Pathology

SA-TransU²Net: a rock thin section grain segmentation network based on multi-scale RSU and global context enhancement.

Rock thin section segmentation serves as a critical technical pillar for reservoir characterization in oil and gas exploration. It enables the effective identification of reservoir lithological characteristics, determination of diagenetic types, and quantification of pore structures, thereby providing essential microscopic evidence for reservoir quality assessment, oil-bearing prediction, and expl...

AI-based organ weight estimation from postmortem computed tomography.

Accurate organ weight determination is essential in forensic autopsy. Postmortem computed tomography (CT) combined with Artificial Intelligence (AI)-based segmentation allows automated, non-invasive organ delineation. This study evaluated the accuracy of CT-derived organ weights against conventional autopsy measurements. A total of 100 postmortem CT examinations (mean age 54 ± 21 years; 35 females...

Multi-omics machine learning-driven investigation of super-enhancers signatures and prognostic biomarkers in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is the most common histological subtype of malignant lung tumors, characterized by high incidence and mortality rates. Supe...

Role of artificial intelligence in developing predictive models for major adverse cardiovascular outcomes using CCTA adipose tissue characteristics: a systematic review and meta-analysis.

Cardiovascular diseases remain as a leading cause of mortality and morbidity worldwide, with coronary artery disease (CAD) and its complications, coll...

Explainable Ensemble Learning With Stain Normalization and Deep Feature Extraction for Acute Lymphoblastic Leukaemia Classification.

Acute lymphoblastic leukaemia (ALL), a common form of cancer, remains a life-threatening condition that affects individuals worldwide, including both ...

Toward robust histopathology imaging: An unsupervised framework for artifact detection, localization, and restoration.

Whole Slide Images are central to modern pathology and computational histopathology. However, tissue processing and slide scanning can introduce artif...

Machine learning-based prognostic model and single-cell transcriptomic integration for identifying brain metastasis-associated malignant subpopulations and potential therapeutic targets in lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD) is a highly prevalent and lethal form of lung cancer. Brain metastasis (BrM) is a major cause of mortality in p...

Predictive modeling of soft tissue changes after orthodontic treatment with premolar extractions using frontal facial photographs.

BACKGROUND: The present study aimed to determine the accuracy of machine learning in predicting soft tissue changes after orthodontic treatment after ...

DDI-MMAF: Multi-modal affine fusion of visual and semantic representations for anticancer drug synergy prediction.

Predicting anticancer drug synergy is pivotal for personalizing combination therapies; however, existing deep learning models often rely heavily on co...

Deep learning-empowered Raman spectroscopy for gastric cancer diagnosis: a review of data pipelines and algorithms.

Raman spectroscopy is emerging as a label-free tool for gastric cancer diagnosis by capturing molecular fingerprints of malignant transformation. Howe...

Label-Free Prediction of EGFR Mutation Status Using Fluorescence Lifetime Imaging and Deep Learning in Lung Adenocarcinoma.

UNLABELLED: Accurate prediction of epidermal growth factor receptor (EGFR) mutations is essential for guiding targeted therapy in non-small cell lung ...

Machine learning modeling to predict HCC locations in cirrhotic patients undergoing MRI - a proof-of-concept study.

PURPOSE: Current hepatocellular carcinoma (HCC) surveillance guidelines rely on manually defined LI-RADS (Liver Imaging Reporting and Data System) fea...

A machine learning-guided epithelial plasticity score refines prognostication and immune-context stratification in muscle-invasive bladder cancer.

PURPOSE: Muscle-invasive bladder cancer (MIBC) remains biologically heterogeneous, and clinicopathological variables do not fully capture epithelial-s...

Activated Cancer-Associated Fibroblasts Are Associated with Immunosuppression and Poor Prognosis in Clear Cell Renal Cell Carcinoma.

INTRODUCTION: Clear cell renal cell carcinoma (ccRCC) exhibits substantial heterogeneity within its tumor microenvironment, contributing to variable c...

An advanced AI-driven deep learning framework for early detection and precise diagnosis of breast cancer from medical images.

A malignant disorder known as breast cancer (BC) arises when cells in breast tissue grow out of control, frequently due to genetic, hormonal, or envir...

Serum metabolomics profiling in head and neck squamous cell carcinoma: a multicentre diagnostic study.

BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) is frequently diagnosed at advanced stages, when outcomes are poor. Biomarkers for HNSCC det...

MAPPING THE MTOR PATHWAY IN LUNG TRANSPLANTATION: IS IT TIME FOR A BIOMARKER-DRIVEN PRECISION THERAPY?

Solid organ transplantation has revolutionized the treatment of end-stage diseases, yet long-term graft survival remains constrained by immune-mediate...

Machine Learning for Predicting Ki-67 Expression in Renal Tumors: A Systematic Review And Meta-Analysis.

RATIONALE AND OBJECTIVES: To systematically evaluate the diagnostic performance of machine learning (ML) models for predicting Ki-67 expression in ren...

Novel application of attention-enhanced hybrid efficient net for multi-scale histopathological cancer classification.

Examination of high-resolution whole-slide images requires an analysis of the histopathological images, which is essential in the precise diagnosis of...

A Multimodal, Multitask Prediction Framework for Diagnosis and Prognosis of Clear Cell Renal Cell Carcinoma.

The purpose of this study is to develop and validate a multimodal, multitask prediction framework for clear cell renal cell carcinoma (ccRCC) by integ...