Oncology/Hematology

GR2ST: Spatial Transcriptomics Prediction based on Graph-Enhanced Multimodal Contrastive Learning.

MOTIVATION: Spatial transcriptomics techniques capture gene expression data and spatial coordinates, while simultaneously correlating them with tissue section images. This advantage makes Spatial transcriptomics data highly valuable for research, such as investigating disease mechanisms and cancer prognosis. However, the extended time and high cost of spatial transcriptomic sequencing currently li...

A machine learning-driven framework integrating cell death and senescence signatures for multi-target drug design and immunotherapy optimization in ovarian cancer.

Ovarian cancer (OC) remains therapeutic challenge due to its complex molecular heterogeneity and therapy-induced adaptive resistance. While non-apoptotic cell death and senescence pathways contribute to tumor evolution and immunosuppression, their integration into predictive models for multi-target drug design and immunotherapy optimization is underexplored. Machine learning was used to identify k...

Reinventing miRNA cancer therapy through immunomodulation and AI.

Despite the rational therapeutic premise of microRNA (miRNA) replacement or inhibition for cancer treatment, its clinical translation remains signific...

Deep learning-based Wilms tumor segmentation to create 3D models for surgical planning: Implementation in the clinical workflow.

BACKGROUND AND AIM: Creating 3D models based on pre-operative MRI of patients with a Wilms tumor (WT) can aid surgical planning. However, creating the...

Emerging role of artificial intelligence in necrotizing enterocolitis and implementation challenges.

Necrotizing enterocolitis (NEC) remains a persistent clinical challenge, with diagnostic strategies largely relying on reactive staging criteria that ...

Plasma multi-omics profiling unravels molecular alterations and biomarker panels during recovery from severe coronavirus disease 2019: a pilot study.

BACKGROUND: The potential multidimensional molecular alterations during recovery of severe patients with coronavirus infectious disease (COVID-19) rem...

Neck adiposity on standard oncologic CT predicts radiation-induced carotid disease in oropharyngeal cancer.

A macrophage co-expression signature enables robust prognostic prediction in glioblastoma.

BACKGROUND: Glioblastoma (GBM) exhibits profound cellular heterogeneity and a highly immunosuppressive microenvironment in which tumor-associated macr...

3D-printed real-time biosensing system integrating deep learning for label-free in vitro T cell culture analysis.

Recent advances in T cell-based immunotherapies highlight the urgent need for precise and dynamic monitoring across the entire cell culture pipeline. ...

Radiomics and artificial intelligence in pancreatic cyst characterization: future or fiction?

Pancreatic cystic lesions (PCLs) are increasingly detected due to the widespread use of cross-sectional imaging and represent a significant diagnostic...

An explainable clinical-radiomics machine learning model for preoperative prediction of WHO/ISUP nuclear grade in clear cell renal cell carcinoma.

BACKGROUND: Accurate preoperative assessment of the WHO/ISUP nuclear grade of clear cell renal cell carcinoma (ccRCC) is critical for guiding individu...

Evaluating the Utility and Limitations of Machine Learning Tumor Segmentation for Automated Longitudinal RANO Treatment Response Classification.

PURPOSE: Machine learning segmentation has emerged in tumor assessment with high performance in volumetric evaluation of brain tumors. It is unclear, ...

Image Quality Assessment of the External Carotid Artery and Its Branches on Ultra-High-Resolution Head and Neck Computed Tomography Angiography Using a High-Resolution 0.25-mm Detector and Deep Learning Reconstruction.

BACKGROUND AND PURPOSE: Deep learning reconstruction can improve image quality of CTA, but its benefit for visualizing small-caliber external carotid ...

Radiomics based on multiphasic contrast-enhanced CT combined with clinical features for differentiating hypovascular pancreatic neuroendocrine tumors and pancreatic ductal adenocarcinoma.

Combined radiomics, PI-RADS, and clinical model improve significant prostate cancer prediction and guide biopsy decision.

OBJECTIVES: The aim of this study was to develop and validate an MRI radiomics-based predictive model to discriminate significant prostate cancer (sPC...

S2A-RConvNet: standalone self-attention enabled deep learning model for brain tumor classification with MRI images.

Globally, the main factor that contributes to increasing the mortality rate among people is the development of abnormal cells in the brain, which lead...

Construction of an XGBoost-SHAP-based malignant transformation risk prediction model for gallbladder polyps.

BACKGROUND: To develop and validate a risk prediction model for malignant transformation in patients with gallbladder polyps (GBPs) using an interpret...

Artificial intelligence-based reclassification of gastric adenocarcinoma enables prognostic stratification via diffuse-type patch proportion.

BACKGROUND: Gastric adenocarcinoma (GAC) remains a major global health burden with marked heterogeneity, complicating diagnosis and prognostic assessm...

Artificial Intelligence for histopathological diagnosis and grading of breast cancer in Ethiopia.

BACKGROUND: Recent advances in computational pathology enables AI-assisted diagnosis and risk stratification of breast cancer. This advance in technol...

Machine learning-based identification of key genes underlying sex differences in hepatocellular carcinoma and targeted drug screening.

Hepatocellular carcinoma (HCC) shows a marked predominance in men, yet the molecular basis for this sex disparity remains unclear. The present study l...