IEEE Trans Biomed Eng. 2020 Oct;67(10):2735-2744. doi: 10.1109/TBME.2020.2969839. Epub 2020 Jan 27.
Feature dimensionality reduction plays an important role in radiomic studies with a large number of features. However, conventional radiomic approaches may suffer from noise, and feature dimensionality reduction techniques are not equipped to utilize latent supervision information of patient data under study, such as differences in patients, to learn discriminative low dimensional representations. To achieve robustness to noise and feature dimensionality reduction with improved discriminative power, we develop a robust collaborative clustering method to simultaneously cluster patients and radiomic features into distinct groups respectively under adaptive sparse regularization. Our method is built upon matrix tri-factorization enhanced by adaptive sparsity regularization for simultaneous feature dimensionality reduction and denoising. Particularly, latent grouping information of patients with distinct radiomic features is learned and utilized as supervision information to guide the feature dimensionality reduction, and noise in radiomic features is adaptively isolated in a Bayesian framework under a general assumption of Laplacian distributions of transform-domain coefficients. Experiments on synthetic data have demonstrated the effectiveness of the proposed approach in data clustering, and evaluation results on an FDG-PET/CT dataset of rectal cancer patients have demonstrated that the proposed method outperforms alternative methods in terms of both patient stratification and prediction of patient clinical outcomes.
特征降维在具有大量特征的放射组学研究中起着重要作用。然而，传统的放射组学方法可能会受到噪声的影响，并且特征降维技术无法利用研究中患者数据的潜在监督信息，例如患者之间的差异，来学习有区别的低维表示。为了在实现噪声鲁棒性和特征降维的同时提高判别能力，我们开发了一种鲁棒的协同聚类方法，在自适应稀疏正则化下分别将患者和放射组学特征聚类到不同的组中。我们的方法基于矩阵三因子分解，通过自适应稀疏正则化增强，同时进行特征降维和去噪。特别是，学习具有不同放射组学特征的患者的潜在分组信息，并将其用作监督信息来指导特征降维，并且在拉普拉斯分布的变换域系数的一般假设下，在贝叶斯框架中自适应地隔离放射组学特征中的噪声。在合成数据上的实验表明了所提出方法在数据聚类方面的有效性，并且在直肠癌患者的 FDG-PET/CT 数据集上的评估结果表明，与其他方法相比，该方法在患者分层和预测患者临床结局方面都具有更好的性能。
