Search

BackgroundOptimizing the diagnosis and treatment of hematological diseases is a challenging yet crucial research area. Effective treatment plans typically require the comprehensive integration of cell morphology, immunology, cytogenetics, and molecular biology. These plans also consider patient-specific factors such as disease stage, age, and genetic mutation status. With the advancement of artificial intelligence (AI), more “AI + medical” application models are emerging. In clinical practice, many AI-assisted systems have been successfully applied to the diagnosis and treatment of hematological diseases, enhancing precision and efficiency and offering valuable solutions for clinical practice.ObjectiveThis study summarizes the research progress of various AI-assisted systems applied in the clinical diagnosis and treatment of hematological diseases, with a focus on their application in morphology, immunology, cytogenetics, and molecular biology diagnosis, as well as prognosis prediction and treatment.MethodsUsing PubMed, Web of Science, and other network search engines, we conducted a literature search on studies from the past 5 years using the main keywords “artificial intelligence” and “hematological diseases.” We classified the clinical applications of AI systems according to the diagnosis and treatment. We outline and summarize the current advancements in AI for optimizing the diagnosis and treatment of hematological diseases, as well as the difficulties and challenges in promoting the standardization of clinical diagnosis and treatment in this field.ResultsAI can significantly shorten turnaround times, reduce diagnostic costs, and accurately predict disease outcomes through applications in image-recognition technology, genomic data analysis, data mining, pattern recognition, and personalized medicine. However, several challenges remain, including the lack of AI product standards, standardized data, medical–industrial collaboration, and the complexity and non-interpretability of AI systems. In addition, regulatory gaps can lead to data privacy issues. Therefore, more research and improvements are needed to fully leverage the potential of AI to promote standardization of the clinical diagnosis and treatment of hematological diseases.ConclusionOur results serve as a reference point for the clinical diagnosis and treatment of hematological diseases and the development of AI-assisted clinical diagnosis and treatment systems. We offer suggestions for further development of AI in hematology and standardization of clinical diagnosis and treatment.

Adverse events of atrial fibrillation (AF) have been commonly reported in lymphoma patients in treating Bruton's tyrosine kinase inhibitors (BTKi). The incidence rate of AF can vary depending on the specific types of BTKi and the patient population. Totally 45 published studies have revealed that the overall incidence rate of AF is 5% (95% CI 4%–7%). By performing a subtype single-rate analysis, the second-generation BTKi shows a lower AF incidence rate and lower cardiovascular toxicity. In the subtype single-rate analysis, we conclude the different AF incidence rates of Ibrutinib (10%, 95% CI 7%–13%), Acalabrutinib (4%, 95% CI 1%–6%), Orelabrutinib (0%, 95% CI 0%–1%), and Zanubrutinib (0%, 95% CI 0%–1%). The comprehensive analysis of AF inspires us to better predict and manage AF and other cardiovascular events in treating lymphoma. Meticulous evaluation, collaboration between cardiologists and hematologists, and discovery of new biomarkers are essential for its management.

Abstract Spatial transcriptomics technologies developed in recent years can provide various information including tissue heterogeneity, which is fundamental in biological and medical research, and have been making significant breakthroughs. Single-cell RNA sequencing (scRNA-seq) cannot provide spatial information, while spatial transcriptomics technologies allow gene expression information to be obtained from intact tissue sections in the original physiological context at a spatial resolution. Various biological insights can be generated into tissue architecture and further the elucidation of the interaction between cells and the microenvironment. Thus, we can gain a general understanding of histogenesis processes and disease pathogenesis, etc. Furthermore, in silico methods involving the widely distributed R and Python packages for data analysis play essential roles in deriving indispensable bioinformation and eliminating technological limitations. In this review, we summarize available technologies of spatial transcriptomics, probe into several applications, discuss the computational strategies and raise future perspectives, highlighting the developmental potential.

Background: T cell lymphoma is a complex and highly aggressive clinicopathological entity with a poor outcome. The angioimmunoblastic T-cell lymphoma (AITL) tumor immune microenvironment is poorly investigated. Methods: Here, to the best of our knowledge, spatial transcriptomics was applied for the first time to study AITL. Results: Using this method, we observed that AITL was surrounded by cells bearing immune-suppressive markers. CCL17 and CCL22, the dominant ligands for CCR4, were up-regulated, while the expression of natural killer (NK) cell and CD8+ cytotoxic T lymphocyte (CTL) markers decreased. Colocalization of Treg cells with the CD4+ TFH-GC region was also deduced from the bioinformatic analysis. The results obtained with spatial transcriptomics confirm that AITL has a suppressive immune environment. Chemotherapy based on the CHOP regimen (cyclophosphamide, doxorubicin, vincristine plus prednisone) induced complete remission (CR) in this AITL patient. However, the duration of remission (DoR) remains a concern. Conclusions: This study demonstrates that AITL has an immune suppressive environment and suggests that anti-CCR4 therapy could be a promising treatment for this lethal disease.