophil infiltration and (or) elevated platelet counts tends to exhibit increased aggressiveness.^41,42 Neutrophils can suppress apoptosis and induce tumor angiogenesis by releasing inflammatory cytokines and phagocytic mediators, thereby altering the tumor microenvironment.⁴³ Additionally, platelet-derived growth factor receptor alpha (PDGFRα) induces cytoskeletal reorganization, which drives an invasive cell phenotype, promotes the formation of functional invasive pseudopodia in PTC cells, and facilitates lymph node metastasis.^44,45 Preoperative blood immune indicators may serve as predictive markers for PTC. Some researchers have identified high NRL⁴⁶ and high SII⁴⁷ as adverse prognostic indicators for PTC patients. This study incorporated blood immune indicators such as LMR, PLR, NLR, and SII, but the results indicate that these indicators lack statistical significance in relation to LNM (P > 0.05). Future research with larger sample sizes is still needed to validate these findings.

Limitations

This study has several limitations. The sample size is relatively small, and the data for analysis all originate from a single institution. Additionally, retrospective analysis may introduce certain sample biases. Therefore, further research on the prediction of cervical lymph node metastasis in PTC would benefit from multi-center studies or larger clinical datasets.

Conclusions

We conducted a comprehensive analysis and identified age, FT3 levels, maximum tumor diameter, intratumoral microcalcifications, and ultrasound suspicion of lymph node metastasis as independent predictive factors for cervical lymph node metastasis (LNM) in patients with PTC. By integrating demographic characteristics, serological indicators, and conventional ultrasound features, we can guide personalized treatment for PTC patients, thereby avoiding unnecessary prophylactic lymph node dissection.

Supplemental Material

Supplemental Material - Prediction Model of Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma

Supplemental Material for Prediction Model of Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma by Huiting Chen, Li Zhu, Yong Zhuang, Xiaojian Ye, Fang Chen, and Jinshu Zeng in Cancer Control

Supplemental Material

Supplemental Material - Prediction Model of Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma

Footnotes

Authors’ Contributions

All authors have contributed significantly to the work described in the manuscript. All authors read and approved the final manuscript.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Natural Science Foundation of Fujian Province under grant no. 2021J02028X.

Ethical Statement

ORCID iD

Jinshu Zeng

Data Availability Statement

All data generated or analyzed during this study are included in this published article.

Supplemental Material

Supplemental material for this article is available online.

Appendix

References

Cabanillas

McFadden

Durante

. Thyroid cancer. Lancet Lond Engl. 2016;388(10061):2783-2795.

Sung

Ferlay

Siegel

, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA A Cancer J Clin. 2021;71(3):209-249.

Malterling

Andersson

Falkmer

Niléhn

Järhult

. Differentiated thyroid cancer in a Swedish county – long-term results and quality of life. Acta Oncol. 2010;49(4):454-459.

Lundgren

Hall

Dickman

Zedenius

. Clinically significant prognostic factors for differentiated thyroid carcinoma: a population-based, nested case–control study. Cancer. 2006;106(3):524-531.

Lee

Sung

Kim

Chung

Yoon

Hong

. Risk factors for recurrence in patients with papillary thyroid carcinoma undergoing modified radical neck dissection. Br J Surg. 2016;103(8):1020-1025.

Adam

Pura

Goffredo

, et al. Presence and number of lymph node metastases are associated with compromised survival for patients younger than age 45 Years with papillary thyroid cancer. J Clin Oncol. 2015;33(21):2370-2375.

Haugen

Alexander

Bible

, et al. 2015 American thyroid association management guidelines for adult patients with thyroid nodules and differentiated thyroid cancer: The American thyroid association guidelines task force on thyroid nodules and differentiated thyroid cancer. Thyroid Off J Am Thyroid Assoc. 2016;26(1):1–133.

Chan

Lang

BHH

Wong

. The pros and cons of routine central compartment neck dissection for clinically nodal negative (cN0) papillary thyroid cancer. Gland Surg. 2013;2(4):1.

Dismukes

Fazendin

Obiarinze

, et al. Prophylactic central neck dissection in papillary thyroid carcinoma: All risks, No reward. J Surg Res. 2021;264:230-235.

10.

Tessler

Middleton

Grant

, et al. ACR thyroid imaging, reporting and data system (TI-RADS): White paper of the ACR TI-RADS committee. J Am Coll Radiol. 2017;14(5):587-595.

11.

Yang

Zhang

Qiao

. Diagnostic accuracy of ultrasound, CT and their combination in detecting cervical lymph node metastasis in patients with papillary thyroid cancer: a systematic review and meta-analysis. BMJ Open. 2022;12(7):e051568.

12.

Huang

Rusiecki

Zhao

, et al. Thyroid-stimulating hormone, thyroid hormones and risk of papillary thyroid cancer: A nested case-control study. Cancer Epidemiol Biomark Prev Publ Am Assoc Cancer Res Cosponsored Am Soc Prev Oncol. 2017;26(8):1209-1218.

13.

Kimura

Van Keymeulen

Golstein

Fusco

Dumont

Roger

. Regulation of thyroid cell proliferation by TSH and other factors: a critical evaluation of in vitro models. Endocr Rev. 2001;22(5):631-656.

14.

Gudmundsson

Sulem

Gudbjartsson

, et al. Discovery of common variants associated with low TSH levels and thyroid cancer risk. Nat Genet. 2012;44(3):319-322.

15.

Gul

Ozdemir

Dirikoc

, et al.

Are endogenously lower serum thyroid hormones new predictors for thyroid malignancy in addition to higher serum thyrotropin?

Endocrine. 2010;37(2):253-260.

16.

Demircioglu

Aygun

, et al. Relationship between thyroid-stimulating hormone level and aggressive pathological features of papillary thyroid cancer. Med Bull Sisli Etfal Hosp. 2022;56(1):126-131.

17.

Sun

Liu

Zhang

. Sensitivity to thyroid hormone indices are associated with papillary thyroid carcinoma in Chinese patients with thyroid nodules. BMC Endocr Disord. 2023;23(1):126.

18.

Fan

, et al. Thyroid functional parameters and correlative autoantibodies as prognostic factors for differentiated thyroid cancers. Oncotarget. 2016;7(31):49930.

19.

Collins

Reitsma

Altman

Moons

KGM

. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement. Eur Urol. 2015;67(6):1142-1151.

20.

Leboulleux

Girard

Rose

, et al. Ultrasound criteria of malignancy for cervical lymph nodes in patients followed up for differentiated thyroid cancer. J Clin Endocrinol Metab. 2007;92(9):3590-3594.

21.

Haugen

Alexander

Bible

22.

Attia

Hussein

Issa

, et al. Association of BRAFV600E mutation with the aggressive behavior of papillary thyroid microcarcinoma: A meta-analysis of 33 studies. Int J Mol Sci. 2022;23(24):15626.

23.

Song

Sun

Dong

Huang

Zhou

. Predictive value of BRAFV600E mutation for lymph node metastasis in papillary thyroid cancer: A meta-analysis. Curr Med Sci. 2018;38(5):785-797.

24.

Kebebew

Weng

Bauer

, et al. The prevalence and prognostic value of BRAF mutation in thyroid cancer. Ann Surg. 2007;246(3):466-471.

25.

Kim

Chai

Park

, et al. Nomogram for predicting central node metastasis in papillary thyroid carcinoma. J Surg Oncol. 2017;115(3):266-272.

26.

Lee

Schneider

, et al.

Is BRAF mutation associated with lymph node metastasis in patients with papillary thyroid cancer?

Surgery. 2012;152(6):977-983.

27.

de Souza Meyer

Dora

Wagner

Maia

. Decreased type 1 iodothyronine deiodinase expression might be an early and discrete event in thyroid cell dedifferentation towards papillary carcinoma. Clin Endocrinol. 2005;62(6):672-678.

28.

Jonklaas

Nsouli-Maktabi

Soldin

. Endogenous thyrotropin and triiodothyronine concentrations in individuals with thyroid cancer. Thyroid. 2008;18(9):943-952.

29.

Xiang

Xie

Hong

Wang

. Papillary thyroid microcarcinomas located at the middle part of the middle third of the thyroid gland correlates with the presence of neck metastasis. Surgery. 2015;157(3):526-533.

30.

Liu

Xiao

Chen

, et al. Risk factor analysis for predicting cervical lymph node metastasis in papillary thyroid carcinoma: a study of 966 patients. BMC Cancer. 2019;19(1):622.

31.

Jiang

Chen

Tan

, et al. Clinical characteristics related to central lymph node metastasis in cN0 papillary thyroid carcinoma: A retrospective study of 916 patients. Internet J Endocrinol. 2014;2014:1-6.

32.

Joo

Park

Yoon

, et al. Prediction of occult central lymph node metastasis in papillary thyroid carcinoma by preoperative BRAF analysis using fine-needle aspiration biopsy: a prospective study. J Clin Endocrinol Metab. 2012;97(11):3996-4003.

33.

Chinese Society of Clinical Oncology (CSCO) . Diagnosis and treatment guidelines for persistent/recurrent and metastatic differentiated thyroid cancer 2018 (English version). Chin J Cancer Res. 2019;31(1):99-116.

34.

Zhao

. Meta-analysis of ultrasound for cervical lymph nodes in papillary thyroid cancer: diagnosis of central and lateral compartment nodal metastases. Eur J Radiol. 2019;112:14-21.

35.

Yoon

Kim

Moon

, et al. Contribution of computed tomography to ultrasound in predicting lateral lymph node metastasis in patients with papillary thyroid carcinoma. Ann Surg Oncol. 2011;18(6):1734-1741.

36.

Patel

Lind

McKinney

, et al. Clinical validation of a predictive model for the presence of cervical lymph node metastasis in papillary thyroid cancer. Am J Neuroradiol. 2018;39(4):756-761.

37.

Yoo

Song

Park

Lee

Tae

. Predictive factors and pattern of central lymph node metastasis in unilateral papillary thyroid carcinoma. Auris Nasus Larynx. 2016;43(1):79-83.

38.

Zaydfudim

Feurer

Griffin

Phay

. The impact of lymph node involvement on survival in patients with papillary and follicular thyroid carcinoma. Surgery. 2008;144(6):1070-1078.

39.

Peng

Wei

Zhang

, et al. Single‐cell transcriptomic landscape reveals the differences in cell differentiation and immune microenvironment of papillary thyroid carcinoma between genders. Cell Biosci. 2021;11:39.

40.

Shi

, et al. Single-cell transcriptomic analysis of the tumor ecosystems underlying initiation and progression of papillary thyroid carcinoma. Nat Commun. 2021;12:6058.

41.

Martin

Mustata

Enache

, et al. Platelet activation and inflammation in patients with papillary thyroid cancer. Diagnostics. 2021;11(11):1959.

42.

Kucuk

Oltulu

. The relationship between types of inflammatory cells located at the micro-environment of papillary thyroid microcarcinoma prognostic factors. J BUON Off J Balk Union Oncol. 2021;26(5):2157-2168.

43.

Zhou

Yin

, et al. METTL3 restrains papillary thyroid cancer progression via m6A/c-Rel/IL-8-mediated neutrophil infiltration. Mol Ther. 2021;29(5):1821.

44.

Ekpe-Adewuyi

Lopez-Campistrous

Tang

Brindley

McMullen

TPW

. Platelet derived growth factor receptor alpha mediates nodal metastases in papillary thyroid cancer by driving the epithelial-mesenchymal transition. Oncotarget. 2016;7(50):83684.

45.

Zhang

Wang

Dykstra

, et al. Platelet-derived growth factor receptor-α promotes lymphatic metastases in papillary thyroid cancer. J Pathol. 2012;228(2):241-250.

46.

Ceylan

Kumanlıoğlu

Oral

Ertan

Özcan

. The correlation of clinicopathological findings and neutrophil-to- lymphocyte and platelet-to-lymphocyte ratios in papillary thyroid carcinoma. Mol Imaging Radionucl Ther. 2019;28(1):15-20.

47.

Zhao

Zhou

Zhang

, et al. Blood immune indexes can predict lateral lymph node metastasis of thyroid papillary carcinoma. Front Endocrinol. 2022;13:995630.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.22 MB

0.83 MB