Pathology

Development and clinical validation of deep learning-based immunohistochemistry prediction models for subtyping and staging of gastrointestinal cancers.

BACKGROUND: Immunohistochemistry (IHC) is a critical tool for tumor diagnosis and treatment, but it is time and tissue consuming, and highly dependent on skilled laboratory technicians. Recently, deep learning-based IHC biomarker prediction models have been widely developed, but few investigations have explored their clinical application effectiveness.

Leveraging commonality across multiple tissue slices for enhanced whole slide image classification using graph convolutional networks.

BACKGROUND: Accurate classification of histopathological whole slide images (WSIs) is essential for cancer diagnosis and treatment planning. Conventional WSI creation involves slicing a biopsy tissue into multiple slices, placing them on a single glass slide, and digitally scanning them. While deep learning approaches have shown promise in WSI analysis, they mostly overlook potential common patter...

Transforming heart transplantation care with multi-omics insights.

Heart transplantation (HTx) remains the definitive treatment for patients with end-stage heart disease. Despite the number of HTx performed annually i...

A novel approach for classifying patients with adrenal tumors based on decision support systems and artificial intelligence.

AIMS: Adrenal incidentalomas (AIs) encompass a wide range of clinical entities, from incidental benign neoplasms that need to be monitored to aggressi...

MRI's evolving role in rectal cancer in the era of personalized medicine.

Recent decades have seen great advances in the diagnosis and management of rectal cancer, and magnetic resonance imaging (MRI) has become pivotal for ...

Developing a novel medulloblastoma diagnostic with miRNA biomarkers and machine learning.

BACKGROUND: Medulloblastoma (MB) is the most common malignant brain tumor in children. Current diagnostic methods, such as MRI and lumbar puncture, ar...

Recent topics on thyroid cytopathology: reporting systems and ancillary studies.

As fine-needle aspiration techniques and diagnostic methodologies for thyroid nodules have continued to evolve and reporting systems have been updated...

Ultrasound Radio Frequency Time Series for Tissue Typing: Experiments on In-Vivo Breast Samples Using Texture-Optimized Features and Multi-Origin Method of Classification (MOMC).

OBJECTIVES: One of the most promising auxiliaries for screening breast cancer (BC) is ultrasound (US) radio-frequency (RF) time series. It has the sup...

Leveraging Representation Learning for Bi-parametric Prostate MRI to Disambiguate PI-RADS 3 and Improve Biopsy Decision Strategies.

OBJECTIVES: Despite its high negative predictive value (NPV) for clinically significant prostate cancer (csPCa), MRI suffers from a substantial number...

AI-augmented differential diagnosis of granulomatous rosacea and lupus miliaris disseminatus faciei: A 23-year retrospective pilot study.

Granulomatous rosacea (GR) and lupus miliaris disseminatus faciei (LMDF) exhibit overlapping clinical features, making their differentiation challengi...

Raman spectroscopy in tandem with machine learning - based decision logic methods for characterization and detection of primary precancerous and cancerous cells.

Early cancer detection improves patient outcomes, but most Raman spectroscopy research has focused on discriminating between normal and malignant cell...

Deep learning for classification of aggressive versus non-aggressive central giant cell granuloma using whole-slide histopathology images.

Microscopic images of aggressive and non-aggressive cases of central giant cell granuloma (CGCG) were analyzed by deep learning algorithms in order to...

Radiomic 'Stress Test': exploration of a deep learning radiomic model in a high-risk prospective lung nodule cohort.

BACKGROUND: Indeterminate pulmonary nodules (IPNs) are commonly biopsied to ascertain a diagnosis of lung cancer, but many are ultimately benign. The ...

AI-supported versus manual microscopy of Kato-Katz smears for diagnosis of soil-transmitted helminth infections in a primary healthcare setting.

Soil-transmitted helminths primarily comprise Ascaris lumbricoides, Trichuris trichiura, and hookworms, infecting more than 600 million people globall...

Generating and characterizing human telencephalic brain organoids from stem cell-derived single neural rosettes.

We have developed a method for generating human telencephalic organoids from stem cell-derived isolated single neural rosettes. The use of single neur...

Machine learning-based comparison of transperineal vs. transrectal biopsy for prostate cancer diagnosis: evaluating procedural effectiveness.

BACKGROUND: Transrectal (TR) and transperineal (TP) biopsies are commonly used methods for diagnosing prostate cancer. However, their comparative effe...

Deep Learning MRI Models for the Differential Diagnosis of Tumefactive Demyelination versus Wild-Type Glioblastoma.

BACKGROUND AND PURPOSE: Diagnosis of tumefactive demyelination can be challenging. The diagnosis of indeterminate brain lesions on MRI often requires ...

Epicardial adipose tissue, myocardial remodelling and adverse outcomes in asymptomatic aortic stenosis: a post hoc analysis of a randomised controlled trial.

BACKGROUND: Epicardial adipose tissue represents a metabolically active visceral fat depot that is in direct contact with the left ventricular myocard...

HECLIP: Histology-Enhanced Contrastive Learning for Imputation of Transcriptomics Profiles.

MOTIVATION: Histopathology, particularly hematoxylin and eosin (H&E) staining, is pivotal for diagnosing and characterizing pathological conditions by...

Non-conventional diagnostic tools for lower urinary tract symptoms and bladder outlet obstruction in men: a perspective review.

Introduction Assessing male lower urinary tract symptoms (LUTS) due to Benign outlet obstruction (BOO) remains a challenge in urology due to the limit...