Oncology/Hematology

TCR sequencing in cancer immunology and immunotherapy: what, when, where, why, and how.

T-cell receptors (TCRs) are generated through somatic recombination of variable (V), diversity (D), and joining (J) gene segments, resulting in an extraordinarily diverse receptor repertoire that is essential for immune surveillance and host defense. TCR sequencing (TCR-seq) has emerged as a powerful tool for comprehensive characterization of the adaptive immune repertoire, offering deep insights ...

A high density of T-cell lymphocytes and Tregs subset correlate to a worse survival in major salivary gland carcinomas.

Salivary gland carcinomas (SGC) are rare and heterogeneous tumors with limited therapeutic options in advanced stages. Recent evidence suggests a potential role of the tumor immune microenvironment (TME) in disease progression. This study aimed to investigate the immune profile of SGCs by analyzing tumor-infiltrating lymphocytes (TILs), tumor-associated macrophages (TAMs), and PD-L1 expression, an...

A domain-adaptive deep contrastive network for magnetic resonance imaging-driven bladder cancer classification.

Bladder cancer is one of the most prevalent malignancies of the urinary system and is associated with high morbidity and mortality. With advances in m...

Clinical value of ultra-low-dose chest CT with deep learning image reconstruction in the detection and characterization of pulmonary nodules.

OBJECTIVE: To evaluate the clinical value of ultra-low-dose CT (ULDCT) with deep learning image reconstruction (DLIR) in the diagnosis of pulmonary no...

GSDME-mediated pyroptosis modulates the immunosuppressive microenvironment in lung adenocarcinoma.

Despite the increasing recognition of pyroptosis, particularly that involving GSDME, its precise impact on tumor prognosis and the immune microenviron...

Gene driven analytical learning model for accurate breast cancer diagnosis.

Patients diagnosed with breast cancer exhibit a diverse range of prognostic outcomes due to the varied nature of the disease across different patient ...

Integration of circulating tumor DNA data enhances lung cancer prediction in patients with COPD.

Patients with chronic obstructive pulmonary disease (COPD) are at increased risk of lung cancer, and the identification of circulating tumor DNA (ctDN...

Circulating tumor cells (CTCs) enumeration and machine-learning based diagnostic biomarkers for breast cancer detection.

BACKGROUND: Circulating tumor cells (CTCs) are detectable in early-stage cancer and may enable early cancer detection. We evaluated a CTC-based assay ...

Integrative multi-omics analysis reveals metabolic dysfunction signatures as critical determinants of prostate cancer prognosis and immunosuppressive microenvironments.

BACKGROUND: Metabolic reprogramming is a hallmark of cancer. However, the precise mechanisms by which specific metabolic pathways drive prostate cance...

Machine learning models based on tumoral, 4 mm-peritumoral, and combined radiomic features for evaluating the invasiveness of lung adenocarcinoma manifesting as ground-glass nodules.

Neutrophil extracellular trap-related genes in PTCL: identification, prognosis and drug interaction prediction via bioinformatics-machine learning.

OBJECTIVE: This study aimed to identify neutrophil extracellular trap-related genes (NET-RGs), explore their prognostic significance, and predict drug...

Multi-omics profiling reveals cholic acid-mediated immunosuppression driven by peritumoral ductular reactions in hepatocellular carcinoma.

Hepatocellular carcinoma remains a leading cause of cancer mortality worldwide, with peritumoral microenvironment interactions playing a critical role...

Di-(2-ethylhexyl) terephthalate promotes breast cancer progression: Multi-omics integrated experimental validation.

Di-(2-ethylhexyl) terephthalate (DOTP), as an alternative to phthalate plasticizers, has been widely used in sensitive fields such as food packaging a...

18F-FDG PET/CT and receptor-positive circulating tumor cells-based machine learning model for predicting poorly differentiated lung adenocarcinoma.

INTRODUCTION: This study aimed to develop and validate a machine learning model that integrates radiomic features from 2-[18F]fluoro-2-deoxy-D-glucose...

Machine learning approaches to optimize the integration of sociodemographic factors for predicting cancer-specific survival among patients with high-risk prostate cancer.

BACKGROUND: Sociodemographic factors influence the outcomes of prostate cancer (PCa); however, they are rarely incorporated into clinical risk predict...

Artificial Intelligence Integration in Radiation Therapy Education: A Multi-Modal Approach.

INTRODUCTION: Critical workforce shortages in radiation oncology have led tertiary institutions to rapidly expand their radiation therapy (RT) student...

Application of a Natural Language Processing Framework for Data Extraction From Pathology Reports Across Multiple Cancer Types.

BACKGROUND: Pathological reports provide comprehensive insights into the clinical and pathological features of different cancer types. However, extrac...

Advances and challenges of PET imaging in bladder cancer-An update and future trends.

Molecular imaging with positron emission tomography (PET) is a powerful tool in the clinical management of bladder cancer, providing functional inform...

Minimally invasive surgery for advanced-stage ovarian cancer: a review of evidence and emerging paradigms.

Minimally invasive surgery has emerged as a promising approach to the management of advanced-stage epithelial ovarian cancer, particularly in the sett...

Cluster-Based MR Radiomics Model for Predicting Induction Chemotherapy Response in Nasopharyngeal Carcinoma.

RATIONALE AND OBJECTIVES: To develop a cluster-specific magnetic resonance (MR) radiomics model for predicting induction chemotherapy (ICT) response i...