Pathology

Advances in laboratory diagnostic studies of pheochromocytoma and paraganglioma.

Pheochromocytoma (PCC) and paraganglioma (PGL), collectively referred to as pheochromocytomas and paragangliomas (PPGLs), are rare neuroendocrine tumors characterized by marked genetic susceptibility and substantial biological heterogeneity. Given that all PPGLs are considered to have metastatic potential, early and accurate diagnosis is critical for optimizing therapeutic strategies and improving...

Electroactive Nanomaterials in Tissue Engineering: Advances, Mechanisms and Future Perspectives.

Simulation of the dynamic electromechanical microenvironment is of great significance in tissue engineering, especially in the regeneration of those electrically responsive tissues, such as nerves, heart and bone. In recent years, electroactive nanomaterials (ENMs) have shown great potentials in tissue engineering due to their intrinsic conductivity, piezoelectricity and redox activity, which can ...

Identification of replication factor C subunit 4 as a potential therapeutic target in esophageal squamous cell carcinoma based on bioinformatic analysis and machine learning.

BACKGROUND: Esophageal squamous cell carcinoma (ESCC) is one of the highly lethal and aggressive malignant tumors worldwide. To effectively prevent an...

3D Registration-Guided Deformable Residual Inpainting for ssEM Restoration.

MOTIVATION: Serial section Electron Microscopy (ssEM) is essential for studying biological cell structures at nanometer resolution. However, Supportin...

Non-invasive predictive model for incidental gallbladder carcinoma based on multimodal features: Integrating clinical data, MRI radiomics, and deep transfer learning features.

Gallbladder carcinoma, among the most prevalent malignancies of the biliary system, often presents with insidious early symptoms. Delayed diagnosis of...

Preoperative Risk Assessment of Adrenal Metastases in a Multicenter Study: Development of a Robust Federated Learning Model.

RATIONALE AND OBJECTIVES: In clinical practice, the preoperative risk assessment of adrenal metastases versus benign adrenal lesions carries a substan...

AI-Driven Detection and Classification of Voice Disorders Using Acoustic Recordings.

OBJECTIVES: To develop and evaluate artificial intelligence (AI) models for detecting and classifying voice disorders using acoustic recordings, aimin...

An evaluation of artificial intelligence assisted prostate biopsy reporting in the Articulate Pro study.

Prospective evidence on clinical utility of AI in histopathology is limited. We conducted a prospective study across three National Health Service spe...

Research on multi-stage deep learning based intelligent diagnosis of skin diseases and skin medicine diagnosis community construction concept.

Accurate diagnosis and assessment of the severity of skin diseases are essential for appropriate clinical treatment. This paper proposes a multi-stage...

Radiomics analysis of restaging MRI for detection of pathological complete response in locally advanced rectal cancer.

PURPOSE: magnetic resonance imaging (MRI)-based radiomics has emerged as a promising approach for non-invasive prediction of treatment response in rec...

Overcoming Domain Shift in Atypical Mitotic Figure Detection with Deep Ensemble Learning.

The morphological classification of atypical mitotic figures (AMFs) is a critical prognostic task in histopathology, but deep learning models often la...

Explainable Hierarchical Swin Transformer for Multi-Scale Breast Cancer Histopathology Classification.

Accurate and transparent classification of breast cancer histopathology remains a major challenge due to morphological variability, class imbalance, a...

Integrating Radiomics and Machine Learning to Improve Fluorescence Image Segmentation in in vitro models.

Myocardial infarction leads to fibrotic scar formation, compromising heart function and leading to heart failure. In vitro models of cardiac fibrotic ...

AI and Digital-Twin Synergy for Field Optimisation for Targeted Drug Delivery.

Precise mapping of magnetic fields is crucial for magnetic drug targeting, microrobotics, and magnetically actuated biomedical devices. In this paper,...

Building an Ontology-Based Cohort of Liver Cancer Imaging Data for AI Development on the European Federated Platform EUCAIM.

Hepatocellular carcinoma (HCC) is steadily increasing in incidence worldwide and requires data-driven approaches to improve diagnosis, prognosis, and ...

Automated Segmentation of Tissue Zones in Distraction Osteogenesis.

Automated analysis of histological sections in distraction osteogenesis can reduce manual effort and subjectivity in histological assessment. A 2D nnU...

Multimodal Graph-Based Model for Discrete-Time Survival Prediction in Liver Cancer.

Liver cancer is a leading cause of cancer mortality; hepatocellular carcinoma (HCC), its predominant form, requires accurate survival prediction to gu...

Identification of Cervical Cancer Biomarkers Using Gene Co-Expression Networks and Machine Learning Methods.

Cervical cancer (CC) causes significant mortality due to late diagnosis and limited understanding of its molecular drivers. The complex gene co-expres...

Practical Implications for Using Laboratory Data: Research over Federated Networks.

INTRODUCTION: Semantic standardization is essential, but not sufficient, to enable research over real-world data networks. Even when harmonized, diffe...

U-CBAMNet: an attention-guided deep learning model for accurate and explainable prediction of HER2 expression from breast ultrasound cine videos.

BACKGROUND: Accurate assessment of human epidermal growth factor receptor 2 (HER2) expression is essential for guiding targeted therapy in breast canc...