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ELTIRADS framework for thyroid nodule classification integrating elastography, TIRADS, and radiomics with interpretable machine learning

E. Barzegar-Golmoghani, M. Mohebi, Z. Gohari, S. Aram, A. Mohammadzadeh, S. Firouznia, M. Shakiba, H. Naghibi, S. Moradian, M. Ahmadi, K. Almasi, M. Issaiy, M. Anjomrooz, SM. Tavangar, S. Javadi, A. Bitarafan-Rajabi, M. Davoodi, H. Sharifian, M....

. 2025 ; 15 (1) : 8763. [pub] 20250313

Language English Country England, Great Britain

Document type Journal Article

Persistent link   https://www.medvik.cz/link/bmc25009503

Early detection of malignant thyroid nodules is crucial for effective treatment, but traditional diagnostic methods face challenges such as variability in expert opinions and limited integration of advanced imaging techniques. This prospective cohort study investigates a novel multimodal approach, integrating traditional methods with advanced machine learning techniques. We studied 181 patients who underwent fine-needle aspiration (FNA) biopsy, each contributing one nodule, resulting in a total of 181 nodules for our analysis. Data collection included sex, age, and ultrasound imaging, which incorporated elastography. Features extracted from these images included Thyroid Imaging Reporting and Data System (TIRADS) scores, elastography parameters, and radiomic features. The pathological results based on the FNA biopsy, provided by the pathologists, served as our gold standard for nodule classification. Our methodology, termed ELTIRADS, combines these features with interpretable machine learning techniques. Performance evaluation showed that a Support Vector Machine (SVM) classifier using TIRADS, elastography data, and radiomic features achieved high accuracy (0.92), with sensitivity (0.89), specificity (0.94), precision (0.89), and F1 score (0.89). To enhance interpretability, we used hierarchical clustering, shapley additive explanations (SHAP), and partial dependence plots (PDP). This combined approach holds promise for enhancing the accuracy of thyroid nodule malignancy detection, thereby contributing to advancements in personalized and precision medicine in the field of thyroid cancer research.

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$a Early detection of malignant thyroid nodules is crucial for effective treatment, but traditional diagnostic methods face challenges such as variability in expert opinions and limited integration of advanced imaging techniques. This prospective cohort study investigates a novel multimodal approach, integrating traditional methods with advanced machine learning techniques. We studied 181 patients who underwent fine-needle aspiration (FNA) biopsy, each contributing one nodule, resulting in a total of 181 nodules for our analysis. Data collection included sex, age, and ultrasound imaging, which incorporated elastography. Features extracted from these images included Thyroid Imaging Reporting and Data System (TIRADS) scores, elastography parameters, and radiomic features. The pathological results based on the FNA biopsy, provided by the pathologists, served as our gold standard for nodule classification. Our methodology, termed ELTIRADS, combines these features with interpretable machine learning techniques. Performance evaluation showed that a Support Vector Machine (SVM) classifier using TIRADS, elastography data, and radiomic features achieved high accuracy (0.92), with sensitivity (0.89), specificity (0.94), precision (0.89), and F1 score (0.89). To enhance interpretability, we used hierarchical clustering, shapley additive explanations (SHAP), and partial dependence plots (PDP). This combined approach holds promise for enhancing the accuracy of thyroid nodule malignancy detection, thereby contributing to advancements in personalized and precision medicine in the field of thyroid cancer research.
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$a Gohari, Zahra $u Department of Radiology, Tehran University of Medical Science, Tehran, Iran
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$a Aram, Sadaf $u Department of Biomedical Engineering, Tarbiat Modares University, Tehran, Iran
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$a Mohammadzadeh, Ali $u Department of Radiology, Iran University of Medical Sciences, Tehran, Iran
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$a Firouznia, Sina $u Second Faculty of Medicine, Charles University, Prague, Czech Republic
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$a Shakiba, Madjid $u Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Iran
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$a Moradian, Sadegh $u Department of Radiology, Kermanshah University of Medical Sciences, Kermanshah, Iran
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$a Ahmadi, Maryam $u Department of Biomedical Engineering, Tarbiat Modares University, Tehran, Iran
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$a Almasi, Kazhal $u Department of Biomedical Engineering, Tarbiat Modares University, Tehran, Iran
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$a Issaiy, Mahbod $u Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Iran
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$a Javadi, Sheida $u Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Iran
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$a Davoodi, Mohammad $u Department of Radiology, Tehran University of Medical Science, Tehran, Iran. mohammaddavoodi47@yahoo.com
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