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Optimized dual-source dual-energy computed tomography nomogram model integrating background normalization improves

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Summary
This summary is machine-generated.

A dual-source dual-energy computed tomography (DS-DECT) nomogram effectively predicts malignant thyroid nodules using quantitative and qualitative imaging features. This tool aids physicians in distinguishing benign from malignant nodules, improving patient management.

Keywords:
Thyroid noduledual-energy computed tomography (DECT)nomogram

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Area of Science:

  • Radiology
  • Oncology
  • Medical Imaging

Background:

  • Accurate preoperative diagnosis of thyroid nodules is essential for personalized treatment.
  • Distinguishing between benign and malignant nodules impacts patient management strategies.

Purpose of the Study:

  • To develop a dual-source dual-energy computed tomography (DS-DECT) based nomogram model.
  • To predict the risk of malignancy in thyroid nodules.

Main Methods:

  • A total of 263 patients with 288 thyroid nodules were included.
  • Radiological features and DS-DECT quantitative parameters were analyzed.
  • A nomogram was constructed using logistic regression and validated internally.

Main Results:

  • Independent predictors of malignancy included iodine concentration, normalized iodine concentration, effective atomic number, thyroid edge interruption, and enhanced blurring.
  • The nomogram achieved high diagnostic performance with AUCs of 0.932 (training) and 0.908 (validation).
  • The model demonstrated good fit and calibration, with significant clinical utility shown by decision curve analysis.

Conclusions:

  • The developed nomogram is an effective tool for distinguishing benign from malignant thyroid nodules.
  • It integrates multiphase quantitative DS-DECT parameters and qualitative imaging features.
  • The model supports imaging physicians in preoperative diagnosis and patient management.