Objective This study aims to develop and validate a predictive model for thyroid nodule malignancy risks using clinical and ultrasonography features and a machine learning (ML) approach. Methods This retrospective study is based on the clinical and ultrasound characteristics of 1035 thyroid nodules (845 benign and 190 malignant) to develop and validate the risk prediction model. Employing multiple logistic regression, key features were selected in developing the model. Eight ML algorithms were evaluated for predicting the risks of malignancy. Finally, the predictive ability of the best-performing algorithm was compared against American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) and American Thyroid Association (ATA) guidelines. Results Based on AUC criteria (88.3, 95% CI: 81.2–94.2), sensitivity (84.2, 95% CI: 71.1–94.7), specificity (92.3, 95% CI: 88.2–95.9), positive predictive value, (71.4, 95% CI: 60.4–83.3) and negative predictive value (96.3, 95% CI: 93.5–98.8), the XGBoost algorithm exhibited superior performance over the other ML algorithms and ACR TI-RADS and ATA. These criteria were obtained for ACR TI-RADS at 54.2%, 63.2%, 48.5%, 21.1%, and 84.8%, while for ATA, they were 44.3%, 76.3%, 27.2%, 18.4%, and 81.6%. In addition, the unnecessary fine-needle aspiration (FNA) rate with ACR TI-RADS and ATA was 43% and 63%, respectively—significantly higher than the 7% obtained with XGBoost. Conclusions This study demonstrated the capability of ML approaches in enhancing the accuracy of predicting thyroid malignancy risks as well as their potential benefits in optimizing healthcare resources by reducing unnecessary FNA rates. Using the proposed model through a web-based tool can facilitate clinical judgments in thyroid nodule management and personalized treatment. Key Points <Emphasis Type="BoldItalic">Question</Emphasis> Current risk assessment systems have limitations, with high unnecessary FNA rates compared to machine learning (ML) models. <Emphasis Type="BoldItalic">Findings</Emphasis> The XGBoost algorithm was compared to other ML algorithms, ACR TI-RADS, and ATA and demonstrated superior performance. <Emphasis Type="BoldItalic">Clinical relevance</Emphasis> This study demonstrated the capability of ML approaches in enhancing the accuracy of predicting thyroid malignancy. The proposed web-based tool to facilitate the prediction of thyroid nodule risk is available at <a href="https://aimedlab.ir/tnr">https://aimedlab.ir/tnr</a>.

Prediction of thyroid malignancy risk using clinical and ultrasonography features and a machine learning approach

期刊论文
发表日期：

2025年2月14日

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Seyed Mahdi Hosseini Sarkhosh,
Nooshin Shirzad,
Mahdieh Taghvaei,
Seyed Mohammad Tavangar,
Sara Farhat,
Hojat Ebrahiminik,
Mahboobeh Hemmatabadi,
Maryam Pourashraf,
Hossein Chegeni

摘要

Objective

This study aims to develop and validate a predictive model for thyroid nodule malignancy risks using clinical and ultrasonography features and a machine learning (ML) approach.

Methods

This retrospective study is based on the clinical and ultrasound characteristics of 1035 thyroid nodules (845 benign and 190 malignant) to develop and validate the risk prediction model. Employing multiple logistic regression, key features were selected in developing the model. Eight ML algorithms were evaluated for predicting the risks of malignancy. Finally, the predictive ability of the best-performing algorithm was compared against American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) and American Thyroid Association (ATA) guidelines.

Results

Based on AUC criteria (88.3, 95% CI: 81.2–94.2), sensitivity (84.2, 95% CI: 71.1–94.7), specificity (92.3, 95% CI: 88.2–95.9), positive predictive value, (71.4, 95% CI: 60.4–83.3) and negative predictive value (96.3, 95% CI: 93.5–98.8), the XGBoost algorithm exhibited superior performance over the other ML algorithms and ACR TI-RADS and ATA. These criteria were obtained for ACR TI-RADS at 54.2%, 63.2%, 48.5%, 21.1%, and 84.8%, while for ATA, they were 44.3%, 76.3%, 27.2%, 18.4%, and 81.6%. In addition, the unnecessary fine-needle aspiration (FNA) rate with ACR TI-RADS and ATA was 43% and 63%, respectively—significantly higher than the 7% obtained with XGBoost.

Conclusions

This study demonstrated the capability of ML approaches in enhancing the accuracy of predicting thyroid malignancy risks as well as their potential benefits in optimizing healthcare resources by reducing unnecessary FNA rates. Using the proposed model through a web-based tool can facilitate clinical judgments in thyroid nodule management and personalized treatment.

Key Points

Question Current risk assessment systems have limitations, with high unnecessary FNA rates compared to machine learning (ML) models.

Findings The XGBoost algorithm was compared to other ML algorithms, ACR TI-RADS, and ATA and demonstrated superior performance.

Clinical relevance This study demonstrated the capability of ML approaches in enhancing the accuracy of predicting thyroid malignancy. The proposed web-based tool to facilitate the prediction of thyroid nodule risk is available at https://aimedlab.ir/tnr.