Oncology/Hematology

Lipid Metabolism and Oxidative Stress in Uterine Corpus Endometrial Carcinoma: Prognostic and Immune Landscape Analysis.

OBJECTIVE: Uterine corpus endometrial carcinoma (UCEC) is a common gynecologic malignancy characterized by metabolic reprogramming and immune dysregulation. This study aimed to investigate the prognostic and diagnostic value of lipid metabolism- and oxidative stress-related genes (LMOSGs) in UCEC. METHODS: Transcriptomic and clinical data from The Cancer Genome Atlas and Gene Expression Omnibus we...

Investigation of multimodal deep learning models for predicting ovarian tumor malignancy based on ultrasound images and clinical information - a comprehensive comparative study against readers and O-RADS.

BACKGROUND: As the second deadly cancer affecting women globally, precise and timely classification of ovarian tumors plays an instrumental role in improving the rate of curing and reducing the rate of mortality. This study was set out to comprehensively investigate the effectiveness of deep learning model for classifying benign and malignant ovarian tumors, utilizing multimodal ultrasound images ...

A fully automated CT-based pelvimetry pipeline for quantifying mid-pelvic surgical workspace in rectal cancer.

PURPOSE: Pelvimetry may aid preoperative planning in rectal cancer surgery, yet manual measurements are time-consuming and MRI-based methods require d...

Construction of chronic inflammation and mitochondrial energy metabolism-associated predictive and therapeutic models for lung adenocarcinoma patients.

Integration of multi-omics and machine learning to identify core genes in PANoptosisof lung adenocarcinoma and their mechanisms in the tumor microenvironment and therapeutic potential.

Lung adenocarcinoma (LUAD) is one of the leading causes of cancer-related deaths worldwide, and its complex tumor microenvironment (TME) is a key barr...

Integrative multi-omics identifies a diagnostic T cell signature for cutaneous squamous cell carcinoma.

Cutaneous squamous cell carcinoma (cSCC) involves complex immune interactions. This study aimed to identify a T cell-related gene signature to charact...

Physics-constrained multimodal vision transformer for ultra-short-term solar radiation forecasting error correction.

Accurate ultra-short-term solar radiation forecasting is critical for renewable energy integration and power grid stability, yet operational systems e...

Uncovering Global Trends in Surgical Oncology Trials: Applying Natural Language Processing to ClinicalTrials.gov.

BACKGROUND: Identifying surgical oncology trials within the National Clinical Trial (NCT) database is challenging owing to the absence of medical spec...

LILRB3 inhibition reverses immunosuppression in glioma: a nanoparticle-based therapeutic strategy.

Glioma is a highly aggressive brain tumor characterized by a profoundly immunosuppressive tumor microenvironment dominated by M2-polarized tumor-assoc...

Mapping intratumoral heterogeneity through PET-derived washout and deep learning after proton therapy.

The distribution of produced isotopes during proton therapy can be imaged with Positron Emission Tomography (PET) to verify dose delivery. However, bi...

FDG PET in pulmonary sarcoidosis: An update and future aspects.

Pulmonary sarcoidosis is a heterogeneous granulomatous disease with an unpredictable clinical course, in which accurate assessment of disease activity...

Mapping whole-organism genetic comorbidities across model Species using unified ontologies.

Understanding how genetic variation contributes to organism-wide phenotypes is critical for identifying mechanisms of disease. Here, we present a comp...

Application of transformer-enhanced convolutional neural network: multicenter MRI assessment of muscle invasion in bladder cancer.

OBJECTIVE: Accurate preoperative assessment of muscle invasion in bladder cancer (BCa) guides therapy selection. However, MRI interpretation varies ac...

Dual-layer spectral CT for predicting spread through air spaces in lung adenocarcinoma: a dual-center study.

OBJECTIVES: To investigate the value of machine learning classifiers incorporating dual-layer spectral CT (DLCT) parameters for preoperative predictio...

Integration of machine learning-based screening approaches and single-cell sequencing technique to identify cancer stemness genes associated with radiotherapy resistance in lung adenocarcinoma.

Circulating Immune Profiles in ACTH-Dependent Cushing's Syndrome.

OBJECTIVE: ACTH-dependent Cushing's syndrome (CS) causes profound immune dysfunction and severe infections. This study aimed to characterize immune ce...

A deep learning-based multimodal model with automated body composition analysis predicts prognosis in advanced clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is an aggressive malignancy with a high risk of postoperative recurrence. Body composition has emerged as a pr...

scAgeClock: a single-cell transcriptome-based human aging clock model using gated multi-head attention neural networks.

Aging clock models have emerged as a crucial tool for measuring biological age, with significant implications for anti-aging interventions and disease...

Predicting the biological behavior of cervical squamous cell carcinoma: a machine learning approach using apparent transverse relaxation rate (R2* maps) radiomics nomogram.

Integration of deep learning and radiomic features from multiplex immunohistochemistry images for reproducible Multi-Outcome prediction in a Multi-Center study of colorectal cancer.

OBJECTIVE: To develop and validate a robust, multimodal machine learning framework integrating radiomic and deep learning features from multiplex immu...