Pathology

Deep-learning triage of three-dimensional pathology datasets for comprehensive and efficient pathologist assessments.

Standard slide-based two-dimensional (2D) histopathology severely undersamples spatially heterogeneous tissue, with each thin 2D section representing <1% of the entire biopsy volume. Recent advances in non-destructive three-dimensional (3D) pathology, such as open-top light-sheet microscopy, enable comprehensive high-resolution imaging of large clinical specimens. Since manual review of these mass...

Biomarkers of nivolumab benefit in resectable non-small cell lung cancer.

Perioperative nivolumab significantly improved event-free survival (EFS) compared with placebo in patients with resectable non-small cell lung cancer (NSCLC) in the CheckMate 77T study (ClinicalTrials.gov NCT04025879 )1. Here, after randomization, 98 out of 229 patients who received nivolumab and 92 out of 232 patients who received placebo had evaluable biomarkers (41% of randomized patients). Of ...

CURL-AID: Automated Echocardiographic Motion Analysis for Quantitative Assessment of Posterior Systolic Curling.

OBJECTIVE: Posterior systolic curling (PSC) is a morphofunctional abnormality of the posterior mitral annulus associated with malignant ventricular ar...

Deep learning in Myocarditis: A novel approach to severity assessment.

BACKGROUND: Myocarditis is life-threatening in the acute phase, yet biopsy-the diagnostic gold standard-lacks an objective method to quantify cardiomy...

Machine learning-guided bioelectrical impedance mapping for rapid adjunctive margin assessment in Mohs surgery.

A label-free high-frequency bioelectrical impedance spectroscopy method, coupled with supervised machine learning, was evaluated as an adjunct to hist...

A Three-Slice Deep Learning-Radiomics Nomogram for Challenging Renal Mass Cases: A Double-Center Study.

To determine the optimal spatial-depth architecture for deep learning-radiomics (DLR) differentiation of fat-poor angiomyolipoma (fp-AML) from clear c...

Benchmarking Deep Segmentation Architectures for Histopathological Nuclei Segmentation: A Controlled Study of Transfer Learning, Robustness, and Efficiency Across CNN, Transformer, and Hybrid Models.

Accurate nuclei segmentation is essential for quantitative histopathological analysis and downstream clinical applications such as cancer grading, bio...

Fungal morphotype detection and quantification in microscopic images with TU_MyCo-vision: a user-friendly deep learning object detection tool.

Morphological switching in response to environmental stimuli is a well-known phenomenon in fungi, leading to diverse morphotypes. Microscopic observat...

Deep learning-assisted detection of lymph node metastases in bladder cancer.

BACKGROUND: Bladder cancer is the most common malignancy of the urinary tract and is often treated with radical cystectomy with lymph node dissection,...

Machine Learning-Based Classification of Neuroblastoma Risk Groups and MYCN Status.

BACKGROUND: Artificial intelligence (AI)-based methods have demonstrated considerable promise in identifying histological features in whole slide imag...

Diagnostic decision-making, vertebral augmentation, and osteoporosis therapy in vertebral compression fractures: a narrative review.

Vertebral compression fractures (VCFs) represent the most prevalent osteoporotic fracture and constitute a growing cause of morbidity, mortality, and ...

Unsupervised Global CBC/RUO/CPD Phenotyping Identifies Haematological Clusters Enriched for Thrombocytopenia Severity and Mechanisms.

INTRODUCTION: Thrombocytopenia has heterogeneous mechanisms, including peripheral destruction, consumption, and reduced marrow production. This study ...

Accelerating Structure-Property Relationship Discovery with Multimodal Machine Learning and Self-Driving Microscopy.

Microscopy combined with local spectroscopy is widely used to correlate nanoscale structure with functional properties in materials, but conventional ...

Computable Phenotype for Identifying Undiagnosed Hypermobile Ehlers-Danlos Syndrome: Protocol for a Development and Validation Study.

BACKGROUND: Hypermobile Ehlers-Danlos syndrome (hEDS) is a multisystemic hereditary connective tissue disorder characterized by generalized joint hype...

Deep Learning Enables Automated Detection of Mature B Cell Neoplasms by Flow Cytometry.

Multiparameter flow cytometry is essential for diagnosing mature B-cell lymphomas, yet analysis remains largely manual, time-consuming, and subject to...

Multi-Classification of Vocal Cord Masses via Local-Higher Order Graph Neural Network on Voice Data.

OBJECTIVE: To develop an intelligent diagnostic model based on a Local-Higher Order Graph Neural Network (LHGNN) to achieve noninvasive auxiliary mult...

Placental Blood-Flow Velocity Quantification From Diffusion MRI.

PURPOSE: Altered placental capillary blood flow is closely linked to obstetric complications, yet quantifying capillary-scale blood velocity remains c...

Histopathology-guided multimodal pathomics identifies a PLEKHA6-positive epithelial state associated with phosphoinositide metabolism in esophageal cancer.

Esophageal cancer is biologically heterogeneous, and conventional clinicopathological staging incompletely captures variation in patient outcomes. Thi...

Revealing Crystallization Mechanism of Gallium Arsenide by Machine Learning Molecular Dynamics Simulation.

Due to its characteristics of high electron mobility, moderate band gap, excellent radiation resistance, and high-frequency performance, gallium arsen...

Proactive traffic conflict prediction: uncovering multi-dimensional risk mechanisms through spatiotemporal heterogeneity.

OBJECTIVE: Proactive traffic conflict prediction often relies on average-state macroscopic variables (e.g., mean speed, flow, density), which can obsc...