Latest AI and machine learning research in oncology/hematology for healthcare professionals.
T-lineage acute lymphoblastic leukemia (T-ALL) is an aggressive pediatric malignancy characterized by complex heterogeneity across multiple molecular layers. Accurate subtyping is essential for understanding disease mechanisms, risk stratification, and guiding targeted therapeutic strategies. However, current diagnostic approaches are labor-intensive and time-consuming, and existing computational ...
Multi-omics integration, the joint analysis of two or more high-dimensional molecular data types collected on the same biological samples, is now a standard analytical approach across nutrigenomics, toxicogenomics, microbiome research, and disease genomics. Existing methods sit on a trade-off between expressiveness and interpretability: latent-variable methods such as MOFA and DIABLO yield compact...
Tumour-educated platelets (TEPs) carry cancer-type-specific RNA signatures accessible through whole-blood RNA sequencing, but systematic multi-algorit...
Skin cancer is a common and fast rising malignancy worldwide. Early detection is critical for improving outcomes. Deep learning models trained on derm...
Hepatocellular carcinoma (HCC) is biologically heterogeneous, shaped by the interplay between hepatic functional reserve and tumor-related oncologic f...
Computational pathology leverages deep learning to extract clinically relevant information from digitized tumor slides, predicting histopathological s...
While the expansion of spatial omics has revolutionized our ability to dissect tissue architecture, the accumulation of incompatible computational met...
Tissue-specific chromatin states shape regional mutation density in primary tumors, but whether this relationship persists in metastases is unclear. W...
Breast ultrasound imaging is an important noninvasive method for early breast cancer diagnosis, but automatic benign/malignant classification remains ...
Despite years of methodological progress, how far AI has come in liver fibrosis staging has never been systematically evaluated under the heterogeneou...
This study presents a methodology for constructing a clinically verified dataset of dermatoscopic images for medical informatics research. The relevan...
Background Background breast features are frequently noted in pathology reports alongside invasive breast cancer but rarely factor into prognosis or t...
General-purpose large language models (LLMs) are trained on large corpora to acquire broad knowledge, but whether LLMs can replace, or augment, task-s...
Perturbations of genes with functional importance in T cells could be used to change the distribution of CD8 T cell states to enhance anti-tumor funct...
Accurate brain tumor segmentation using multiparametric MRI is critical for effective treatment planning. However, in clinical settings, complete acqu...
Training Deep Neural Networks for tracking individual cells in biomedical videos requires a large amount of annotated data. The annotation of videos f...
Background: Structuring oncology clinical notes into registry-grade variables is essential for research and care but remains labour-intensive and erro...
Background: African Americans (AA) experience disproportionate burden of colorectal cancer (CRC). Dysregulation of the Wingless-related integration si...
Treatment of advanced head and neck squamous cell carcinoma (HNSCC) often involves radiotherapy combined with chemotherapy, targeted therapy, or immun...
Today, advancements in our understanding of cancer biology are increasingly attributed to large-scale clinical-molecular datasets. The case in point f...