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The Predictive Value of a Nomogram Based on Ultrasound Radiomics, Clinical Factors, and Enhanced Ultrasound Features for Central Lymph Node Metastasis in Papillary Thyroid Microcarcinoma

  • 0Department of Ultrasound, South China Hospital, Medical School, Shenzhen University, Shenzhen, China.
Ultrasonic Imaging +

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January 27, 2025

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January 27, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

This study developed an ultrasound radiomics nomogram to predict central lymph node metastasis in papillary thyroid microcarcinoma (PTMC). The combined model accurately predicted metastasis, showing good clinical utility for early detection.

Area Of Science

  • Oncology
  • Radiology
  • Medical Imaging

Background

  • Papillary thyroid microcarcinoma (PTMC) is the smallest form of thyroid cancer.
  • Accurate preoperative prediction of central lymph node metastasis (CLNM) in PTMC is crucial for treatment planning.
  • Current prediction methods have limitations in sensitivity and specificity.

Purpose Of The Study

  • To establish and validate an ultrasound radiomics nomogram for preoperative prediction of CLNM in PTMC.
  • To compare the predictive performance of clinical features, ultrasound features, and radiomics scores.
  • To assess the clinical utility of the developed nomogram.

Main Methods

  • Retrospective analysis of 288 PTMC patients, divided into training (n=201) and validation (n=87) cohorts.
  • Extraction and selection of radiomics features using LASSO regression to create a radiomics score (Radscore).
  • Development of prediction models: ultrasound + clinical (US-Clin), Radscore, and a combined model, evaluated using logistic regression and nomograms.

Main Results

  • The combined model demonstrated high predictive performance with AUCs of 0.921 (training) and 0.889 (validation).
  • Independent risk factors identified included age <45, tumor envelope invasion, male gender, microcalcifications, and extrathyroidal expansion.
  • The nomogram showed good calibration and clinical utility, as indicated by Decision Curve Analysis (DCA).

Conclusions

  • A preoperative ultrasound radiomics nomogram integrating clinical data, ultrasound features, and Radscore can accurately predict CLNM in PTMC.
  • The nomogram offers improved accuracy over models using only clinical or ultrasound features.
  • Further validation in multicenter studies with larger sample sizes and genomic analysis is recommended.

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