烧蚀
特征选择
杠杆(统计)
随机森林
医学
多模光纤
计算机科学
人工智能
内科学
电信
光纤
作者
Xinyi Wang,Wentao Li,Kangwei Zhang,Jianqi Sun,Jianlong Yang,Aili Zhang,Lisa X. Xu
出处
期刊:IEEE Transactions on Biomedical Engineering
[Institute of Electrical and Electronics Engineers]
日期:2022-04-01
卷期号:69 (4): 1386-1397
被引量:3
标识
DOI:10.1109/tbme.2021.3116607
摘要
The multimode ablation of liver cancer, which uses radio-frequency heating after a pre-freezing process to treat the tumor, has shown significantly improved therapeutic effects and enhanced anti-tumor immune response. Unlike open surgery, the ablated lesions remain in the body after treatment, so it is critical to assess the immediate outcome and to monitor disease status over time. Here we propose a novel tumor progression prediction method for simultaneous postoperative evaluation and prognosis analysis.We propose to leverage the intraoperative therapeutic information extracted from thermal dose distribution. For tumors with specific sensitivity reflected in medical images, different thermal doses implicitly indicate the degree of instant damage and long-term inhibition excited under specific ablation energy. We further propose a survival analysis framework for the multimode ablation treatment. It extracts carefully designed features from clinical, preoperative, intraoperative, and postoperative data, then uses random survival forest for feature selection and deep neural networks for survival prediction.We evaluated the proposed methods using clinical data. The results show that our method outperforms the state-of-the-art survival analysis methods with a C-index of 0.855±0.090. The thermal dose information contributes significantly to the prediction accuracy by taking up 21.7% of the overall feature importance.The proposed methods have been demonstrated to be a powerful tool in tumor progression prediction of multimode ablation therapy.This kind of data-driven prognosis analysis may benefit personalized medicine and simplify the follow-up process.
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