ORIGINAL RESEARCH article
Front. Oncol.
Sec. Cancer Imaging and Image-directed Interventions
This article is part of the Research TopicHarnessing Machine Learning for Enhanced Biomedical Diagnosis and Early Disease Detection: Bridging Data Science and HealthcareView all 7 articles
Ultrasound Radiomics-Based Machine Learning Models for Risk Stratification of Follicular Thyroid Tumors
Provisionally accepted
- 1The First Affiliated Hospital With Nanjing Medical University, Nanjing, China
- 2The Shanghai Tenth Clinical Medical College with Nanjing Medical University, Shanghai, China
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Background: Follicular thyroid carcinoma (FTC) is the second most common malignant thyroid tumor. Preoperative differentiation among follicular thyroid adenoma (FA), follicular tumor of uncertain malignant potential (FT-UMP), and FTC remains challenging using conventional ultrasound and fine-needle aspiration. This study aims to develop a machine learning model utilizing ultrasound radiomic features to improve risk stratification of follicular thyroid tumors. Methods: A total of 277 patients with histopathologically confirmed follicular tumors (163 FA, 63 FT-UMP, 51 FTC) were included. Clinical and ultrasound features, along with radiomic features from intratumoral and peritumoral regions, were extracted from preoperative ultrasound images. Three machine learning models—logistic regression (LR), support vector machine (SVM), and random forest (RF)—were trained to construct four models: clinical-ultrasound (U), clinical-ultrasound with intratumoral radiomics (UI), clinical-ultrasound with peritumoral radiomics (UP), and clinical-ultrasound with combined intratumoral and peritumoral radiomics (UIP). Results: The RF-based clinical-ultrasound model demonstrated the highest accuracy (test: 0.643) but exhibited significant overfitting in radiomics-based models. The SVM model showed moderate performance. The LR model in the UP and UIP models delivered stable performance, achieving the highest test accuracy of 0.643. Specifically, the UP model showed improved micro-AUC, specificity, negative predictive value (NPV), and F1 score. The LR model exhibited high sensitivity but low specificity for benign nodules, and high specificity but low sensitivity for malignant nodules. All models performed poorly in identifying FT-UMP nodules. Conclusion: Integrating peritumoral radiomic features with clinical-ultrasound features using logistic regression enhances the differentiation between benign and malignant follicular thyroid tumors.
Keywords: Follicular thyroid tumors, machine learning, peritumoral radiomics, risk stratification, ultrasound
Received: 17 Sep 2025; Accepted: 26 Nov 2025.
Copyright: © 2025 Yuan, Wang, Deng, Cao and Yu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Fei Yu
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