Zheng Yao, Huang Dong, Feng Yuefei, Hao Xiaoshuo, He Yutao, Liu Yang
School of Biomedical Engineering, Air Force Medical University, No. 169 Changle West Road, Xi'an 710032, China.
Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, No. 169 Changle West Road, Xi'an 710032, China.
Bioengineering (Basel). 2023 Jul 26;10(8):887. doi: 10.3390/bioengineering10080887.
Deep networks have shown strong performance in glioma grading; however, interpreting their decisions remains challenging due to glioma heterogeneity. To address these challenges, the proposed solution is the Causal Segmentation Framework (CSF). This framework aims to accurately predict high- and low-grade gliomas while simultaneously highlighting key subregions. Our framework utilizes a shrinkage segmentation method to identify subregions containing essential decision information. Moreover, we introduce a glioma grading module that combines deep learning and traditional approaches for precise grading. Our proposed model achieves the best performance among all models, with an AUC of 96.14%, an F1 score of 93.74%, an accuracy of 91.04%, a sensitivity of 91.83%, and a specificity of 88.88%. Additionally, our model exhibits efficient resource utilization, completing predictions within 2.31s and occupying only 0.12 GB of memory during the test phase. Furthermore, our approach provides clear and specific visualizations of key subregions, surpassing other methods in terms of interpretability. In conclusion, the Causal Segmentation Framework (CSF) demonstrates its effectiveness at accurately predicting glioma grades and identifying key subregions. The inclusion of causality in the CSF model enhances the reliability and accuracy of preoperative decision-making for gliomas. The interpretable results provided by the CSF model can assist clinicians in their assessment and treatment planning.
深度网络在胶质瘤分级中表现出强大的性能；然而，由于胶质瘤的异质性，解释它们的决策仍然具有挑战性。为了应对这些挑战，提出的解决方案是因果分割框架（CSF）。该框架旨在准确预测高级别和低级别胶质瘤，同时突出关键子区域。我们的框架利用一种收缩分割方法来识别包含关键决策信息的子区域。此外，我们引入了一个胶质瘤分级模块，该模块结合了深度学习和传统方法进行精确分级。我们提出的模型在所有模型中取得了最佳性能，曲线下面积（AUC）为96.14%，F1分数为93.74%，准确率为91.04%，灵敏度为91.83%，特异性为88.88%。此外，我们的模型展现出高效的资源利用，在测试阶段2.31秒内完成预测，仅占用0.12GB内存。此外，我们的方法提供了关键子区域清晰且具体的可视化，在可解释性方面超越了其他方法。总之，因果分割框架（CSF）在准确预测胶质瘤分级和识别关键子区域方面证明了其有效性。CSF模型中纳入因果关系提高了胶质瘤术前决策的可靠性和准确性。CSF模型提供的可解释结果可以帮助临床医生进行评估和治疗规划。
