Nomogram for predicting the risk of central lymph node metastasis in papillary thyroid microcarcinoma: a combination of sonographic findings and clinical factors

Abstract

Background: A considerable controversy over performing thyroidectomy and central lymph node dissection in patients with papillary thyroid microcarcinoma (PTMC) remained. However, accurate prediction of central lymph node metastasis (CLNM) is crucial for surgical extent and proper management. The aim of this study was to develop and validate a practical nomogram for predicting CLNM in patients with PTMC.

Methods: A total of 1,029 patients with PTMC who underwent thyroidectomy and central lymph node dissection at Tangdu Hospital (the Second Affiliated Hospital of Air Force Medical University) and Xijing Hospital (the First Affiliated Hospital of Air Force Medical University) were selected. Seven hundred and nine patients were assigned to the training set and 320 patients to the validation set. Data encompassing demographic characteristics, ultrasonography results, and biochemical indicators were obtained. Stepwise backward selection and multiple logistic regression were used to screen the variables and establish the nomogram. Concordance index (C-index), receiver operating characteristic (ROC) curve analysis, and decision curve analysis (DCA) were employed to evaluate the nomogram's distinguishability, accuracy, and clinical utility.

Results: Young age, multifocality, bigger tumor, presence of microcalcification, aspect ratio (height divided by width) ≥1, loss of fatty hilum, high free thyroxine (FT4), and lower anti-thyroid peroxidase antibody (TPOAb) were significantly associated with CLNM. The nomogram showed strong predictive capacity, with a C-index and accuracy of 0.784 and 0.713 in the training set and 0.779 and 0.703 in the external validation set, respectively. DCA indicated that the nomogram demonstrated strong clinical applicability.

Conclusions: We established a reliable, cost-effective, reproducible, and noninvasive nomogram for predicting CLNM in patients with PTMC. This tool could be a valuable guidance for deciding on management in PTMC.

Keywords: Nomogram; central lymph node metastasis (CLNM); microcarcinoma; papillary thyroid cancer (PTC); ultrasonography (US).

Figure 1

Patients recruited in the training set (A) and the validation set (B). PTMC, papillary thyroid micropapillary carcinoma; CLND, central lymph node dissection; CLNM (+), pathologically positive lymph nodes; CLNM (−), pathologically negative lymph nodes.

Figure 2

Nomogram to predict CLNM in patients with PTMC based on eight predictors. Adding up the total scores of each predictive factor, finding the point corresponding to the horizontal axis of the score, and drawing a perpendicular line will yield the corresponding probability of CLNM. FT4, free thyroxine; TPOAb, thyroid peroxidase antibody; CLNM, central lymph node metastasis; PTMC, papillary thyroid micropapillary carcinoma.

Figure 3

The calibration plot of the nomogram in the training (A) and validation (B) sets. The gray solid line represents the ideal model. The black solid line represents the prediction performance of the nomogram, and the black dotted line is the bias-corrected estimate. The better the predictive power of the nomogram, the closer the logistical calibration line is to the ideal line. ROC, receiver operating characteristic.

Figure 4

ROC curves of the nomogram in the training (A) and validation (B) sets. ROC, receiver operating characteristic; CI, confidence interval.

Figure 5

Decision curve analysis of the nomogram in the training (A) and validation (B) sets. The yellow curve represents the hypothesis that all patients underwent CLND. The blue curve represents the hypothesis that none of the patients underwent CLND. The green curve represents the benefit of developing a treatment plan based on our prediction model. According to the decision curve, when the patient’s threshold was 12–85%, compared to using a treat-all or treat-none approach, using our predictive model to formulate treatment strategies yielded more benefit. DCA, decision curve analysis; NOMO, nomogram; CLND, central lymph node dissection.