Prediction Research of TACE Treatment Response Based on Multimodal Data Fusion

Transcatheter arterial chemoembolization TACE is the preferred non-surgical treatment for HCC patients, but up to 60 of HCC patients do not benefit from TACE treatment. Therefore, accurately and efficiently predicting the treatment response after TACE in HCC patients is of great significance for treatment planning. To address this, a predictive model based on the integration of clinical data and preoperative CT imaging was proposed. This model first used a convolutional neural network to extract microscopic structural features from CT images, then inputed the extracted features into a Long Short-Term Memory network to obtain the global feature vector for each CT slice. Next, a deep neural network was used to extract features from the clinical data. Finally, the features were fused using an asymmetric cross-attention mechanism, followed by classification using a feedforward neural network. A retrospective study was conducted on 181 HCC patients who underwent TACE treatment at a hospital in Zhejiang Province from January 2018 to April 2022. The AUC, precision, accuracy, and recall of the prediction model are 0.85, 0.86, 0.88, and 0.87, respectively. The experimental results demonstrate that the model Cnn-Lstm-Dnn-Cross-Attention, CLDCA outperforms the comparison models, providing an effective solution for predicting the post-TACE treatment response in HCC patients.