Frontiers in endocrinology · 2026
Multimodal MRI radiomics-clinical fusion model predicts intravenous glucocorticoid response in thyroid eye disease.
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Zhou Yanhu, Jia Fei, Zhao Xuelian, Ma Xiaojin, Chang Tao, Yao Shunyu, Che Kuanyu, Zhang Jing
Аннотация
BACKGROUND: This study aimed to develop a multimodal MRI radiomics-clinical fusion model for predicting intravenous glucocorticoid (IVGC) treatment response in patients with thyroid eye disease (TED).
METHODS: In this retrospective multicenter study, 108 TED patients (78 responders, 30 non-responders) from two institutions (January 2020-December 2024) were included, and treatment response was assessed at 12 weeks after completion of therapy. Patients were randomly split into training and test sets (8:2). All patients received a standardized intravenous methylprednisolone regimen (total dose 4.5 g over 12 weeks) according to EUGOGO recommendations. Univariate logistic regression was used to identify clinical predictors associated with response. Radiomics features and deep transfer learning (DTL) features were extracted from pretreatment T1-weighted imaging (T1WI) and fat-suppressed T2-weighted imaging (T2WI-FS). Feature selection followed a three-step pipeline (t-test, Pearson correlation filtering, and LASSO with 10-fold cross-validation), and a radiomics-deep learning fused (RDL) model was built. A combined model integrating the RDL score with independent clinical predictors was constructed and visualized as a nomogram. Model performance was evaluated using ROC/AUC, calibration curves, and decision curve analysis (DCA), and AUCs were compared using the DeLong test.
RESULTS: Disease duration and Clinical Activity Score (CAS) were independent predictors of IVGC response (P < 0.05). The RDL model outperformed radiomics-only models, achieving AUCs of 0.894 (95% CI: 0.804-0.984) in the training set and 0.804 (95% CI: 0.595-1.000) in the test set. The combined model demonstrated further improved performance, with training and test set AUCs of 0.916 (0.837-0.994) and 0.862 (0.702-1.000), respectively, along with better calibration and higher net clinical benefit. The DeLong test showed that the AUC of the combined model was significantly higher than that of the clinical model (P = 0.032), but did not differ significantly from that of the RDL model (P = 0.161).
CONCLUSION: The multimodal MRI radiomics-clinical fusion model accurately predicts IVGC treatment response in TED, offering a non-invasive tool for personalized therapy planning.
METHODS: In this retrospective multicenter study, 108 TED patients (78 responders, 30 non-responders) from two institutions (January 2020-December 2024) were included, and treatment response was assessed at 12 weeks after completion of therapy. Patients were randomly split into training and test sets (8:2). All patients received a standardized intravenous methylprednisolone regimen (total dose 4.5 g over 12 weeks) according to EUGOGO recommendations. Univariate logistic regression was used to identify clinical predictors associated with response. Radiomics features and deep transfer learning (DTL) features were extracted from pretreatment T1-weighted imaging (T1WI) and fat-suppressed T2-weighted imaging (T2WI-FS). Feature selection followed a three-step pipeline (t-test, Pearson correlation filtering, and LASSO with 10-fold cross-validation), and a radiomics-deep learning fused (RDL) model was built. A combined model integrating the RDL score with independent clinical predictors was constructed and visualized as a nomogram. Model performance was evaluated using ROC/AUC, calibration curves, and decision curve analysis (DCA), and AUCs were compared using the DeLong test.
RESULTS: Disease duration and Clinical Activity Score (CAS) were independent predictors of IVGC response (P < 0.05). The RDL model outperformed radiomics-only models, achieving AUCs of 0.894 (95% CI: 0.804-0.984) in the training set and 0.804 (95% CI: 0.595-1.000) in the test set. The combined model demonstrated further improved performance, with training and test set AUCs of 0.916 (0.837-0.994) and 0.862 (0.702-1.000), respectively, along with better calibration and higher net clinical benefit. The DeLong test showed that the AUC of the combined model was significantly higher than that of the clinical model (P = 0.032), but did not differ significantly from that of the RDL model (P = 0.161).
CONCLUSION: The multimodal MRI radiomics-clinical fusion model accurately predicts IVGC treatment response in TED, offering a non-invasive tool for personalized therapy planning.