What question did this study set out to answer?

The study aims to evaluate the predictive effectiveness of deep learning, radiomics, and their combination on gross extrathyroidal extension in papillary thyroid carcinoma.

March 13, 2026Open Access

Innovative fusion models: elevating preoperative gross ETE prediction in thyroid cancer patients

TPTing PanZhejiang Provincial People's Hospital FWFan WuSecond Affiliated Hospital of Guangzhou Medical University JCJuanjuan CaiZhejiang Provincial People's Hospital

Key Points

The study aims to evaluate the predictive effectiveness of deep learning, radiomics, and their combination on gross extrathyroidal extension in papillary thyroid carcinoma.
Conducted a retrospective analysis of 4,542 papillary thyroid carcinoma patients from three hospitals.
Developed models using ultrasound images and clinical data, focusing on tumor expansion regions.
Used metrics such as AUC, sensitivity, and specificity to assess model performance.
Implemented SHAP analysis to evaluate feature importance and Grad-CAM for model interpretability.
The radiomics model achieved an AUC of 0.796; the ResNet101 deep learning model achieved 0.832.
The DLR model incorporating 15 pixel features reached an AUC of 0.849, with 0.857 accuracy and 0.888 specificity.
Deep learning features were identified as primary predictors, and Grad-CAM confirmed spatial correlations with histopathological patterns.

Abstract

Abstract The intratumoral and peritumoral architectural heterogeneities of papillary thyroid carcinoma (PTC) are important in preoperative prediction of gross extrathyroidal extension (Gross ETE). This study systematically evaluated and compared the predictive efficacies of deep learning, radiomics, and their combined approach (Deep Learning-Radiomics, DLR) in predicting Gross ETE in PTC patients using ultrasound imaging. This retrospective study from three hospitals, between 01/01/2018, and 12/31/2022, included 4,542 PTC patients, divided into training ( n = 3,179) and testing ( n = 1,363) sets in a 7:3 ratio. Preoperative ultrasound images and clinical data were collected to develop radiomics and deep learning models based on different tumor expansion regions (5/10/15/20 pixels). A nomogram prediction model was developed by integrating multi-regional radiomics features and key clinical parameters. Model performance was assessed using metrics such as the area under the curve (AUC), sensitivity, and specificity. Feature importance was evaluated using SHapley Additive exPlanations (SHAP) analysis, and model interpretability was analyzed with Gradient-weighted Class Activation Mapping (Grad-CAM). In the test cohort, the radiomics model with 15 pixel expansion (AUC: 0.796) and the ResNet101 deep learning model (AUC: 0.832) showed optimal performance. The DLR model incorporating 15 pixel peritumoral features (DLRexpand15) combined with clinical parameters achieved superior predictive performance (AUC: 0.849, accuracy: 0.857, and specificity: 0.888). SHAP analysis identified deep learning features as the primary predictors in the fusion model, while Grad-CAM visualization confirmed spatial concordances between model-activated regions and histopathological invasion patterns. The DLRexpand15-based nomogram integrating clinical indicators provided an effective tool for preoperative prediction of Gross ETE in PTC patients. Strategic incorporation of peritumoral information significantly enhanced the predictive capacity of both radiomics and deep learning models. This multimodal approach provided clinically useful insights for surgical planning and risk stratification.

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