Nomograms for predicting cervical central lymph node metastases and high-volume cervical central lymph node metastases in papillary thyroid carcinoma

Abstract

Background: Cervical central lymph node metastasis (CLNM) is a known risk factor for recurrent thyroid cancer (TC), and cervical high-volume central lymph node metastases (HVCLNM) are associated with higher recurrence rates and shorter disease-specific survival. The status of CLNM is critical in determining surgical strategies for papillary thyroid carcinoma (PTC). We developed two separate nomograms to predict the probability of CLNM and HVCLNM.

Methods: We retrospectively analyzed 590 PTC patients who underwent total thyroidectomy or lobectomy with central lymph node dissection (CLND) between January 2020 and May 2023. Univariate and multivariate analyses were conducted to identify risk factors associated with CLNM and HVCLNM. The nomograms were internally validated using bootstrapping and evaluated on a temporal validation cohort.

Results: Between January 2020 and May 2023, 1,019 patients were screened, 590 (57.9%) were eligible, and they were divided into development (n=353) and validation (n=237) cohorts. HVCLNM was present in 41 patients (11.6%). The variables with the strongest predictive value for CLNM were younger age (P<0.001), male sex (P=0.045), tumor size (P<0.001), and tumor multifocality (P=0.001). The strongest predictors for HVCLNM were younger age (P=0.001), tumor size (P<0.001), bilateral lesions (P=0.005), and preoperative serum thyroid peroxidase antibody (TPOAb) ≤14.95 IU/mL (P=0.01). The area under the curve (AUC) for the CLNM model was 0.75, with similar results achieved in internal validation (0.74) and external validation (0.68). The AUC for the HVCLNM model was 0.80, with similar values in internal validation (0.79) and external validation (0.79). Both models demonstrated good calibration, with predictions closely aligning with observed outcomes.

Conclusions: Based on the quantified risk stratification offered by our nomograms, clinicians can engage in comprehensive preoperative discussions with PTC patients. Prophylactic CLND and strict postoperative evaluation may be recommended for patients with high nomogram scores.

Keywords: Papillary thyroid carcinoma (PTC); cervical high-volume central lymph node metastases (cervical HVCLNM); nomogram; thyroid peroxidase antibody (TPOAb).

Figure 1

PTC patients exclusion flowchart. CLND, central lymph node dissection; PTC, papillary thyroid carcinoma; TC, thyroid cancer.

Figure 2

ROC curve and optimal cut-off value of TgAb-CLNM (A), TPOAb-CLNM (B), TgAb-HVCLNM (C), TPOAb-HVCLNM (D). AUC, area under the curve; CLNM, central lymph node metastasis; HVCLNM, high-volume central lymph node metastasis; ROC, receiver operating characteristic; TgAb, thyroglobulin antibody; TPOAb, thyroid peroxidase antibody.

Figure 3

Nomograms based on clinical characteristics present the risk factors for CLNM (A) and HVCLNM (B). CLNM, central lymph node metastasis; HVCLNM, high-volume central lymph node metastasis; TPOAb, thyroid peroxidase antibody.

Figure 4

ROC curve shows nomogram predication model for CLNM in training cohort (A), and in validation cohort (B), in addition to the predication model for HVCLNM in training cohort (C), in validation cohort (D). AUC, area under the curve; CLNM, central lymph node metastasis; HVCLNM, high-volume central lymph node metastasis; ROC, receiver operating characteristic.

Figure 5

Calibration curve of the predication model for CLNM in training cohort (A) and validation cohort (B), in addition to nomogram prediction model for HVCLNM in training cohort (C) and validation cohort (D). The closer the two lines are, the higher the accuracy of the model. CLNM, central lymph node metastasis; HVCLNM, high-volume central lymph node metastasis.

Figure 6

The decision curve of the nomogram model and single factors of CLNM in training cohort (A) and validation cohort (B), in addition to the nomogram prediction model for HVCLNM in training cohort (C) and validation cohort (D). Decision curves of CLNM/HVCLNM risk factors present in the training cohort respectively. The gray line represents CLNM or HVCLNM positive and the horizontal black line represents CLNM or HVCLNM negative. When the red line is above the gray and black lines, the nomogram model possesses a net return at this very risk threshold. CLNM, central lymph node metastasis; HVCLNM, high-volume central lymph node metastasis.