Abstract / Summary
Background: There is a lack of intelligent methodologies that effectively integrate multimodal information to predict bone cement leakage (BCL) during percutaneous kyphoplasty (PKP). This study aimed to develop and validate a deep learning (DL)-based multimodal fusion model that incorporates preoperative CT, MRI, and clinical variables to predict BCL subtypes during PKP.
Methods: This study included a retrospective internal dataset for model training and validation, a prospective internal dataset, and an external dataset for independent testing. The fusion model incorporated preoperative spinal CT, MRI, and structured clinical baseline data within a two-stage framework. The first stage consisted of target vertebra localization based on vertebral segmentation. The second stage comprised a classification module implemented using a multibranch 3D ResNet-50 network. Performance was compared with image-only models, single-modality models, and spine surgeons using the area under the receiver operating characteristic curve (AUC), the area under the precision-recall curve, and other metrics.
Results: The multimodal DL model achieved AUC values ranging from 0.795 to 0.861 in the internal test set and from 0.767 to 0.848 in the external test set for predicting BCL subtypes. Type III leakage demonstrated the highest predictive performance (internal AUC, 0.861; external AUC, 0.848). Overall, the fusion model achieved the highest AUC values and showed superior accuracy and agreement compared with spine surgeons, particularly for Type I (κ: internal, 0.439 vs. 0.030-0.075; external, 0.298 vs. 0.019-0.066) and Type IV leakage (κ: internal, 0.459 vs. 0.133-0.186; external, 0.545 vs. 0.002-0.095).
Conclusion: A two-stage multimodal fusion DL framework enables accurate, reliable, and promisingly generalizable prediction of BCL subtypes in PKP, outperforming spine surgeons and supporting individualized preoperative decision-making.