Multimodal radiomics model with triple -timepoint contrast-enhanced ultrasound for precise diagnosis of C-TIRADS 4 thyroid nodules
ObjectiveThis study aims to construct a multimodal radiomics model based on contrast-enhanced ultrasound (CEUS) radiomic features, combined with conventional ultrasonography (US) images and clinical data, to evaluate its diagnostic efficacy in differentiating benign and malignant thyroid nodules (TN...
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| Format: | Article |
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Endocrinology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2025.1639017/full |
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| author | Linlin Shao Lili Zhang Lifang Liu Fangfang Sun Hongyu Li Tongfeng Liu Feng Hu Lirong Zhao |
| author_facet | Linlin Shao Lili Zhang Lifang Liu Fangfang Sun Hongyu Li Tongfeng Liu Feng Hu Lirong Zhao |
| author_sort | Linlin Shao |
| collection | DOAJ |
| description | ObjectiveThis study aims to construct a multimodal radiomics model based on contrast-enhanced ultrasound (CEUS) radiomic features, combined with conventional ultrasonography (US) images and clinical data, to evaluate its diagnostic efficacy in differentiating benign and malignant thyroid nodules (TNs) classified as C-TIRADS 4, and to assess the clinical application value of the model.MethodsThis retrospective study enrolled 135 patients with C-TIRADS 4 thyroid nodules who underwent concurrent US and CEUS before FNA/surgery. From each case, one US image and three CEUS key frames (2s post-perfusion, peak enhancement, 2s post-peak) were selected. Patients were randomly split into training (n=108) and test (n=27) cohorts (8:2 ratio). ROIs were manually delineated (3D-Slicer), with radiomics features extracted (PyRadiomics) and selected via mRMR and LASSO. Six CEUS radiomics-based machine learning models (KNN, SVM, RF, XGBoost, LightGBM, SGD) were developed and evaluated using AUC, accuracy, sensitivity, specificity, and F1-score. The optimal classifier was used to build US-only, US+CEUS, and clinical+US+CEUS models. Statistical comparisons employed DeLong tests, calibration curves, and DCA.ResultsThe CEUS radiomics model demonstrated favorable diagnostic performance in differentiating benign and malignant C-TIRADS 4 thyroid nodules, with sensitivity, specificity, and accuracy of 0.875, 0.769, and 0.833, respectively. When CEUS radiomic features were combined with US features, the diagnostic performance of the CEUS radiomics model was comparable to that of the US+CEUS radiomics model (AUC: 0.813 vs. 0.829, P=0.005). Furthermore, the multimodal radiomics model integrating clinical data (clinical+US+CEUS radiomics model) achieved significantly improved diagnostic efficacy, with an AUC of 0.967, along with accuracy, sensitivity, specificity, and F1-score values of 0.815, 0.823, 0.792, and 0.884, respectively.ConclusionOur study developed a high-performance multimodal diagnostic model through the innovative integration of radiomic features from three critical CEUS timepoints combined with conventional ultrasound and clinical data, establishing a novel decision-support tool for accurate noninvasive classification of C-TIRADS 4 thyroid nodules. The model’s superior diagnostic performance (AUC 0.967) demonstrates the transformative potential of multimodal integration in overcoming single-modality limitations and enhancing clinical decision-making, positioning this approach as a promising solution to mitigate unnecessary diagnostic procedures and overtreatment. |
| format | Article |
| id | doaj-art-6b51f927d4db415da50b022ecbfd9b9f |
| institution | Kabale University |
| issn | 1664-2392 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Endocrinology |
| spelling | doaj-art-6b51f927d4db415da50b022ecbfd9b9f2025-08-20T03:44:11ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922025-08-011610.3389/fendo.2025.16390171639017Multimodal radiomics model with triple -timepoint contrast-enhanced ultrasound for precise diagnosis of C-TIRADS 4 thyroid nodulesLinlin ShaoLili ZhangLifang LiuFangfang SunHongyu LiTongfeng LiuFeng HuLirong ZhaoObjectiveThis study aims to construct a multimodal radiomics model based on contrast-enhanced ultrasound (CEUS) radiomic features, combined with conventional ultrasonography (US) images and clinical data, to evaluate its diagnostic efficacy in differentiating benign and malignant thyroid nodules (TNs) classified as C-TIRADS 4, and to assess the clinical application value of the model.MethodsThis retrospective study enrolled 135 patients with C-TIRADS 4 thyroid nodules who underwent concurrent US and CEUS before FNA/surgery. From each case, one US image and three CEUS key frames (2s post-perfusion, peak enhancement, 2s post-peak) were selected. Patients were randomly split into training (n=108) and test (n=27) cohorts (8:2 ratio). ROIs were manually delineated (3D-Slicer), with radiomics features extracted (PyRadiomics) and selected via mRMR and LASSO. Six CEUS radiomics-based machine learning models (KNN, SVM, RF, XGBoost, LightGBM, SGD) were developed and evaluated using AUC, accuracy, sensitivity, specificity, and F1-score. The optimal classifier was used to build US-only, US+CEUS, and clinical+US+CEUS models. Statistical comparisons employed DeLong tests, calibration curves, and DCA.ResultsThe CEUS radiomics model demonstrated favorable diagnostic performance in differentiating benign and malignant C-TIRADS 4 thyroid nodules, with sensitivity, specificity, and accuracy of 0.875, 0.769, and 0.833, respectively. When CEUS radiomic features were combined with US features, the diagnostic performance of the CEUS radiomics model was comparable to that of the US+CEUS radiomics model (AUC: 0.813 vs. 0.829, P=0.005). Furthermore, the multimodal radiomics model integrating clinical data (clinical+US+CEUS radiomics model) achieved significantly improved diagnostic efficacy, with an AUC of 0.967, along with accuracy, sensitivity, specificity, and F1-score values of 0.815, 0.823, 0.792, and 0.884, respectively.ConclusionOur study developed a high-performance multimodal diagnostic model through the innovative integration of radiomic features from three critical CEUS timepoints combined with conventional ultrasound and clinical data, establishing a novel decision-support tool for accurate noninvasive classification of C-TIRADS 4 thyroid nodules. The model’s superior diagnostic performance (AUC 0.967) demonstrates the transformative potential of multimodal integration in overcoming single-modality limitations and enhancing clinical decision-making, positioning this approach as a promising solution to mitigate unnecessary diagnostic procedures and overtreatment.https://www.frontiersin.org/articles/10.3389/fendo.2025.1639017/fullthyroid nodulescontrast-enhanced ultrasoundradiomics featuresmachine learningC-TIRADS |
| spellingShingle | Linlin Shao Lili Zhang Lifang Liu Fangfang Sun Hongyu Li Tongfeng Liu Feng Hu Lirong Zhao Multimodal radiomics model with triple -timepoint contrast-enhanced ultrasound for precise diagnosis of C-TIRADS 4 thyroid nodules Frontiers in Endocrinology thyroid nodules contrast-enhanced ultrasound radiomics features machine learning C-TIRADS |
| title | Multimodal radiomics model with triple -timepoint contrast-enhanced ultrasound for precise diagnosis of C-TIRADS 4 thyroid nodules |
| title_full | Multimodal radiomics model with triple -timepoint contrast-enhanced ultrasound for precise diagnosis of C-TIRADS 4 thyroid nodules |
| title_fullStr | Multimodal radiomics model with triple -timepoint contrast-enhanced ultrasound for precise diagnosis of C-TIRADS 4 thyroid nodules |
| title_full_unstemmed | Multimodal radiomics model with triple -timepoint contrast-enhanced ultrasound for precise diagnosis of C-TIRADS 4 thyroid nodules |
| title_short | Multimodal radiomics model with triple -timepoint contrast-enhanced ultrasound for precise diagnosis of C-TIRADS 4 thyroid nodules |
| title_sort | multimodal radiomics model with triple timepoint contrast enhanced ultrasound for precise diagnosis of c tirads 4 thyroid nodules |
| topic | thyroid nodules contrast-enhanced ultrasound radiomics features machine learning C-TIRADS |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2025.1639017/full |
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