Predictive value of a multimodal radiomics model for central lymph node metastasis in clinically node-negative papillary thyroid microcarcinoma based on machine learning
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
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.
Prädiktiver Wert eines multimodalen Radiomik-Modells für zentrale Lymphknotenmetastasen bei klinisch nodal-negativem papillärem Schilddrüsenmikrokrebs basierend auf maschinellem Lernen | Synapse