Latest AI and machine learning research in oncology/hematology for healthcare professionals.
Background: Colorectal cancer (CRC) remains a leading cause of cancer-related mortality worldwide. Accurate survival prediction is essential for treatment stratification, yet existing pathology foundation models often overlook organ-specific features critical for CRC prognostication. Methods: We propose MorphDistill, a two-stage framework that distills complementary knowledge from multiple patho...
Hepatocellular carcinoma (HCC) is a leading cause of cancer mortality worldwide, yet existing prognostic models incompletely capture its molecular heterogeneity. We developed an interpretable, attention-based multi-branch deep learning framework for multi-omics survival prediction in HCC. Using 358 TCGA LIHC patients with matched mRNA expression, miRNA expression, and DNA methylation data, we firs...
Background Cardiovascular adverse events (CVAEs) after chemoradiotherapy (CRT) for lung cancer are major concerns in Appalachia due to high rates of s...
Inferring cell-cell communication (CCC) from single-cell transcriptomics remains fundamentally limited by reliance on curated ligand-receptor database...
Background: Transthyretin cardiomyopathy (ATTR-CM) is a progressive, potentially fatal disease requiring accurate risk stratification. Echocardiograph...
Background: Accurate preoperative prediction of lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) remains challenging, particularly in ...
Purpose: Manual verification of AI-based auto-contouring is labor-intensive and prone to fatigue-related errors. This study developed the large langua...
Colorectal cancer (CRC) is a leading cause of cancer-related mortality, highlighting the need for early detection and accurate lesion characterization...
Purpose: To evaluate whether chest radiograph-derived age acceleration is associated with incident lung cancer and whether it improves discrimination ...
Background: Previous recommendations on screening for prostate cancer relied on ongoing trials of screening with prostate-specific antigen (PSA), whic...
Biological staging models are a key tool for assessing the severity of Alzheimer's disease (AD), supporting personalized medicine and playing a critic...
Deep learning methods, including deep representation learning (DRL) approaches such as variational autoencoders (VAEs), have been widely applied to ca...
Background: Early breast cancer detection remains central to improving clinical outcomes, yet conventional screening pathways, particularly mammograph...
Reliable, minimally invasive biomarkers for predicting immunotherapy response in head and neck squamous cell carcinoma (HNSCC) remain an unmet clinica...
Based on single-cell RNA sequencing data, differentially expressed genes (LMR DEGs) between colorectal cancer liver metastasis epithelium and primary ...
Deep learning (DL) models have achieved strong performance in an intelligence healthcare setting, yet most existing approaches operate as black boxes ...
Background: Interferon-gamma (IFN-{gamma}) is the primary effector cytokine of adaptive anti-tumor immunity, yet it paradoxically induces a potent imm...
Accurate drug response prediction is a critical bottleneck in computational biochemistry, limited by the challenge of modelling the interplay between ...
Background: Artificial intelligence (AI) has rapidly garnered interest in healthcare, with research showing promise to improve quality, efficiency, an...
Purpose: Manual screening for trial eligibility is inefficient and costly. We prospectively evaluated a large language model (LLM)-assisted prescreeni...