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An integrated AI model to improve diagnostic accuracy of ultrasound and output known risk features in suspicious thyroid nodules

  • Ultrasound
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

From the viewpoint of ultrasound (US) physicians, an ideal thyroid US computer-assisted diagnostic (CAD) system for thyroid cancer should perform well in suspicious thyroid nodules with atypical risk features and be able to output explainable results. This study aims to develop an explainable US CAD model for suspicious thyroid nodules.

Methods

A total of 2992 solid or almost-solid thyroid nodules were analyzed retrospectively. All nodules had pathological results (1070 malignancies and 1992 benignities) confirmed by ultrasound-guided fine-needle aspiration cytology and histopathology after thyroidectomy. A deep learning model (ResNet50) and a multiple risk features learning ensemble model (XGBoost) were used to train the US images of 2794 thyroid nodules. Then, an integrated AI model was generated by combining both models. The diagnostic accuracies of the three AI models (ResNet50, XGBoost, and the integrated model) were predicted in a testing set including 198 thyroid nodules and compared to the diagnostic efficacy of five ultrasonographers.

Results

The accuracy of the integrated model was 76.77%, while the mean accuracy of the ultrasonographers was 68.38%. Of the risk features, microcalcifications showed the highest contribution to the diagnosis of malignant nodules.

Conclusions

The integrated AI model in our study can improve the diagnostic accuracy of suspicious thyroid nodules and output the known risk features simultaneously, thus aiding in training young ultrasonographers by linking the explainable results to their clinical experience and advancing the acceptance of AI diagnosis for thyroid cancer in clinical practice.

Key Points

• We developed an artificial intelligence (AI) diagnosis model based on both deep learning and multiple risk feature ensemble learning methods.

• The AI diagnosis model showed higher diagnostic accuracy for suspicious thyroid nodules than ultrasonographers.

• The AI diagnosis model showed partial explainability by outputting the known risk features, thus aiding young ultrasonic doctors in increasing the diagnostic level for thyroid cancer.

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Abbreviations

ACR:

The American College of Radiology

AI:

Artificial intelligence

AUC:

The area under the curve

DL:

Deep learning

FNA:

Fine-needle aspiration

ROI:

Region of interest

TI-RADS:

Thyroid Image Radiology and Data System

US CAD:

US computer-assisted diagnosis

US:

Ultrasound

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Acknowledgements

We thank the ultrasonic physicians who took part in the labeling process of the study, as well as the engineers Yuli She and Yi Jing who gave good suggestions from a standpoint of medical image analysis. Last, we also want to give our sincere thanks to pathological physicians who offered the histopathological results for the thyroid nodules in our study.

Funding

This study has received funding from the National Key Research and Development Program of China (No. 2017YFA0700800), the National Natural Science Foundation of China (No. 81871366), the Key Research and Development Program of Shaanxi Province of China (No. 2021SF-346).

Author information

Author notes

  1. Dong Zhang

    Present address: Institute of Artificial Intelligence and Robotics, College of Artificial Intelligence, Xi’an Jiaotong University, Xi’an, 710049, China

  2. Juan Wang and Jue Jiang contributed equally to this work and are considered to be co-first authors.

Authors and Affiliations

  1. Department of Ultrasound, the Second Affiliated Hospital, Medical School of Xi’an Jiaotong University, Xi’an, 710004, China

    Juan Wang, Jue Jiang & Qi Zhou

  2. School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, 710049, Shaanxi, China

    Dong Zhang

  3. The Institute of Medical Science, The University of Tokyo, Shirokanedai 4-6-1, Minato-ku, Tokyo, 108-8639, Japan

    Yao-zhong Zhang

  4. Precision Medicine Institute, Western China Science and Technology Innovation Harbor, Xi’an Jiaotong University, Xi’an, 712000, China

    Long Guo

  5. Department of Computer Science, Columbia University, 500 West 120 Street, New York, NY, 10027, USA

    Yusheng Jiang

  6. Institute of Artificial Intelligence and Robotics, College of Artificial Intelligence, Xi’an Jiaotong University, Xi’an, 710049, China

    Shaoyi Du

Authors
  1. Juan Wang
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  2. Jue Jiang
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  3. Dong Zhang
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  4. Yao-zhong Zhang
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  7. Shaoyi Du
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  8. Qi Zhou
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Corresponding authors

Correspondence to Shaoyi Du or Qi Zhou.

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Guarantor

The scientific guarantor of this publication is Qi Zhou.

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 obtained.

Methodology

  • Retrospective

  • Diagnostic study

  • Performed at one institution

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Cite this article

Wang, J., Jiang, J., Zhang, D. et al. An integrated AI model to improve diagnostic accuracy of ultrasound and output known risk features in suspicious thyroid nodules. Eur Radiol 32, 2120–2129 (2022). https://doi.org/10.1007/s00330-021-08298-7

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  • DOI: https://doi.org/10.1007/s00330-021-08298-7

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