Precision of predictive nomograms for lymph node metastasis of thyroid cancer from Chinese real-world study: a
Yongke Wu1, Yuanhao Su1, Yiyuan Zhao1
1Department of Geriatric General Surgery, The Second Affiliated Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Background:
Current guidelines lack nomograms to predict lymph node metastasis (LNM) in thyroid carcinoma (TC) in China. Nomograms are simple, accurate tools to estimate the probability of specific events and have been extensively developed to predict LNM in TC. However, few effective nomograms have been validated in clinical practice.
Methods:
The recommendations of the Cochrane Prognosis Methods Group were implemented in this systematic review. We conducted searches in PubMed, Web of Science, and Scopus for published research. The nomogram was categorized based on outcomes. We summarized the key characteristics and effectiveness of the nomogram and assessed the overall risk of bias (ROB). We employed random-effects and bivariate mixed-effects models to estimate the efficacy of the nomogram group and its predictive reliability.
Results:
The systematic review identified 57 nomogram models from China, of which only 14 had external validation cohorts. While the applicability was acceptable, the heterogeneity among the included nomograms was substantial, leading to a high overall risk of bias (ROB). Ultrasound information was utilized in nearly all studies. Size, extrathyroidal extension (ETE), tumor consistency index (TCI), and multifocality are commonly employed independent risk factors. Both outcome models showed good to excellent predictive efficacy. However, the performance of models that integrate radiomics with clinical features was inferior to those using ultrasound alone.
Conclusions:
The feature-combined model offers several potential outcomes and advantages for clinical practice in China. Additionally, the systematic review serves as a reference tool for physicians to select appropriate nomograms based on individual clinical needs. Future research should focus on external validation and evaluation to minimize limitations in clinical utility.
