We update the "2021 expert consensus on clinical pathway for transcatheter aortic valve replacement in China" using the Delphi method. By searching for evidence related to the clinical pathways of transcatheter aortic valve replacement (TAVR) in PubMed, CIKI, and Wanfang Database over the past four years, 35 core viewpoints were proposed in four directions: TAVR team composition and clinical evaluation, perioperative imaging assessment, surgical process, and perioperative and postoperative long-term management of patients. The specific updates include: refining the responsibilities and composition of the TAVR team, clarifying the steps and content of clinical evaluation; emphasizing the core position of CT in perioperative imaging assessment, and introducing the application of new technologies such as artificial intelligence, numerical simulation, and 3D printing; optimizing the TAVR surgical process, including anesthesia selection, access establishment, valve selection and release, and others; and proposing management strategies for special types of patients (such as emergency TAVR, simplified TAVR, pure aortic regurgitation, combined coronary heart disease and atrial fibrillation). In addition, the consensus also emphasizes the importance of postoperative follow-up and rehabilitation, and provides detailed antithrombotic and rehabilitation guidance. The update of this consensus will further promote the standardized development of TAVR technology in China and improve clinical treatment effects.
ObjectiveTo summarize the current research progress of imaging in evaluating the efficacy of neoadjuvant therapy for breast cancer, analyze the clinical application value and limitations of conventional imaging modalities, and explore the application prospects of advanced technologies such as multimodal fusion, radiomics, and artificial intelligence in efficacy prediction. MethodBy searching recent domestic and international literature on imaging evaluation of neoadjuvant therapy for breast cancer, this review summarizes traditional imaging evaluation methods and the applications of cutting-edge technologies such as multimodal fusion, radiomics, and deep learning. ResultsTraditional imaging methods play an important role in the evaluation of post-treatment efficacy, but there are inherent limitations. The morphology-based evaluation paradigm struggles to meet the clinical demand for early prediction and exhibits a significant gap compared with the histopathological gold standard. In recent years, the development of multimodal fusion, radiomics, and deep learning technologies has demonstrated promising efficacy in predicting pathologic complete response. However, most studies are single-center retrospective designs, and their external validation and interpretability still need to be strengthened. ConclusionsImaging enables both retrospective assessment and prospective prediction in evaluating the efficacy of neoadjuvant therapy for breast cancer. In the future, the establishment of standardized imaging acquisition and validation databases, the development of hybrid models that combine predictive performance with interpretability, and the promotion of the deep integration of imaging with multi-omics data will be important directions for clinical transformation.