Dynamic Nomogram for Predicting Lateral Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma

Abstract

Objective

To establish a dynamic nomogram based on preoperative clinical data for prediction of lateral lymph node metastasis (LLNM) of papillary thyroid carcinoma.

Study Design

Retrospective study.

Setting

The Sixth Affiliated Hospital of Sun Yat-Sen University.

Methods

The data of 477 patients from 2 centers formed the training group and validation group and were retrospectively reviewed. Preoperative clinical factors influencing LLNM were identified by univariable and multivariable analysis and were to construct a predictive dynamic nomogram for LLNM. Receiver operating characteristic analysis and calibration curves were used to evaluate the predictive power of the nomogram.

Results

The following were identified as independent risk factors for LLNM: male sex (odds ratio [OR] = 4.6, P = .04), tumor size ≥10.5 mm (OR = 7.9, P = .008), thyroid nodules (OR = 6.1, P = .013), irregular tumor shape (OR = 24.6, P = .001), rich lymph node vascularity (OR = 9.7, P = .004), and lymph node location. The dynamic nomogram constructed with these factors is available at https://zxh1119.shinyapps.io/DynNomapp/. The nomogram showed good performance, with an area under the curve of 0.956 (95% CI, 0.925-0.986), a sensitivity of 0.87, and a specificity of 0.91, if high-risk patients were defined as those with a predicted probability ≥0.3 or total score ≥200. The nomogram performed well in the external validation cohort (area under the curve, 0.915; 95% CI, 0.862-0.967).

Conclusions

The dynamic nomogram for preoperative prediction of LLNM in papillary thyroid carcinoma can help surgeons identify high-risk patients and develop individualized treatment plans.