Molecular imaging and biology · 2026
LLM-Enhanced Multimodal Fusion of SPECT Radiomics and Clinical Data for Predicting 131I Therapeutic Response in Differentiated Thyroid Cancer.
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Wang Hangyu, Chen Erhao, Pan Liqin, Wu Xiaofeng, Zhang Songyi, Lin Li, Sun Ying, Lu Lijun, Ouyang Wei
Аннотация
BACKGROUND: Patients with differentiated thyroid cancer (DTC) treated with radioactive iodine (RAI) remain at risk of adverse outcomes. Early prediction of RAI efficacy is critical for optimizing clinical management.
PURPOSE: To develop a model integrating Single Photon Emission Computed Tomography (SPECT) radiomic features and clinical variables for predicting the therapeutic efficacy of 131I treatment in patients with DTC.
MATERIALS AND METHOD: The SPECT images and clinical data from 311 patients were included in this study. A total of 1,688 radiomic features were extracted from SPECT images. We investigated the diagnostic performance of 30 cross-combined models. Radiomic features were then combined with clinical parameters using early-fusion and late-fusion strategies. To further enhance predictive performance, an input sequence reflecting the selected features and the prediction task was designed and fed into a selected Large Language Model (LLM), which was fine-tuned using low-rank adaptation to optimize fused feature representations. Model performance was evaluated using accuracy (Acc), sensitivity (Sens), positive predictive value (PPV), F1-score, and the area under the curve (AUC).
RESULTS: The Relief-F + random forest radiomic model achieved the highest AUC (0.69). Multimodal models combining radiomics with clinical features outperformed clinical-only models, demonstrating the added predictive value of radiomics. The final LLM-enhanced multimodal model achieved the best performance, with an AUC of 0.85 and notable improvements in sensitivity and F1-score.
CONCLUSIONS: The proposed multimodal framework provides accurate early prediction of RAI therapeutic response in DTC and holds promise for improving individualized treatment planning and follow-up strategies.
PURPOSE: To develop a model integrating Single Photon Emission Computed Tomography (SPECT) radiomic features and clinical variables for predicting the therapeutic efficacy of 131I treatment in patients with DTC.
MATERIALS AND METHOD: The SPECT images and clinical data from 311 patients were included in this study. A total of 1,688 radiomic features were extracted from SPECT images. We investigated the diagnostic performance of 30 cross-combined models. Radiomic features were then combined with clinical parameters using early-fusion and late-fusion strategies. To further enhance predictive performance, an input sequence reflecting the selected features and the prediction task was designed and fed into a selected Large Language Model (LLM), which was fine-tuned using low-rank adaptation to optimize fused feature representations. Model performance was evaluated using accuracy (Acc), sensitivity (Sens), positive predictive value (PPV), F1-score, and the area under the curve (AUC).
RESULTS: The Relief-F + random forest radiomic model achieved the highest AUC (0.69). Multimodal models combining radiomics with clinical features outperformed clinical-only models, demonstrating the added predictive value of radiomics. The final LLM-enhanced multimodal model achieved the best performance, with an AUC of 0.85 and notable improvements in sensitivity and F1-score.
CONCLUSIONS: The proposed multimodal framework provides accurate early prediction of RAI therapeutic response in DTC and holds promise for improving individualized treatment planning and follow-up strategies.