Individualized Prediction Of Metastatic Involvement Of Lymph Nodes Posterior To The Right Recurrent Laryngeal Nerve In Papillary Thyroid Carcinoma

Abstract

Purpose: We aimed to establish a prediction model based on preoperative clinicopathologic features and intraoperative frozen section examination for precise prediction of metastatic involvement of lymph nodes posterior to the right recurrent laryngeal nerve (LN-prRLN) in patients with papillary thyroid carcinoma (PTC).

Methods: Clinicopathologic data pertaining to patients with PTC who underwent initial thyroid surgery between July 2015 and December 2017 were collected from electronic medical records. Multivariate logistic regression analysis was performed to identify independent predictors of LN-prRLN metastasis for incorporation in the nomogram. The performance of the model was assessed using discriminative ability, calibration, and clinical application.

Results: A total of 592 patients were enrolled in this study. The LN-prRLN metastatic positivity was 19% (95% confidence interval [CI], 15.61-21.89%). On multivariate analysis, ultrasonography-reported LN status, extrathyroid extension, Delphian lymph node metastasis, and number of metastatic pretracheal and paratracheal lymph nodes were independent predictors of LN-prRLN metastasis. The nomogram showed good discriminative ability (C-index: 0.87; [95% CI, 0.84-0.91]; bias-corrected C-index: 0.86 [through bootstrapping validation]) and was well calibrated. The decision curve analysis indicated potential clinical usefulness of the nomogram.

Conclusion: This study demonstrates that the risk of LN-prRLN metastasis in individual patients can be robustly predicted by a nomogram that integrates readily available preoperative clinicopathologic features and intraoperative frozen section examination. The nomogram may facilitate intraoperative decision-making for patients with PTC.

Keywords: lymph node metastasis; lymph nodes posterior to the right recurrent laryngeal nerve; nomogram; papillary thyroid carcinoma; predict model.

Figure 1

Schematic illustration of the patient selection criteria.

Figure 2

Nomogram for prediction of LN-prRLN metastases in patients with PTC. A line is drawn straight up to the point axis that corresponds with each patient variable to obtain the points. The sum of these points is located on the total score points axis. Lastly, a line is drawn downwards to the risk axis to determine the likelihood of LN-prRLN metastases. Abbreviations: US-reported LN status, ultrasound-reported lymph node status; ETE, extrathyroidal extension; Delphian LNM, Delphian lymph node metastasis; No. of pretracheal LNM, number of pretracheal lymph node metastasis; No. of paratracheal LNM, number of paratracheal lymph node metastasis.

Figure 3

Calibration plot of the nomogram. The calibration curve depicts the calibration of model in terms of the agreement between the predicted risks of LN-prRLN metastasis and observed outcomes of LN-prRLN metastasis. The x-axis represents the predicted LN-prRLN metastasis risk. The y-axis represents the actual LN-prRLN metastasis rate. The diagonal dotted blue line represents an ideal calibration model in which the predicted probabilities are identical to the actual outcomes. The dotted line represents the predictive performance of the nomogram; the closer the fit of the dotted line to the ideal line, the better the prediction. The black line represents the bias corrected.

Figure 4

Decision curve analysis for the nomogram. The y-axis measures the net benefit. The x-axis represents the threshold probability. The red solid line represents the nomogram. The gray line represents the hypothesis that all patients had LN-prRLN metastasis. The black solid line represents the hypothesis that none of the patients had LN-prRLN metastasis. The decision curve indicates that when the threshold probability is >10.0%, use of this predictive model would accrue greater benefit than that accruing from a treat-all or treat-none strategy.