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Calcium-Scoring CT ScanA calcium-scoring CT scan, also known as coronary artery calcium (CAC) scan, detects calcium deposits in the coronary arteries. This test assesses the risk of coronary artery disease (CAD), which can lead to cardiovascular events such as angina, heart failure, and sudden cardiac arrest.A calcium-scoring CT scan is generally recommended for individuals at intermediate risk of CAD without symptoms. It includes:Men aged 40-75 and women aged 50-75: Especially those with a...
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Toward Reduction in False-Positive Thyroid Nodule Biopsies with a Deep Learning-based Risk Stratification System

Rikiya Yamashita1, Tara Kapoor1, Minhaj Nur Alam1

  • 1Departments of Biomedical Data Science (R.Y., T.K., M.N.A., A.G., M.U.A., E.A., N.D.S., H.S., D.L.R.) and Radiology (S.A.S., A.L.W., N.M., D.G., V.B., D.L.R., T.S.D.), Stanford University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305.

Radiology. Artificial Intelligence
|June 2, 2022
PubMed
Summary
This summary is machine-generated.

A new deep learning system for thyroid nodule risk stratification using ultrasound (US) cine images demonstrated superior diagnostic performance. This AI tool improved the specificity of the American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) recommendations.

Keywords:
Abdomen/GIComputer Applications–3DConvolutional Neural Network (CNN)DiagnosisHead/NeckNeural NetworksOncologySupervised LearningThyroidTransfer LearningUS

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Area of Science:

  • Medical Imaging and Diagnostics
  • Artificial Intelligence in Healthcare
  • Oncology and Radiology

Background:

  • Thyroid nodules are common, and accurate risk stratification is crucial for appropriate management.
  • Current methods like the American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) have limitations in specificity.
  • Advancements in deep learning offer potential for improved diagnostic accuracy in medical imaging.

Purpose of the Study:

  • To develop and evaluate a deep learning-based system for thyroid nodule risk stratification using ultrasound (US) cine images.
  • To compare the diagnostic performance of the deep learning system against existing methods, including 2D CNN, radiomics, and ACR TI-RADS.

Main Methods:

  • A retrospective study involving 192 biopsy-confirmed thyroid nodules evaluated with cine US.
  • Development of a deep learning system utilizing 3D volumetric cine US data for malignancy risk assessment.
  • Comparison of the deep learning system against Static-2DCNN, Cine-Radiomics, and ACR TI-RADS using fivefold cross-validation, with histopathology as ground truth.

Main Results:

  • The deep learning system achieved a significantly higher area under the receiver operating characteristic curve (AUC) of 0.88 compared to Static-2DCNN (0.72, P = .03).
  • The system showed a trend towards higher AUC than Cine-Radiomics (0.78) and ACR TI-RADS (0.80).
  • When used to revise ACR TI-RADS recommendations, the system significantly improved specificity (79.4% vs. 26.9%, P < .001) with comparable sensitivity (71% vs. 82%).

Conclusions:

  • The deep learning system leveraging US cine images demonstrates superior diagnostic performance for thyroid nodule risk stratification.
  • This AI-driven approach enhances the specificity of ACR TI-RADS recommendations, potentially reducing unnecessary biopsies.
  • The findings suggest a promising role for deep learning in improving the accuracy and efficiency of thyroid nodule evaluation.