College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, People's Republic of China.
Department of Physiology, Shanxi Medical University, Taiyuan 030051, People's Republic of China.
Phys Med Biol. 2023 Mar 23;68(7). doi: 10.1088/1361-6560/acc168.
. Recently, imaging genomics has increasingly shown great potential for predicting postoperative recurrence of lung cancer patients. However, prediction methods based on imaging genomics have some disadvantages such as small sample size, high-dimensional information redundancy and poor multimodal fusion efficiency. This study aim to develop a new fusion model to overcome these challenges.. In this study, a dynamic adaptive deep fusion network (DADFN) model based on imaging genomics is proposed for predicting recurrence of lung cancer. In this model, the 3D spiral transformation is used to augment the dataset, which better retains the 3D spatial information of the tumor for deep feature extraction. The intersection of genes screened by LASSO, F-test and CHI-2 selection methods is used to eliminate redundant data and retain the most relevant gene features for the gene feature extraction. A dynamic adaptive fusion mechanism based on the cascade idea is proposed, and multiple different types of base classifiers are integrated in each layer, which can fully utilize the correlation and diversity between multimodal information to better fuse deep features, handcrafted features and gene features.. The experimental results show that the DADFN model achieves good performance, and its accuracy and AUC are 0.884 and 0.863, respectively. This indicates that the model is effective in predicting lung cancer recurrence.. The proposed model has the potential to help physicians to stratify the risk of lung cancer patients and can be used to identify patients who may benefit from a personalized treatment option.
. 最近，影像基因组学在预测肺癌患者术后复发方面显示出了巨大的潜力。然而，基于影像基因组学的预测方法存在一些缺点，例如样本量小、高维信息冗余和多模态融合效率差。本研究旨在开发一种新的融合模型来克服这些挑战。. 在这项研究中，提出了一种基于影像基因组学的动态自适应深度融合网络（DADFN）模型，用于预测肺癌的复发。在该模型中，采用 3D 螺旋变换来扩充数据集，这更好地保留了肿瘤的 3D 空间信息，以便进行深度特征提取。通过 LASSO、F 检验和 CHI-2 选择方法筛选出的基因交集，用于消除冗余数据并保留与基因特征提取最相关的基因特征。提出了一种基于级联思想的动态自适应融合机制，在每一层集成多个不同类型的基本分类器，可以充分利用多模态信息之间的相关性和多样性，更好地融合深度特征、手工特征和基因特征。. 实验结果表明，DADFN 模型表现出良好的性能，其准确率和 AUC 分别为 0.884 和 0.863。这表明该模型在预测肺癌复发方面是有效的。. 该模型有望帮助医生对肺癌患者进行风险分层，并可用于识别可能受益于个性化治疗方案的患者。
