Pathology

Engineered Living Systems With Self-Organizing Neural Networks: From Anatomy to Behavior and Gene Expression.

A great deal is known about the formation and architecture of biological neural networks in animal models, which have arrived at their current structure-function relationship through evolution by natural selection. Little is known about the development of such structure-function relationships in a scenario where neurons are allowed to grow within evolutionarily-novel, motile bodies. Previous work ...

vEMRec: High-Resolution Volume Electron Microscopy Reconstruction Based on Structure-Preserving and High-Fidelity 3D Alignment.

Three-dimensional (3D) alignment is a key step in volume electron microscopy (vEM), aimed at addressing misalignment during data acquisition, thereby recovering the correct biological structures. However, automated 3D alignment has long been challenged by the dilemma between eliminating nonlinear distortions and capturing natural morphological variations inherent to biological specimens. Here, we ...

Pericoronary Adipose Tissue Imaging: Quantitative Assessment, Artificial Intelligence Integration, and Therapeutic Modulation.

Pericoronary adipose tissue (PCAT) is increasingly recognised as a biosensor of vascular inflammation. The guideline-driven widespread adoption of cor...

Artificial intelligence differentiates prefibrotic primary myelofibrosis with thrombocytosis from essential thrombocythemia using digitized bone marrow biopsy images.

Prefibrotic primary myelofibrosis (prePMF) and essential thrombocythemia (ET) are distinct myeloproliferative neoplasms (MPNs) with overlapping clinic...

Multimodal ultrasound and artificial intelligence for characterization of thyroid nodules in Hashimoto's thyroiditis: current challenges and future perspectives.

Patients with Hashimoto's thyroiditis (HT) frequently present with concurrent nodular lesions such as nodular goiter and thyroid cancer (especially pa...

Machine Learning Prediction of Recurrence in Pediatric Thyroid Cancer: Malignant Endocrine Tumors Cohort Analysis Using XGBoost and SHAP.

CONTEXT: Pediatric differentiated thyroid carcinoma (DTC) often presents with advanced disease but generally has excellent long-term survival. However...

Identifying a better-prognosis pancreatic cancer from its benign inflammatory mimic: a machine learning approach with contrast-enhanced ultrasound for early intervention.

PURPOSE: Hypervascular pancreatic ductal adenocarcinoma (PDAC) and mass-forming pancreatitis (MFP) represent a classic diagnostic mimicry on contrast-...

Vision transformer-based diagnosis of psoriasis and eczema in whole-slide histology.

Psoriasis and eczema are chronic inflammatory skin diseases with overlapping histopathological features, which often lead to diagnostic uncertainty ev...

Fat-containing soft-tissue tumors: Imaging findings and pathologic correlation.

Fat-containing soft-tissue tumors encompass a broad spectrum of entities, ranging from indolent lipomas to aggressive liposarcomas, many of which shar...

Machine learning based classification of aggressive and malignant renal tumors from multimodal data.

This study aimed to develop and evaluate a machine learning pipeline using multiphase contrast-enhanced CT images and clinical data to classify renal ...

Driver gene mutations and clinical features predict bone metastasis risk in NSCLC: a logistic regression model.

Bone metastasis affects approximately 40% of patients with non-small cell lung cancer (NSCLC), significantly impacting patient survival and prognosis....

TADynFed: Dynamic modality-adaptive federated learning with tissue-aware disentanglement for cross-disease analysis.

Federated learning (FL) enables collaborative medical image analysis across decentralized institutions while preserving data privacy. However, real-wo...

Dual-Stream Deep Feature and Cell Phenotype Fusion Model for the Diagnosis of Myeloproliferative Neoplasms.

Diagnosing myeloproliferative neoplasms (MPNs) is challenging due to the nuanced and overlapping clinical manifestations of the various subtypes. Prec...

Non-invasive screening for ovarian cancer by combining serum SERS with interpretable machine learning models.

Early identification of malignant ovarian tumors is critical for informing treatment decisions and enhancing patients' quality of life. As the third m...

Fusing data from CT deep learning, CT radiomics and peripheral blood immune profiles to diagnose lung cancer in a cohort of patients experiencing symptoms.

BACKGROUND: Lung cancer is the leading cause of cancer-related deaths. Diagnosis at late stages is common due to the largely non-specific nature of pr...

Spatial and single-cell transcriptomic atlas of human suprachiasmatic nucleus.

The suprachiasmatic nucleus (SCN) is considered the master pacemaker of the circadian clock in mammals, but our current knowledge of the SCN is mostly...

Performance of Radiologist in Interpretation of Non-mass Lesions Detected by Automated Breast Ultrasound: With and Without Commercially Available AI.

OBJECTIVES: To evaluate the performance of radiologists with artificial intelligence (AI)-based computer-aided detection (CAD) systems on automated br...

Deep Learning Integration of Endoscopic Ultrasound Features and Serum Data Reveals LTB4 as a Diagnostic and Therapeutic Target in ESCC.

BACKGROUND: Early diagnosis and accurate prediction of treatment response in esophageal squamous cell carcinoma (ESCC) remain major clinical challenge...

Dual-Parallel Artificial Intelligence Framework for Breast Cancer Grading via High-Intensity Ultrasound and Biomarkers.

BACKGROUND: Accurate and noninvasive breast cancer grading and therapy monitoring remain critical challenges in oncology. Traditional methods often re...

AI portal tract detection and characterisation for a regional analysis of steatosis and inflammation in MASLD, MASH and AIH.

AIMS: Annotation of liver biopsies for disease staging is increasingly aided by digital pathology; however, existing systems do not quantify inflammat...