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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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A Machine Learning Model Based on Thyroid US Radiomics to Discriminate Between Benign and Malignant Nodules.

Antonino Guerrisi1, Elena Seri2, Vincenzo Dolcetti2

  • 1Radiology and Diagnostic Imaging Unit, Department of Clinical and Dermatological Research, San Gallicano Dermatological Institute IRCCS, Via Elio Chianesi 53, 00144 Rome, Italy.

Cancers
|November 27, 2024
PubMed
Summary
This summary is machine-generated.

This study developed a machine learning model using thyroid ultrasound images to accurately classify thyroid nodules as benign or malignant. The model achieved high accuracy, correctly identifying all malignant nodules in external testing.

Keywords:
machine learningnodulesradiomicsultrasound

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Thyroid nodules are common, with accurate diagnosis crucial for distinguishing benign from malignant cases.
  • Current diagnostic methods like ultrasound and fine needle biopsy have limitations, often operator-dependent.
  • Radiomics and machine learning offer advanced tools for improved diagnostic accuracy.

Purpose of the Study:

  • To develop and validate a machine learning model for classifying thyroid nodules as benign or malignant using ultrasound images.
  • To leverage radiomic features for enhanced diagnostic performance beyond traditional methods.

Main Methods:

  • Collected ultrasonography images from 142 subjects (40 malignant, 102 benign) confirmed by histology.
  • Applied a radiomic approach with machine learning classifiers (random forests, SVM, k-NN) for binary classification.
  • Validated the best performing model on an independent external cohort of 21 patients.

Main Results:

  • The best model, an ensemble of random forests, achieved 85% ROC-AUC and 83% accuracy in internal testing.
  • In external validation, the model demonstrated 90.5% accuracy, 100% sensitivity, and 86.7% specificity.
  • The model successfully identified all malignant nodules and a high proportion of benign nodules in the external cohort.

Conclusions:

  • A machine learning model based on radiomic features from ultrasound images can effectively differentiate benign from malignant thyroid nodules.
  • The developed random forest ensemble model shows high potential for clinical application in thyroid nodule diagnosis.
  • This approach offers a promising, objective tool to aid clinicians in diagnosing thyroid nodules, potentially reducing misdiagnosis.