Pathology

Radiomics and artificial intelligence in pancreatic cyst characterization: future or fiction?

Pancreatic cystic lesions (PCLs) are increasingly detected due to the widespread use of cross-sectional imaging and represent a significant diagnostic challenge because of their heterogeneous biological behavior, ranging from benign lesions to neoplasms with malignant potential. Accurate characterization and risk stratification are essential to guide appropriate management and avoid unnecessary su...

Combined radiomics, PI-RADS, and clinical model improve significant prostate cancer prediction and guide biopsy decision.

OBJECTIVES: The aim of this study was to develop and validate an MRI radiomics-based predictive model to discriminate significant prostate cancer (sPCa), compare it with PI-RADS, and determine whether incorporating PI-RADS and other clinical variables improves clinical performance. MATERIALS AND METHODS: A retrospective observational study was conducted using a cohort of 1497 MRI cases from 1395 m...

Construction of an XGBoost-SHAP-based malignant transformation risk prediction model for gallbladder polyps.

BACKGROUND: To develop and validate a risk prediction model for malignant transformation in patients with gallbladder polyps (GBPs) using an interpret...

MMP9 as a shared immune-related gene in Alzheimer's and Huntington's diseases: a cross-tissue transcriptomic analysis.

Alzheimer's disease (AD) and Huntington's disease (HD) share neuroinflammatory mechanisms, yet their specific immune microenvironments remain poorly u...

Artificial intelligence-based reclassification of gastric adenocarcinoma enables prognostic stratification via diffuse-type patch proportion.

BACKGROUND: Gastric adenocarcinoma (GAC) remains a major global health burden with marked heterogeneity, complicating diagnosis and prognostic assessm...

Artificial Intelligence for histopathological diagnosis and grading of breast cancer in Ethiopia.

BACKGROUND: Recent advances in computational pathology enables AI-assisted diagnosis and risk stratification of breast cancer. This advance in technol...

Machine learning-based identification of key genes underlying sex differences in hepatocellular carcinoma and targeted drug screening.

Hepatocellular carcinoma (HCC) shows a marked predominance in men, yet the molecular basis for this sex disparity remains unclear. The present study l...

DUSTrack: Semi-automated point tracking in ultrasound videos.

UNLABELLED: Ultrasound technology enables safe, non-invasive imaging of dynamic tissue behavior, making it a valuable tool in medicine, biomechanics, ...

Predicting Agitation Stability of Monoclonal Antibodies during Developability Assessment.

Developability assessment facilitates the selection of antibody drug candidates with desirable pharmaceutical properties. However, it remains uncertai...

Deep learning-based multimodal radiopathomics for preoperative prediction of lymph node metastasis in papillary thyroid carcinoma.

Papillary thyroid carcinoma (PTC) exhibits a high incidence and a strong propensity for lymph node metastasis (LNM). Accurate preoperative assessment ...

Deep learning for predicting pituitary neuroendocrine tumour lineage and high-risk subtypes from histology.

Pituitary neuroendocrine tumours (PitNETs) exhibit significant heterogeneity, posing challenges for clinical management. We developed a deep learning ...

Hybrid diagnostic framework for bone cancer detection using deep learning and radiomics analysis.

Currently, bone cancer remains a big challenge in healthcare, early and accurate diagnosis is therefore key to achieving the required treatment outcom...

Machine learning based on magnetic resonance imaging-derived texture features for differentiation of minor salivary gland tumors.

OBJECTIVE: We aimed to differentiate between benign and malignant minor salivary gland tumors using machine learning (ML) based on magnetic resonance ...

Topo-UNet: A topology-aware multi-task network for pulmonary vessel segmentation.

The precise segmentation of pulmonary vessels is crucial for the early diagnosis and treatment of pulmonary diseases. However, vessel images are frequ...

3D vessel reconstruction from sparse-view dynamic DSA images via vessel probability guided attenuation learning.

Digital Subtraction Angiography (DSA) is one of the gold standards for vascular disease diagnosis. With the help of a contrast agent, time-resolved 2D...

Sidelobe suppression of Barker codes via MRH method.

The Barker code is widely used in ultrasonic testing to enhance transmitted energy and achieve high-resolution pulse compression through a matched fil...

MechanoAge, a machine learning platform to identify individuals susceptible to breast cancer based on mechanical properties of single cells.

BACKGROUND: Emerging evidence links cellular ageing and biophysical alterations with cancer susceptibility. Existing breast cancer risk models inadequ...

Percutaneous Lung Biopsies Aided by Artificial Intelligence: A Comparison Between Computer and Physician-Chosen Biopsy Paths.

OBJECTIVE: To compare Trajectory Recommendation Algorithm for CT-guided Biopsy (TRAX)-generated lung biopsy puncture pathways versus physician-chosen ...

Advancing collagen-related pathology assessment through second-harmonic generation imaging.

Collagen remodelling and dysregulation are the hallmarks of diverse pathological conditions. In this context, second-harmonic generation (SHG) imaging...

AI in Atomic Force Microscopy: Advancing Biological Nanoscale Imaging and Autonomous Discovery.

Atomic force microscopy (AFM) enables label-free nanoscale imaging and nanomechanical profiling but remains constrained by low throughput, operator de...