Dermatology

Explainable machine learning identifies features and thresholds predictive of immunotherapy response.

The identification of reliable predictors for immunotherapy response remains a critical challenge in precision oncology. Here, we integrated patient clinical features with DNA and RNA sequencing data derived from 229 melanoma samples. We use 138 melanoma samples to develop and train machine learning models predicting patient treatment response. Feature selection included a univariate Mann-Whitney ...

Clinically reliable and stable automated segmentation of DLBCL lesions on PET/CT using self-configuring nnU-Net for robust TMTV quantification.

PURPOSE: Accurate lesion segmentation on 18F-FDG PET/CT is essential for the effective management of Diffuse Large B-cell Lymphoma (DLBCL). While deep learning architectures, particularly U-Net and its variants, are widely used, identifying the most reliable and robust framework for clinical quantification remains a challenge. This study aims to perform a systematic, three-way comparative evaluati...

Curcumin alleviates depression-aggravated psoriasis by targeting MMP9-regulated apoptotic pathways.

Depression and psoriasis are highly comorbid, yet their shared molecular mechanisms remain poorly understood. Utilizing cross-sectional data from NHAN...

Integrative single-cell and bulk transcriptomic analysis identifies lesion-associated gene signatures for prognostic stratification and therapeutic guidance in head and neck squamous cell carcinoma.

BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) is a highly heterogeneous malignancy with poor prognosis and variable response to therapy. E...

Recent Advances in Imaging of Demyelinating and Dysmyelinating White Matter Lesions.

White matter diseases encompass a heterogeneous spectrum of central nervous system disorders, with neuroimaging serving a pivotal role in their diagno...

A rapid total-body PET imaging approach for pediatric patients using non-attenuation-corrected PET scans.

BACKGROUND: Pediatric lymphoma patients undergo multiple 18F-FDG PET/CT examinations for staging and response assessment, raising concerns about cumul...

Research on multi-stage deep learning based intelligent diagnosis of skin diseases and skin medicine diagnosis community construction concept.

Accurate diagnosis and assessment of the severity of skin diseases are essential for appropriate clinical treatment. This paper proposes a multi-stage...

Reliable Enough? Benchmarking LLMs for Clinical Concept Extraction.

Language models (LMs) offer promise for automating concept extraction, a task traditionally reliant on manual curation and earlier natural language pr...

Challenges of Ontology-Based Concept Normalization for Deep Phenotyping in Rare Skin Diseases.

Extracting and standardizing phenotypic information from free-text medical reports remains a main challenge in biomedical natural language processing ...

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...

Novel post-translational modification learning signature reveals DPP3 promotes progression of psoriasis by enhancing inflammatory activation.

BACKGROUND: Although post-translational modifications (PTMs) significantly influence the pathogenesis of psoriasis, their specific mechanisms remain p...

"Bioprinting of Hair: How Far We Proceeded and What Needs to be Done".

Alopecia is a common dermatologic disorder with psychological and quality-of-life impact. Current options such as PRP, topical agents, microneedling o...

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 ...

Development and validation of a novel YOLOv5-based artificial intelligence model for gastric mucosal lesion detection.

BACKGROUND AND AIMS: Artificial intelligence (AI) has been widely used in endoscopic diagnosis; however, an AI model capable of comprehensively diagno...

Immune Checkpoint Inhibitor-Associated Cardiovascular Toxicity: Mechanisms, Clinical Manifestations, and Clinical Impact.

Immune checkpoint inhibitors (ICIs) represent a class of novel anticancer agents that enhance T cell-mediated recognition and elimination of tumor cel...

Xiao Zhen Fang mitigates cutaneous toxicity by inhibiting PANoptosis: Insights from untargeted metabolomics and transcriptomics.

BACKGROUND: Epidermal growth factor receptor (EGFR) inhibitors and other targeted therapies frequently cause cutaneous toxicities, impairing patient q...

Development and Validation of a Deep Learning-enabled Single Breath-hold Abbreviated MRI Protocol for Hepatocellular Carcinoma Diagnosis.

Purpose To develop a deep learning-enabled single breath-hold abbreviated MRI (DL-SBH-aMRI) protocol for hepatocellular carcinoma (HCC) diagnosis. Mat...

Enhancing esophageal cancer detection using a deep learning framework and a novel spectrum-aided vision enhancer for virtual narrow band imaging.

Esophageal cancer is a highly aggressive malignancy where early detection is critical for survival. However, early-stage lesions typically present sub...

Evaluation and enhancement of the prognostic ability of the eighth edition of TNM staging in cutaneous malignant melanoma: a population-based study, using machine learning, of 111 871 patients.

BACKGROUND: Melanoma is the fifth most common cancer in the USA and the UK, with global incidence on the rise. The TNM staging system guides treatment...

Malignancy Prediction of Breast Lesion with Calcifications via Radiomics Analysis Based on Intratumoral and Peritumoral Regions of Ultrasound Images.

RATIONALE AND OBJECTIVES: To evaluate whether intratumoral habitats and peritumoral regions on ultrasound (US) images enhance radiomics-based diagnosi...