Dynamic Nomogram for Predicting Lateral Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma. (1st June 2021) - Zhuo, Xianhua
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.