Oncology/Hematology

Identifying predictive hematological biomarkers for radiation exposure by machine learning in mouse models.

BACKGROUND: Population-scale radiation exposure assessment during radiological emergencies is hindered by the slow and costly nature of current methods, creating a need for rapid, affordable screening tools. Radiation biodosimetry using peripheral blood counts is a promising approach, but estimating low-dose exposures and exposure at extended time points remains challenging, especially when accoun...

Exploring the immune landscape and immunotherapy relevance of ER stress-related lncRNAs in melanoma through multi-omics and explainable machine learning.

BACKGROUND: Melanoma is a highly aggressive malignancy with poor clinical outcomes, and endoplasmic reticulum (ER) stress plays an important role in tumor progression, immune regulation, and therapeutic resistance. However, the prognostic significance and biological functions of ER stress-related long noncoding RNAs (ERlncRNAs) in melanoma remain largely unclear. METHODS: We performed an integrati...

Dissecting T-cell exhaustion heterogeneity and immune ecosystem dynamics in colorectal cancer through multi-omics machine learning.

BACKGROUND: Immunotherapy has shown limited efficacy in a substantial subset of CRC patients, yet the mechanisms underlying therapeutic resistance rem...

Body composition radiomics integrated with machine learning to predict prognosis in advanced NSCLC treated with first-line immunochemotherapy and concurrent radiotherapy.

OBJECTIVE: Adipose tissue is a highly heterogeneous and plastic endocrine and immune organ whose characteristics have been linked to prognosis in NSCL...

Pancreatic organoids and organoid-on-a-chip platforms: from disease modeling to precision therapy.

BACKGROUND: Pancreatic diseases, including diabetes, pancreatic ductal adenocarcinoma, pancreatitis, and cystic fibrosis, impose a substantial clinica...

Machine learning-driven QSAR modeling combined with single cell transcriptomics identifies novel drug targets for lung cancer.

BACKGROUND: Non-small cell lung cancer (NSCLC) is a leading cause of cancer-related mortality, largely due to frequent metastasis to the brain and bon...

Spatially interpretable artificial intelligence framework to tailored neoadjuvant dual HER2 blockade in HER2-positive breast cancer.

Neoadjuvant dual HER2 blockade with trastuzumab and pertuzumab plus chemotherapy represents the current standard-of-care for HER2-positive breast canc...

Dual modal pathomics model for colorectal cancer early recurrence prediction and mutation landscape analysis.

Patients with stage II/III colorectal cancer remain at substantial risk of early postoperative recurrence, yet accurate risk stratification remains ch...

Data-driven system identification in cancer systems biology: A multiscale modeling approach to melanoma.

BACKGROUND AND OBJECTIVE: Developing systems biology models of cancer is critical for improving understanding of complex biological interactions and a...

Differentiating sub-centimeter lung metastases in colorectal cancer by deep learning: a multicenter retrospective study.

BACKGROUND: Early diagnosis of sub-centimeter lung metastases is critical for timely decision-making and improved prognosis in patients with colorecta...

Risk stratification for reoperation outcomes in recurrent or persistent papillary thyroid carcinoma: development and validation of a machine learning model.

BACKGROUNDS: Reoperation is a key therapeutic strategy for recurrent or persistent papillary thyroid carcinoma (PTC), but its outcomes remain highly h...

Computational prediction of TCR cross-reactivity: principles, challenges and translational opportunities.

T-cell receptor (TCR) cross-reactivity, whereby a single TCR recognizes multiple peptide-MHC (pMHC) ligands, is essential for immune surveillance but ...

Development and Validation of a Machine Learning Model to Prognosticate Hepatocellular Carcinoma.

BACKGROUND: Prognostic models for hepatocellular carcinoma (HCC) may have limited accuracy. We aimed to construct and validate a novel prognostic mode...

RTsDEN: reverse task attention enabled deep learning model for lung cancer detection using computed tomography.

BACKGROUND: Lung cancer remains the most significant cause of cancer-related death worldwide due to the critical challenges in diagnosis. Despite the ...

Perspectives on artificial intelligence-generated chronic disease risk predictions in early breast cancer: an international survey among radiation oncologists.

Frontiers in Raman nano-diagnostics: SERS and TERS of extracellular vesicles for cancer detection.

Efforts to create rapid, non-invasive, and reliable cancer diagnostics have increasingly focused on extracellular vesicles (EVs), nanoscale carriers o...

Comparing clinical decision-making between colposcopists and large language models in cervical dysplasia management: a pilot prospective multicenter study.

PURPOSE: This prospective multicenter study aimed to compare the decision-making abilities of board-certified colposcopists and two commercially avail...

Location of AI risk markers and associated mammographic features in screening mammograms obtained years before screen-detected breast cancer.

OBJECTIVES: To examine screening mammograms assigned high-risk scores by two artificial intelligence (AI) models, 2 and 4 years prior to screen-detect...

Engineered small extracellular vesicles in hematologic malignancies: mechanisms, therapeutic strategies, and translational challenges.

Hematologic malignancies remain among the most challenging cancers to treat due to genetic heterogeneity, clonal evolution, and therapy resistance. Ex...

Research progress and challenges of multimodal deep learning models for prognosis of gastric cancer.

In the field of oncology research, patient data typically encompasses diverse multimodal characteristics, including age, survival status, radiological...