Other Cancers

A novel computational framework for tumor-specific T cell antigen identification using a deep neural network.

Identifying tumor-specific T-cell antigens is essential for advancing cancer immunotherapy and enabling precision-driven, AI-assisted discovery. While artificial intelligence (AI) and machine learning (ML) have significantly impacted healthcare and biotechnology, existing approaches often struggle with the inherent complexity and sequence dependency of antigen data, resulting in suboptimal predict...

Deep learning-based automated detection of oral squamous cell carcinoma in histopathological images: a comparative study of five CNN architectures.

Oral squamous cell carcinoma (OSCC) is the most common malignancy of the oral cavity, and early diagnosis plays a crucial role in improving patient prognosis and survival rates. Histopathological examination remains the gold standard for OSCC diagnosis; however, this process is time-consuming and highly dependent on expert interpretation. With the rapid development of digital pathology and artific...

Machine learning-based identification of basement membrane-related signature to predict recurrence and immunotherapy benefit in bladder cancer.

The basement membrane (BM) plays a critical role in regulating bladder cancer (BC) progression. However, a BM-related signature for predicting BC recu...

A radio-pathological fusion model for predicting PD-L1 expression and immunotherapy response in non-small cell lung cancer.

OBJECTIVE: This study aims to construct a multimodal fusion model (FM) based on CT and hematoxylin and eosin (H&E) stained slices to predict the PD-L1...

Integrating machine learning-based molecular design with experimental validation for the discovery of EGFR inhibitors in lung cancer.

The emergence of drug resistance and off-target toxicities in epidermal growth factor receptor (EGFR) targeted therapies underscores the urgent need f...

E2E-OCT: end-to-end joint learning model using optical coherence tomography images for vocal cord leukoplakia diagnosis.

In our previous study, a home-built handheld OCT system was used to collect OCT images in vocal cord leukoplakia. First, 383 valid OCT images were col...

DrugDL: Dual-modal deep learning framework for multi-property drug prediction and targeted therapy discovery.

MOTIVATION: The accurate and robust representation of drug molecule features, the prediction of drug-target biomacromolecule interactions, and the det...

Navigating the metabolic crossroads: A bibliometric analysis of the SLC7A11/xCT field from oxidative stress to ferroptosis and disulfidptosis.

The cystine/glutamate transporter SLC7A11 is a central node in regulated cell death and tumor metabolism. Here, we performed a bibliometric analysis o...

Heterogeneous developmental trajectories and predictors of self-harm in early adolescence: A machine learning study.

BACKGROUND: Although prior studies have examined developmental trajectories of adolescent self-harm in terms of frequency and severity, a fundamental ...

A myeloid immunosuppressive phenotype defines primary refractoriness to atezolizumab Plus bevacizumab in hepatocellular caarcinoma.

BACKGROUND: Despite improved outcomes with atezolizumab plus bevacizumab (A+B) in hepatocellular carcinoma (HCC), primary refractoriness (PRef), chara...

Multimodal multitask deep learning for grading management system in non-small cell lung cancer.

Accurate histologic subtyping, tumor node metastasis classification (TNM) staging and prognostic assessment are central to clinical management of non-...

Robust artificial intelligence frameworks for lung cancer subtyping and malignancy detection on thoracic CT.

Accurate characterization of thoracic malignancies on computed tomography (CT) remains challenging because histological subtype differentiation and no...

Effectiveness of Artificial Intelligence in the Automatic Diagnosis of Lesions Detected in Panoramic Radiographs.

This study was aimed at evaluating the effectiveness of artificial intelligence (AI) in detecting jaw cysts and tumors, analyzing lesion content, and ...

Longitudinal validation of ENLIGHT, an AI predictor of immunotherapy response and resistance, in pan-cancer cohorts.

Artificial intelligence (AI) algorithms such as ENLIGHT and DeepPT represent promising approaches to identify predictive biomarkers for immune checkpo...

Deep learning analysis of breast cancer histology predicts ATM pathogenic variant carrier status.

We performed deep learning analysis of histopathological whole-slide (full-face) images (WSI) to predict ATM pathogenic or likely pathogenic variant (...

Non-invasive imaging diagnostic techniques in metabolic dysfunction-associated steatotic liver disease: a roadmap for clinicians.

Metabolic dysfunction-associated steatotic liver disease (MASLD), previously termed nonalcoholic fatty liver disease (NAFLD), is the most prevalent ch...

Proteomics as a Theranostic Compass in BCR::ABL1-Negative Myeloproliferative Neoplasms: Integrating Biomarker Discovery with Therapeutic Stratification.

Classic BCR::ABL1-negative myeloproliferative neoplasms (MPNs)-polycythaemia vera, essential thrombocythaemia, and primary myelofibrosis-are clonal ha...

A comparative study of machine learning models for microbiome-based diagnosis and multi-class staging of colorectal cancer.

Colorectal cancer (CRC) is closely associated with gut microbiota dysbiosis; however, comprehensive benchmarking of machine learning models that integ...

Oncotype DX: Clinical Utility, Evidence, and Future Trends in Personalized Breast Cancer Management.

The Oncotype DX assay has revolutionized the management of early-stage, hormone receptor-positive, HER2-negative breast cancer. Developed in 2004, it ...

Machine Learning-Optimized Single-Atom Catalysts Enable Microenvironment-Adaptive Chemodynamic-Bioorthogonal Cancer Therapy.

Chemodynamic therapy (CDT), which harnesses endogenous chemical energy within the tumor microenvironment (TME), has shown high potential for precise c...