Latest AI and machine learning research in pathology for healthcare professionals.
OBJECTIVE: We developed interpretable machine learning(ML) models to predict overall survival in bladder cancer patients. This approach aims to improve the interpretability and transparency of our modeling results. METHODS: We collected clinical and pathological information on bladder cancer patients from the SEER database, allocating it to training and validation sets in a 7:3 ratio. At the same ...
G protein-coupled receptors (GPCRs) serve as central hubs in tumor signal transduction and microenvironment regulation. However, their therapeutic exploitation is confounded by a fundamental complexity: GPCR functions are exquisitely context-dependent, varying across cell types and spatial locations within the heterogeneous tumor microenvironment. A single receptor may drive malignant proliferatio...
Understanding the mechanisms that govern viral spread in human airway epithelium (HAE) remains a major challenge, particularly with regard to identify...
BackgroundThe clinical heterogeneity of systemic lupus erythematosus exceeds the resolution of conventional disease activity instruments. Artificial i...
Astrocytes maintain extracellular ion and transmitter homeostasis, with the Na⁺ inward gradient playing a crucial role. Earlier studies suggested a ra...
This work introduces a fully annotated synthetic dataset designed to support machine learning-based estimation of fiber orientation in short-fiber rei...
Biomarker research for cancer diagnosis and prognosis has rapidly expanded technologically and thematically, along with advancements in molecular diag...
Multi-omics is the coordinated acquisition, integration, and interpretation of multiple datasets generated from diverse molecular layers of a biologic...
BACKGROUND: Though follicular thyroid carcinoma can be confirmed postoperatively by the histological findings of capsular or vascular invasion, preope...
OBJECTIVES: To develop various artificial intelligence (AI) models including radiomics, deep transfer learning (DTL) and habitat analysis models, as w...
BACKGROUND: Accurate and timely disease detection is essential in modern healthcare. Conventional imaging methods such as computed tomography (CT), ma...
Early detection of tumors constitutes a cornerstone of cancer prevention and control. Medical assessments alongside emerging screening modalities prov...
Lung cancer remains the leading cause of cancer-related mortality worldwide, with early detection being critical for improving survival outcomes. Liqu...
BACKGROUND: Melanoma is the fifth most common cancer in the USA and the UK, with global incidence on the rise. The TNM staging system guides treatment...
BACKGROUND: Machine-learning (ML) driven molecular diagnostics based on omics data has a potential to revolutionize personalized medicine. However, im...
Biomedical imaging is increasingly defined by a paradox: advances in microscopy now enable the routine generation of extraordinarily rich, high-dimens...
Chronic wasting disease (CWD) and scrapie are transmissible spongiform encephalopathy diseases caused by prions, infectious forms of the prion protein...
Through machine learning, researchers have uncovered distinct spatial ecotypes in the tumor microenvironment that are broadly conserved across multipl...
INTRODUCTION: Prostate cancer is well known to be androgen-dependent, with growth directly relying on the androgen receptor signaling pathway. In fact...
PURPOSE: Advanced MRI techniques may provide non-invasive insight into the molecular heterogeneity of glioblastoma. Amide proton transfer-weighted (AP...