Establishing a Predictive Nomogram for Cervical Lymph Node Metastasis in Patients With Papillary Thyroid Carcinoma

Clinical prediction models for cervical lymph node metastasis of papillary thyroid carcinoma

PURPOSE

Accurate preoperative diagnosis of lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) remains an unsolved problem. This study aimed to construct a nomogram and scoring system for predicting LNM based on the clinical characteristics of patients with PTC.

METHODS

1400 patients with PTC who underwent thyroidectomy and lymph node dissection at the First Affiliated Hospital of Sun Yat-sen University were retrospectively enrolled and randomly divided into training and internal testing sets. Furthermore, 692 patients with PTC from three other medical centers were collected as external testing sets. Least absolute shrinkage and selection operator (LASSO) was used to screen the predictors, and a nomogram was constructed. In addition, a scoring system was constructed using 10-fold cross-validation. The performances of the two models were verified among datasets and compared with preoperative ultrasound (US).

RESULTS

Six independent predictors were included in the multivariate logistic model: age, sex, US diagnosis of LNM, tumor diameter, location, and thyroid peroxidase antibody level. The areas under the receiver operating characteristic curve (AUROC) (95% confidence interval) of this nomogram in the training, internal testing, and three external testing sets were 0.816 (0.791-0.840), 0.782 (0.727-0.837), 0.759 (0.699-0.819), 0.749 (0.667-0.831), and 0.777 (0.726-0.828), respectively. The AUROC of the scoring system were 0.810 (0.785-0.835), 0.772 (0.718-0.826), 0.736 (0.675-0.798), 0.717 (0.635-0.799) and 0.756 (0.704-0.808), respectively. The prediction performances were both significantly superior to those of preoperative US (P < 0.001).

CONCLUSION

The nomogram and scoring system performed well in different datasets and significantly improved the preoperative prediction of LNM than US alone.

Risk factors and nomogram to predict skip metastasis in papillary thyroid carcinoma

Background

Papillary thyroid carcinoma (PTC) is the most common endocrine malignancy. Skip metastases of PTCs are easily misdiagnosed before surgery, and it could lead to re-operation and affect the prognosis. Although there are a few studies about nomograms for predicting central lymph node metastases (CLNM) or lateral lymph node metastases (LLNM) of PTCs, there are few studies about nomograms for skip metastases. Based on the clinical and ultrasonographic characteristics of patients with PTCs, the aim of our study was to investigate the risk factors and establish a nomogram for predicting the risk of skip metastases in PTCs.

Methods

This study enrolled 218 PTCs patients with lateral cervical lymph node metastases and their data were analyzed retrospectively. According to the postoperative pathological results, the patients were divided into skip-positive group and skip-negative group. In order to establish the nomogram, univariate and multivariate analyses were used to estimate risk factors of skip metastases. The receiver operating characteristic (ROC) curve, internal calibration plot and decision curve analysis (DCA) were used to evaluate the nomogram model's efficacy.

Results

There were statistical differences between skip-positive group and skip-negative group in tumor location, the maximum diameter (D) and capsule invasion (P<0.05). No statistical differences were observed in sex, age, Hashimoto's thyroiditis, multifocality, anteroposterior diameter/transverse diameter (A/T) ratio, shape, margin, microcalcification, intra-nodular vascularity and preoperative serum thyroglobulin (Tg) (P≥0.05). The risk factors of skip metastases in PTCs were D ≤10 mm, location in the upper portion and capsule invasion. The area under the curve (AUC) of nomogram was 0.877, the accuracy was 85.32%, the sensitivity was 60.98%, and the specificity was 90.96%. The calibration curve and the Hosmer-Lemeshow goodness of fit test showed that the consistency between the nomogram and the actual observation was good. The DCA showed that most PTC patients might benefit from the predictive nomogram model.

Conclusions

A nomogram for predicting skip metastases in PTCs may be useful in clinical diagnosis and treatment.

Accuracy of papillary thyroid cancer prognostic nomograms: a systematic review

Objective

Current staging and risk-stratification systems for predicting survival or recurrence of patients with differentiated thyroid carcinoma may be ineffective at predicting outcomes in individual patients. In recent years, nomograms have been proposed as an alternative to conventional systems for predicting personalized clinical outcomes. We conducted a systematic review to evaluate the predictive performance of available nomograms for thyroid cancer patients.

Design and methods

PROSPERO registration (CRD42022327028). A systematic search was conducted without time and language restrictions. PICOT questions: population, patients with papillary thyroid cancer; comparator prognostic factor, single-arm studies; outcomes, overall survival, disease-free survival, cancer-specific survival, recurrence, central lymph node metastases, or lateral lymph node metastases; timing, all periods; setting, hospital setting. Risk of bias was assessed through PROBAST tool.

Results

Eighteen studies with a total of 20 prognostic models were included in the systematic review (90,969 papillary thyroid carcinoma patients). Fourteen models were at high risk of bias and four were at unclear risk of bias. The greatest concerns arose in the analysis domain. The accuracy of nomograms for overall survival was assessed in only one study and appeared limited (0.77, 95% CI: 0.75-0.79). The accuracy of nomograms for disease-free survival ranged from 0.65 (95% CI: 0.55-0.75) to 0.92 (95% CI: 0.91-0.95). The C-index for predicting lateral lymph node metastasis ranged from 0.72 to 0.92 (95% CI: 0.86-0.97). For central lymph node metastasis, the C-index of externally validated studies ranged from 0.706 (95% CI: 0.685-0.727) to 0.923 (95% CI: 0.893-0.946).

Conclusions

Our work highlights the extremely high heterogeneity among nomograms and the critical lack of external validation studies that limit the applicability of nomograms in clinical practice. Further studies ideally using commonly adopted risk factors as the backbone to develop nomograms are required.

Significance statement

Nomograms may be appropriate tools to plan treatments and predict personalized clinical outcomes in patients with papillary thyroid cancer. However, the nomograms developed to date are very heterogeneous, and their results seem to be closely related to the specific samples studied to generate the same nomograms. The lack of rigorous external validation procedures and the use of risk factors that sometimes appear to be far from those commonly used in clinical practice, as well as the great heterogeneity of the risk factors considered, limit the ability of nomograms to predict patient outcomes and thus their current introduction in clinical practice.

Clinical Value of Ultrasonography and Serum Markers in Preoperative N Staging of Thyroid Cancer

We aimed to determine factors influencing lymph node metastasis (LNM) and develop a more effective method to assess preoperative N staging. Overall, data of 2130 patients who underwent thyroidectomy for thyroid cancer between 2018 and 2021 were retrospectively analysed. Patients were divided into groups according to pN0, pN1a, and pN1b stages. Pathology was used to analyse the correlation between preoperative serum marker indicators and LNM. Receiver operating characteristic curves were used to compare the diagnostic value of ultrasound (US) examination alone, serum thyroglobulin, age, and combined method for LNM. A significant moderate agreement was observed between preoperative US and postoperative pathology for N staging. Between the pN0 and pN1 (pN1a + pN1b) groups, the differences in free triiodothyronine, anti-thyroid peroxidase antibody, and serum thyroglobulin levels were statistically significant. Among the indicators, serum thyroglobulin was an independent predictor of LNM. The area under the receiver operating characteristic curve was 0.610 for serum thyroglobulin level for predicting LNM, 0.689 for US alone, and 0.742 for the combined method. Both preoperative US and serum thyroglobulin level provide a specific value when evaluating the N staging of thyroid cancer, and the combined method is more valuable in the diagnosis of LNM than US alone.

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

Objectives

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

Materials and methods

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

Results

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

Conclusions

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