Predictive value of a multimodal radiomics model for central lymph node metastasis in clinically node-negative papillary thyroid microcarcinoma based on machine learning

Jiawei FENG; Yuxin YANG; Shuiqing LIU; Ancheng QIN; Jing YE; Yong Jiang

doi:10.3724/zdxbyxb-2025-0648

January 1, 2026Journal of Zhejiang University (Medical Sciences)Open Access

Predictive value of a multimodal radiomics model for central lymph node metastasis in clinically node-negative papillary thyroid microcarcinoma based on machine learning

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Key Points

The research aims to evaluate the effectiveness of a multimodal radiomics model in predicting central lymph node metastasis in patients with clinically node-negative papillary thyroid microcarcinoma.
Developed a radiomics model incorporating multiple imaging features
Utilized machine learning techniques for prediction
Analyzed data from patients diagnosed with cN0 PTMC
The model accurately predicts the risk of central lymph node metastasis (CLNM)
Indicates potential for personalized preoperative risk assessments

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Implication

Demonstrates a machine learning framework that predicts central lymph node metastasis in clinically node-negative papillary thyroid microcarcinoma, suggesting improved preoperative...

Abstract

The multimodal radiomics model based on GBM can accurately predict the risk of CLNM in patients with cN0 PTMC, which may facilitate individualized preoperative risk assessment.

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Cite This Study

FENG et al. (2026) studied this question.

synapsesocial.com/papers/69730f78c8125b09b0d1f4e9 https://doi.org/10.3724/zdxbyxb-2025-0648

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Predictive value of a multimodal radiomics model for central lymph node metastasis in clinically node-negative papillary thyroid microcarcinoma based on machine learning

Key Points

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Implication

Abstract

Discussion

Cite This Study

Predictive value of a multimodal radiomics model for central lymph node metastasis in clinically node-negative papillary thyroid microcarcinoma based on machine learning

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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