Autoimmune pancreatitis (AIP) is a unique subtype of chronic pancreatitis, which shares many clinical presentations with pancreatic ductal adenocarcinoma (PDA). The misdiagnosis of AIP often leads to unnecessary pancreatic resection. 18F-FDG positron emission tomography/ computed tomography (PET/CT) could provide comprehensive information on the morphology, density, and functional metabolism of the pancreas at the same time. It has been proved to be a promising modality for noninvasive differentiation between AIP and PDA. However, there is a lack of clinical analysis of PET/CT image texture features. Difficulty still remains in differentiating AIP and PDA based on commonly used diagnostic methods. Therefore, this paper studied the differentiation of AIP and PDA based on multi-modality texture features. We utilized multiple feature extraction algorithms to extract the texture features from CT and PET images at first. Then, the Fisher criterion and sequence forward floating selection algorithm (SFFS) combined with support vector machine (SVM) was employed to select the optimal multi-modality feature subset. Finally, the SVM classifier was used to differentiate AIP from PDA. The results prove that texture analysis of lesions helps to achieve accurate differentiation of AIP and PDA.
The emergence of single-cell sequencing technology enables people to observe cells with unprecedented precision. However, it is difficult to capture the information on all cells and genes in one single-cell RNA sequencing (scRNA-seq) experiment. Single-cell data of a single modality cannot explain cell state and system changes in detail. The integrative analysis of single-cell data aims to address these two types of problems. Integrating multiple scRNA-seq data can collect complete cell types and provide a powerful boost for the construction of cell atlases. Integrating single-cell multimodal data can be used to study the causal relationship and gene regulation mechanism across modalities. The development and application of data integration methods helps fully explore the richness and relevance of single-cell data and discover meaningful biological changes. Based on this, this article reviews the basic principles, methods and applications of multiple scRNA-seq data integration and single-cell multimodal data integration. Moreover, the advantages and disadvantages of existing methods are discussed. Finally, the future development is prospected.