Oncology/Hematology

Radiomics-based gradient boosting model on contrast-enhanced MRI for non-invasive prediction of epidermal growth factor receptor expression and therapeutic response to EGFR-targeted antibody-drug conjugates in high-grade glioma organoid models.

BACKGROUND: Epidermal growth factor (EGF) and its receptor EGF(EGFR) play crucial roles in glioblastoma (GBM) prognosis. However, non-invasive assessment of their expression remains challenging. This study aimed to determine whether radiomics features extracted from contrast-enhanced MRI could predict EGFR expression in high-grade gliomas (HGG) and to explore their associations with immune infiltr...

Development and Validation of a Machine Learning Model for Dementia Staging in a Heterogeneous Cognitive Impairment Cohort Using Multimodal Features.

RATIONALE AND OBJECTIVES: With the emergence of disease-modifying therapies, precise staging of dementia is urgent. This study aimed to develop a machine learning model integrating multimodal data to achieve objective staging of dementia severity in patients with cognitive impairment. MATERIALS AND METHODS: A total of 149 patients (100 with Alzheimer's disease) were recruited. Demographic data, ne...

Novel "No-Lo" imaging techniques to minimize intraoperative radiation exposure in vascular and endovascular surgery.

INTRODUCTION: The rapid expansion in endovascular techniques has placed vascular surgeons among those most exposed to occupational medical radiation. ...

A navigation-guided 3D breast ultrasound scanning and reconstruction system for automated multi-lesion spatial localization and diagnosis.

Handheld ultrasound (HHUS) is indispensable for breast cancer screening but remains compromised by operator-dependent acquisition, subjective 2D inter...

Multimodal sparse fusion transformer network with spatio-temporal decoupling for breast tumor classification.

Accurate analysis of tumor morphology, vascularity, and tissue stiffness under multimodal ultrasound imaging plays a critical role in the diagnosis of...

Detecting optimal biomarkers in ovarian cancer cells from high-dimensional mRNA expression data using machine learning.

BACKGROUND AND OBJECTIVE: Reliable detection of robust biomarkers from high-dimensional transcriptomic data remains a major challenge in computational...

From slides to AI-ready maps: Standardized multi-layer tissue maps as metadata for artificial intelligence in digital pathology.

A Whole Slide Image (WSI) is a high-resolution digital image created by scanning an entire glass slide containing a biological specimen, such as tissu...

Clinically aligned whole-body MRI segmentation of skeletal metastases via Supervised Anatomical Pretraining.

In oncology practice, response assessment of metastatic disease requires reliable and reproducible quantification of measurable metastatic burden. Man...

Associations of fine particulate matter pollution components with overall, prostate cancer, and cardiovascular disease mortality in men with prostate cancer: A cohort study.

BACKGROUND: Over 3.5 million US men are living with prostate cancer (Pica), many with underlying cardiovascular disease (CVD). Fine particulate matter...

Automated 3D body composition analysis on chest CT scans for survival prediction in high-grade extremity soft tissue sarcomas.

INTRODUCTION: High-grade soft tissue sarcomas (STShg) of the extremities are rare cancers with a poor prognosis. Accurate staging at initial evaluatio...

High reliability Ag/Ni/NiO nanowire-based SERS for cancer detection: a study on breast cancer.

Over the past decade, substantial research has focused on identifying cancer biomarkers using Raman spectroscopy. However, no commercial Raman-based d...

The seeds of the future are in the present: A blind exploration of metastable states.

In this work, we present a type of molecular dynamics simulation that aims at finding, in a blind way, metastable states. Using only data coming from ...

Decrypting potential mechanisms linking ochratoxin A to hepatocellular carcinoma: an integrated approach combining toxicology, machine learning, molecular docking, and molecular dynamics simulation.

BACKGROUND: Ochratoxin A (OTA), a common food-borne mycotoxin, is a potential human carcinogen, yet the specific molecular mechanisms linking it to he...

Commentary on "Deep learning enhanced MRI radiomics in predicting pathologic response of head and neck squamous carcinoma to neoadjuvant chemoimmunotherapy: a retrospective analysis".

Real-time high-throughput characterisation of the surface elasticity of suspended cells.

The intrinsic elasticity of the cell membrane-cortex complex, i.e., the cell surface, is a promising biomarker for cell status and disease and has wid...

Engineering Immune Cell to Counteract Aging and Aging-Associated Diseases.

Aging and age-related diseases are a major public health concern, driving interest in anti-aging research. While small molecules and natural compounds...

Advanced immunotherapy across diseases and the role of artificial intelligence: A review.

Immunotherapy, a therapeutic strategy aimed at modulating the host immune system, has undergone rapid evolution over recent decades, particularly in o...

A multi-algorithm machine learning framework identifies FGD5, LRRC36, C8B, and MYOC as novel diagnostic biomarkers in lung adenocarcinoma.

Machine learning-driven exosome-mimetic lipid nanoparticles for tumor-specific targeting.

Exosome-mimetic lipid nanoparticles (ENPs) are a promising alternative to PEGylated lipid nanoparticles (LNPs) for targeted cancer therapy, offering i...

18F-FDG PET radiomics model for predicting TARE response in patients with colorectal cancer liver metastases.

PURPOSE: Predicting treatment response in patients with colorectal cancer liver metastases (CRCLM) who have undergone transarterial radioembolization ...