An optimal ablation time prediction model based on minimizing the relapse risk. (November 2021) - Weng, Xutao; Song, Hong; Fu, Tianyu; Gao, Yuanjin; Fan, Jingfan; Ai, Danni; Lin, Yucong; Yang, Jian
Highlights: We propose a method to correct the noisy label based on the strong supervision signal and use the corrected label to guide the supervised learning. The proposed model can predict the ablation time closer to that in the non-relapse after microwave ablation for liver cancer, providing a helpful reference for physicians. Abstract: Objective: Percutaneous microwave ablation is an essential and safe method for the treatment of liver cancer. As one therapeutic dose, ablation time is crucial to the treatment effect determined by the physicians. However, due to the different experiences of physicians and the significant individual differences of patients, the final treatment effect is also different, which makes it difficult for the ablation time recorded in the electronic health records (EHRs) to follow the same pattern. To solve this problem, we propose a data mining method based on historical treatment data recorded in EHR, which uses a robust relapse risk as strong supervision to correct the ablation time. The prediction results of this method are closer to the situation of patients without relapse, which can provide physicians with reference. Methods: In the proposed method, we introduce the optimization method to iteratively minimize the postoperative relapse risk and utilize gradient propagation between the risk and ablation time during iteration to correct the latter. We also apply a self-attention mechanism to find the global dependencies between each feature in EHR to improve the final prediction performance of the model. Results: Comparative experimental results show that compared with other baseline model, the proposed model achieves better performance on R-square, MAE, and MSE metric. The results of ablation experiments show that the integration of label correction and self-attention mechanism can improve the model performance. Conclusions: We using relapse risk as strong supervision related to the ablation time can effectively correct the deviation of the ablation time as weak supervision. The self-attention mechanism in the proposed model can significantly improve the prediction performance. Graphical abstract: Image, graphical abstract