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CAD system based on B-mode and color Doppler sonographic features may predict if a thyroid nodule is hot or cold

  • Ali Abbasian Ardakani
  • Ahmad Bitarafan-Rajabi
  • Afshin Mohammadi
  • Sepideh Hekmat
  • Aylin Tahmasebi
  • Mohammad Bagher ShiranEmail author
  • Ali MohammadzadehEmail author
Ultrasound

Abstract

Objectives

The aim of this study was to evaluate if the analysis of sonographic parameters could predict if a thyroid nodule was hot or cold.

Methods

Overall, 102 thyroid nodules, including 51 hyperfunctioning (hot) and 51 hypofunctioning (cold) nodules, were evaluated in this study. Twelve sonographic features (i.e., seven B-mode and five Doppler features) were extracted for each nodule type. The isthmus thickness, nodule volume, echogenicity, margin, internal component, microcalcification, and halo sign features were obtained in the B-mode, while the vascularity pattern, resistive index (RI), peak systolic velocity, end diastolic velocity, and peak systolic/end diastolic velocity ratio (SDR) were determined, based on Doppler ultrasounds. All significant features were incorporated in the computer-aided diagnosis (CAD) system to classify hot and cold nodules.

Results

Among all sonographic features, only isthmus thickness, nodule volume, echogenicity, RI, and SDR were significantly different between hot and cold nodules. Based on these features in the training dataset, the CAD system could classify hot and cold nodules with an area under the curve (AUC) of 0.898. Also, in the test dataset, hot and cold nodules were classified with an AUC of 0.833.

Conclusions

2D sonographic features could differentiate hot and cold thyroid nodules. The CAD system showed a great potential to achieve it automatically.

Key Points

• Cold nodules represent higher volume (p = 0.005), isthmus thickness (p = 0.035), RI (p = 0.020), and SDR (p = 0.044) and appear hypoechogenic (p = 0.010) in US.

• Nodule volume with an AUC of 0.685 and resistive index with an AUC of 0.628 showed the highest classification potential among all B-mode and Doppler features respectively.

• The proposed CAD system could distinguish hot nodules from cold ones with an AUC of 0.833 (sensitivity 90.00%, specificity 70.00%, accuracy 80.00%, PPV 87.50%, and NPV 75.00%).

Keywords

Machine learning Radionuclide imaging Thyroid nodule Thyrotropin Ultrasonography, Doppler 

Abbreviations

ATA

American Thyroid Association

CAD

Computer-aided diagnosis

EDV

End diastolic velocity

FNA

Fine needle aspiration

LEGP

Low-energy general purpose

PSV

Peak systolic velocity

RI

Resistive index

SDR

Peak systolic/end diastolic velocity ratio

SVM

Support vector machine

TSH

Serum thyrotropin

Notes

Funding

This study has received funding from the Iran University of Medical Sciences.

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Mohammad Bagher Shiran.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors has significant statistical expertise.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was not required because all diagnostic procedures were performed according to American thyroid association (ATA).

This study was approved by the ethics committee of the Iran University of Medical Sciences (No. IR.IUMS.REC 1395.95-04-30-29762).

Methodology

• prospective

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Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  1. 1.ENT and Head & Neck Research Center and Department, Hazrat Rasoul Akram HospitalIran University of Medical SciencesTehranIran
  2. 2.Department of Medical Physics, School of MedicineIran University of Medical SciencesTehranIran
  3. 3.Cardiovascular Intervention Research Center, Rajaie Cardiovascular Medical and Research CenterIran University of Medical SciencesTehranIran
  4. 4.Echocardiography Research Center, Rajaie Cardiovascular Medical and Research CenterIran University of Medical SciencesTehranIran
  5. 5.Department of Radiology, Faculty of MedicineUrmia University of Medical ScienceUrmiaIran
  6. 6.Department of Nuclear Medicine, School of Medicine, Hasheminejad HospitalIran University of Medical SciencesTehranIran
  7. 7.Department of Radiology, Rajaie Cardiovascular, Medical and Research CenterIran University of Medical SciencesTehranIran

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