A multi-view prognostic model for diffuse large B-cell lymphoma based on kernel canonical correlation analysis and support vector machine

作者：Yanhong Luo, Yanping Li, Zhenhuan Yang, Yanbo Zhang, Hongmei Yu, Zhiqiang Zhao, Kai Yu, Yujiao Guo, Xueman Wang, Na Yang, Tao Zhang, Tingting Zheng, Jie Zhou · 发表于：BMC Cancer · 年份：2024 · DOI：10.1186/s12885-024-13266-7 · 被引用次数：4 · 研究领域：Lymphoma Diagnosis and Treatment、Radiomics and Machine Learning in Medical Imaging、CNS Lymphoma Diagnosis and Treatment

BACKGROUND AND OBJECTIVE: Positron emission tomography/computed tomography (PET/CT) is recommended as the standard imaging modality for diffuse large B-cell lymphoma (DLBCL) staging. However, many studies have neglected the role of patients' prognostic factors with respect to imaging PET/CT of quantitative features. In this paper, a multi-view learning (MVL) model is established to make full use of both clinical and imaging data to predict the prognosis of DLBCL patients and thereby assist doctors in decision-making. METHODS: Feature engineering, including feature extraction, feature screening by recursive feature elimination, and dimensionality reduction by principal component analysis, are successively performed on the clinical data and imaging data of the research subjects to obtain the study data. After dividing the data into training and test sets, an instance weighting method is applied to the training data. Subsequently, kernel mapping is performed on the imaging features and clinical features separately, and this kernel mapping is processed in the new kernel feature space using kernel canonical correlation analysis (KCCA). Lastly, model training is performed on the obtained common kernel subspace using a support vector machine (SVM). The final overall model, named SVM-2view-KCCA (SVM-2 K), was compared with three other multi-view models (Ensemble-SVM, Multi-view maximum entropy discrimination, and canonical correlation analysis). The performance of the model was evalu...