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A machine learning-based sonomics for prediction of thyroid nodule malignancies

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Abstract

Objectives

This study aims to use ultrasound derived features as biomarkers to assess the malignancy of thyroid nodules in patients who were candidates for FNA according to the ACR TI-RADS guidelines.

Methods

Two hundred and ten patients who met the selection criteria were enrolled in the study and subjected to ultrasound-guided FNA of thyroid nodules. Different radiomics features were extracted from sonographic images, including intensity, shape, and texture feature sets. Least Absolute Shrinkage and Selection Operator (LASSO), Minimum Redundancy Maximum Relevance (MRMR), and Random Forests/Extreme Gradient Boosting Machine (XGBoost) algorithms were used for feature selection and classification of the univariate and multivariate modeling, respectively. Evaluation of models performed using accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC).

Results

In the univariate analysis, Gray Level Run Length Matrix - Run-Length Non-Uniformity (GLRLM-RLNU) and gray-level zone length matrix - Run-Length Non-Uniformity (GLZLM-GLNU) (both with an AUC of 0.67) were top-performing for predicting nodules malignancy. In the multivariate analysis of the training dataset, the AUC of all combinations of feature selection algorithms and classifiers was 0.99, and the highest sensitivity was for XGBoost classifier and MRMR feature selection algorithms (0.99). Finally, the test dataset was used to evaluate our model in which XGBoost classifier with MRMR and LASSO feature selection algorithms had the highest performance (AUC = 0.95).

Conclusions

Ultrasound-extracted features can be used as non-invasive biomarkers for thyroid nodules’ malignancy prediction.

Key Points

  • Ultrasound imaging features can be used for thyroid nodules’ malignancy prediction.

  • The quantitative features of ultrasound images can be used as non-invasive biomarkers.

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Abbreviations

ACR:

American College of Radiology

TI-RADS:

Thyroid Imaging, Reporting, and Data System

ML:

Machine Learning

FNA:

Fine-Needle Aspiration

ROI:

Region of Interest

GLZLM:

Grey-Level Zone Length Matrix

NGLDM:

Neighbor Grey-Level Different Matrix

GLCM:

Grey Level Co-Occurrence Matrix

GLRLM:

Grey-Level Run-Length Matrix

FDR:

False Discovery Rate

ROC:

Receiver Operating Characteristic

SMOTE:

Synthetic Minority Oversampling Technique

LASSO:

Least Absolute Shrinkage and Selection Operator

MRMR:

Max-Relevance Min-Redundancy

RF:

Random Forests

XGBoost:

Extreme Gradient Boosting Machine

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Funding

This work was supported by the Alborz University of Medical Sciences.

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Authors and Affiliations

  1. Department of Physiology, Pharmacology and Medical Physics, Alborz University of Medical Sciences, Karaj, Iran

    Mohsen arabi & Sajad P. Shayesteh

  2. Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran

    Mostafa Nazari

  3. Department of Radiology, Alborz University of Medical Sciences, Karaj, Iran

    Ali Salahshour

  4. Research Centre for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran

    Elnaz Jenabi & Maziar Khateri

  5. Rajaie Cardiovascular, Medical & Research Center, Iran University of Medical Science, Tehran, Iran

    Ghasem Hajianfar

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  1. Mohsen arabi
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Contributions

Conceptualization: S.P.S., M.N.; Methodology/study design: S.P.S., E.J., M.A.; Software: M.N., M.K.; Validation: E.J., S.P.S.; Formal analysis: M.N., G.H.; Investigation: M.A., S.P.S.; Resources: M.K., E.J.; Data curation: A.S., S.P.S., M.A.; Writing—original draft: E.J., S.P.S.; Writing—review and editing: M.A., M.K.; Visualization: E.J.; Supervision: S.P.S.; Project administration: S.P.S., A.S.; Funding acquisition: S.P.S., A.S.

Corresponding author

Correspondence to Sajad P. Shayesteh.

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arabi, M., Nazari, M., Salahshour, A. et al. A machine learning-based sonomics for prediction of thyroid nodule malignancies. Endocrine 82, 326–334 (2023). https://doi.org/10.1007/s12020-023-03407-6

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  • DOI: https://doi.org/10.1007/s12020-023-03407-6

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