Oncology/Hematology

Leveraging AI for Analysis of Digital Health Information on Cancer Prevention Among Arab Youth and Adults: Content Analysis.

BACKGROUND: As TikTok (ByteDance) grows as a major platform for health information, the quality and accuracy of Arabic-language cancer prevention content remain unknown. Limited access to culturally relevant and evidence-based information may exacerbate disparities in cancer knowledge and prevention behaviors. Although large language models offer scalable approaches for analyzing online health con...

Evaluating Sociodemographic Biases in Artificial Intelligence-Based Glioblastoma Response Assessment Algorithms.

BACKGROUND AND PURPOSE: Recent studies have demonstrated bias in various medical imaging artificial intelligence (AI) models, yet the factors underpinning these biases remain relatively unclear. This study evaluated potential sociodemographic biases in AI-based glioblastoma MRI segmentation models trained on datasets varying in size and demographic composition. We evaluated four nnUNet models with...

Integrative single-cell and machine learning framework reveals prognostic fibroblast subtypes and constructs a fibroblast-related risk signature in lung adenocarcinoma.

DANST enables cell-type deconvolution in spatial transcriptomics using deep domain adversarial neural networks.

Spatial transcriptomics is an emerging technology that can analyze gene expression profiles of tissues while preserving spatial location information. ...

A microenvironment-determined risk continuum refines subtyping in meningioma and reveals determinants of machine learning-based tumor classification.

Classification of tumors in neuro-oncology today relies on molecular patterns (mostly DNA methylation) and their machine learning-supported interpreta...

Individualized gamma knife radiosurgery prescription dosing for pituitary adenomas: development and internal validation of a feedforward neural network model.

BACKGROUND: Gamma Knife radiosurgery (GKRS) is an established treatment for pituitary adenomas yet prescription dose selection is often guided by clin...

Deep learning for synthetic PET imaging: a systematic mapping review of techniques, metrics, and clinical relevance.

BACKGROUND: Synthetic positron emission tomography (PET) imaging, enabled by deep learning, represents a promising approach to minimize radiation expo...

Biomarker discovery and drug repurposing in hepatocellular carcinoma through transcriptomics, machine learning, network pharmacology, and molecular dynamics.

This study employed an integrative computational and systems biology framework to define a diagnostic gene signature for hepatocellular carcinoma (HCC...

Machine learning-driven computational drug repurposing to identify new tubulin inhibitors against cancer.

Tubulin is a validated anticancer target, yet the clinical translation of colchicine-binding site inhibitors remains limited by toxicity and resistanc...

Bio-Inspired Smart Elastin-Like Polypeptides (ELPs) for Precision Drug Delivery: Molecular Strategies, Thermal Responsiveness, and Translational Advances.

Elastin-like polypeptides (ELPs), inspired by the natural elasticity of human elastin, are rapidly evolving as next-generation platforms for precision...

Integrating Spatial Proteogenomics in Cancer Research.

BACKGROUND: Spatial proteogenomics marks a paradigm shift in oncology by integrating molecular analysis with spatial information from both spatial pro...

Integrating multi-omics analysis identifies DNA damage-related gene CLSPN as a biomarker in gastric cancer.

DNA damage exhibits a strong correlation with gastric cancer (GC). However, there is still a paucity of comprehensive, in-depth investigations into th...

[Design and synthesis of molecularly imprinted polymers and their applications in medical diagnosis and biocatalysis].

Molecularly imprinted technology （MIT） represents an advanced synthetic strategy that emulates biological recognition mechanisms， such as antigen-anti...

Spatial and multi-omics transcriptomic dissects platinum resistance in lung adenocarcinoma: a five-gene predictive model with tumor microenvironment dynamics.

The scarcity of reliable biomarkers and predictive models for platinum resistance in lung adenocarcinoma (LUAD) poses a significant clinical challenge...

Spectral CT-derived extracellular volume fraction for pathological characterization and noninvasive T staging in colon cancer.

OBJECTIVES: To investigate the association between the extracellular volume fraction (ECV) quantified by spectral CT and multiparameter pathological f...

Current perspectives in cardiogenic shock.

Cardiogenic shock (CS) remains a leading cause of death in intensive cardiac care. Outcomes are limited by delayed recognition of hypoperfusion, heter...

A Multimodal Fusion Model of Radiomics and Deep Learning Integrating the Tumor Microenvironment Accurately Predicts Pathological Complete Response in Breast Cancer.

BACKGROUND: Pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) is a critical prognostic marker in breast cancer, yet its predictio...

Machine learning-facilitated real-time acoustic trapping in time-varying multi-medium environments toward magnetic resonance imaging-guided microbubble manipulation.

Magnetic resonance imaging-guided acoustic trapping is expected to manipulate drug carriers (e.g., microbubbles) within the body, potentially improvin...

Graph adiabatic diffusion neural networks for distribution-shift breast tumor image classification.

Breast tumor images show low intra-class similarity and suffer from distribution shift, posing challenges for recognition tasks. While increasing the ...

ALKBH5 orchestrates ferroptosis-driven tumor suppression: An LLM-powered discovery in prostate cancer.

The treatment of prostate cancer (PCa) continues to pose substantial clinical challenges. The use of large language models (LLMs) to identify the key ...