Pathology

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 rectal cancer. This study aimed to develop and validate a machine learning model based on radiomic features extracted from restaging MRI after neoadjuvant therapy in patients with locally advanced rectal cancer (LARC), to identify those achieving pathol...

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 lack generalization across diverse clinical settings. This study presents a robust and reproducible pipeline for AMF detection. We compiled a large dataset from three public sources and trained an ensemble of three ConvNeXt models using a 3-fold cross-...

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...

The CT colonography radiology and data system: history, updates, and future directions.

Computed tomography colonography, also known as virtual colonoscopy, is a minimally invasive imaging technique developed in the early 1990s to evaluat...

Deep Learning Enhances Failure Mode Analysis in Dental Adhesion Studies.

Failure mode analysis after shear bond strength testing is essential for evaluating adhesive performance yet remains highly subjective when relying so...

THBS1: a biomarker for PCOS and its role in pathogenesis via the PI3K/AKT signaling pathway.

This study identified thrombospondin-1 (THBS1) as a potential biomarker for polycystic ovary syndrome (PCOS) and its pivotal role in disease pathogene...

Integrating network pharmacology, molecular docking, machine learning, and experimental validation: puerarin improves sepsis-induced acute kidney injury via the Sirt1-Nrf2-HO-1 pathway.

Acute kidney injury (AKI) associated with sepsis has a high clinical mortality rate, and there is a lack of effective therapeutic targets; uncontrolle...

New learnings from the molecular pathology of the synovial tissue in rheumatoid arthritis: from pathogenesis to therapeutic targeting toward precision medicine.

Despite the success of targeted therapies in rheumatoid arthritis, the lack of predictive biomarkers of response leads to an empirical treatment appro...

DGAT: a dual-graph attention network for inferring spatial protein landscapes from transcriptomics.

Spatial transcriptomics (ST) technologies provide genome-wide transcriptomic profiles in tissue context but lack direct protein-level measurements, wh...

A deterministic method for quantifying spindle-shaped cells in noisy bright-field microscopy.

Accurate quantification of spindle-shaped cells in bright-field microscopy remains challenging due to low contrast, noise, and highly variable cell mo...

Pan-cancer analysis of single-cell profiles with polygenic signals reveals genetic influences on tumor immunity across cancers.

Inherited genetic variation can weaken the ability of the immune system to detect and eliminate malignant cells, limiting the effectiveness of cancer ...

Machine learning and artificial intelligence in liquid biopsy-based early detection of pancreatic cancer: a scoping review.

Pancreatic ductal adenocarcinoma (PDAC) presents as a cancer with an especially poor prognosis, largely due to the challenges surrounding its early di...