Pathology

An innovative deep learning paradigm for automated detection and accurate classification of lung nodules in magnetic resonance imaging.

Lung cancer, the predominant kind of cancer, needs considerable care, since inadequate treatment may lead to fatal outcomes. The integration of computer-aided diagnostic (CAD) systems is crucial for the early detection of lung nodules, greatly aiding in the decrease of death rates associated with lung cancer. Magnetic resonance imaging (MRI) is a proficient technique for diagnosing lung cancer. Di...

Establishment and validation of intelligent review rules for blood cell analysis based on the gradient boosting decision tree algorithm.

OBJECTIVE: To develop artificial intelligence (AI)-based intelligent review rules to accurately screen samples that require retests or blood smear microscopy examinations, thereby reducing the review rate, improving the work efficiency of haematology laboratories, and ensuring the quality of whole blood analysis. METHODS: A total of 10,212 EDTA-K2 venous blood samples from the clinical laboratorie...

Integrative single-cell and bulk transcriptomics reveal cuproptosis subtypes, prognostic models, and COL5A1 as a key gene in glioblastoma.

Glioblastoma (GBM) remains one of the most aggressive primary brain tumors with limited therapeutic options. Cuproptosis, a recently identified copper...

A blood transcriptomic signature anchored to central nervous system pathology enables noninvasive detection of multiple sclerosis.

BACKGROUND: Multiple sclerosis (MS) lacks noninvasive biomarkers anchored to central nervous system (CNS) pathology. This study aimed to identify a bl...

Effects of breast cancer risk factors on the tumor microenvironment: a morphological deep-learning analysis of three prospective cohorts.

Emerging evidence suggests a critical role of the tumor microenvironment (TME) in breast cancer (BC) development and outcomes, yet factors that modify...

PD-YOLO: a high-precision detection network for fine-grained lung cancer and multi-scale pulmonary nodules in CT images.

Timely and accurate Computed Tomography (CT) screening is crucial for the early clinical treatment of lung cancer and preventing the progression of ma...

A deep learning framework for histopathological analysis of pixel-level extracellular matrix variation in standard H&E-stained images.

Artificial intelligence-driven image analysis has enabled significant advances in digital pathology. However, most approaches have focused on cell or ...

A cloud-based miniscope for neurosurveillance of brain health and disease in freely behaving animals.

Miniaturized microscopes or 'miniscopes' for neuroimaging in freely behaving animals mostly operate over short durations (<2 h) and image either neuro...

A Novel ConvNeXtV2-MIL Approach for Accurate and Efficient Classification of Microscopic Fungal Morphology.

Fungal infections, especially in people with weakened immune systems, are a significant global health burden. Accurate identification of fungal morpho...

MammoDenseSegNet: A Context-Aware Deep Learning Model for Dense Tissue Segmentation in Digital Mammograms.

Breast density is a breast cancer risk factor. The accurate quantification of breast density requires reliable segmentation of dense tissue in mammogr...

Dynamic Fuzzy-Gaussian Modeling (DynFGM): A Kurtosis-Adaptive Unsupervised Framework for Automated Adipose Tissue Segmentation in Abdominal MRI.

Accurate MRI-based quantification of abdominal adipose tissue is critical for metabolic risk assessment but is limited by labor-intensive manual segme...

Transcriptomic identification of key genes influencing m7G modification in thyroid carcinoma and experimental validation.

BACKGROUND: N7-methylguanosine (m7G) modification plays a critical role in RNA metabolism and is increasingly recognized for its implications in cance...

Deep learning for intraoperative recognition of critical structures in total hysterectomy.

BACKGROUND: This study aimed to develop and evaluate a deep learning-based surgical navigation system capable of recognizing the ureter, uterine arter...

Bone marrow CCR3 dictates eosinophil lineage commitment of CD34⁺ progenitors to orchestrate allergic rhinitis: A composite study.

BACKGROUND: Allergic rhinitis (AR) is a common Th2-mediated inflammatory disease of the nasal mucosa, in which eosinophils serve as pivotal effector c...

Extracellular vesicles as diagnostic and prognostic biomarkers in non-small cell lung Cancer.

INTRODUCTION: Non-small cell lung cancer (NSCLC) remains the leading cause of cancer-related mortality worldwide, largely due to late-stage diagnosis ...

Artificial intelligence-assisted screening for NTRK fusion-positive salivary gland tumors: A novel digital pathology workflow.

NTRK fusion is a promising therapeutic target for salivary gland cancer (SGC). However, the diagnostic complexity of the histological SGC subtype and ...

Automated precise recognition of ovine embryo microscopic images using a boundary-enhanced deep learning network.

Efficient selection of in vitro-fertilized embryos is crucial for assisted reproduction in sheep, and automated microscopic analysis can enable object...

A novel model for assessing lymph node metastasis and the immune microenvironment in breast cancer by integrating digital pathology images and transcriptomics.

BACKGROUND/AIMS: Accurate prediction of the lymph node status is essential for clinical decision-making in breast cancer. This work aimed to develop a...

Path beyond the blind end-unravel the imaging spectrum of appendiceal pathologies.

The appendix is involved in a diverse spectrum of inflammatory, infectious, benign, and malignant conditions that extend far beyond acute appendicitis...

Automating standardization of prostate cancer biopsy and histopathology reports with privacy-preserving local large language models.

PURPOSE: Large-scale biomedical analysis in prostate cancer requires structured, tabular datasets, yet most clinical documentation remains in free-tex...