Lung Cancer

CTSB-positive tumor-associated macrophages shape prognosis and therapeutic response in lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD) remains a major clinical challenge in assessment of clinical outcomes and therapeutic response. Although tumor-associated macrophages (TAMs) are known as crucial regulators of tumor progression, their heterogeneity and prognostic relevance in LUAD have not been fully elucidated. METHODS: The heterogeneity of TAMs was detected by integration analyses of single...

C2HFusion: Clinical context-driven hierarchical fusion of multimodal data for personalized and quantitative prognostic assessment in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy. Accurate prognostic modeling enables reliable risk stratification to identify patients most likely to benefit from adjuvant therapy, thereby facilitating individualized clinical management and potentially improving patient outcomes. Although recent deep learning approaches have shown promise in this area, their effectivenes...

Interpretable machine learning-based prediction of liver metastasis risk in elderly patients with small cell lung Cancer: A study based on the SEER database and external validation in a Chinese cohort.

PURPOSE: Small cell lung cancer (SCLC) is a highly aggressive malignancy with a high incidence of liver metastases, particularly among elderly patient...

Machine learning-driven water quality index prediction in the Dau Tieng reservoir, southern Vietnam, with interpretability via SHapley additive exPlanations: A vision for water quality management strategies.

Freshwater reservoirs are essential for ecological stability, biodiversity preservation, and resource sustainability. Managing water quality effective...

Uncovering the potential of pathomics: prognostic prediction and mechanistic investigation of pancreatic cancer.

A machine learning-based pathomics model was investigated for its value and biological significance in predicting overall survival (OS) after surgery ...

MRI-to-PET synthesis via deep learning for amyloid-β quantification in Alzheimer's disease.

OBJECTIVES: Amyloid-β (Aβ) PET is crucial for diagnosing and monitoring Alzheimer's disease (AD), but its high cost and radiation exposure limit its u...

3D tibial HU reconstruction from biplanar X-rays utilizing a hybrid PCA-CNN framework.

High-resolution Computed Tomography (CT) is the gold standard medical imaging technique for bone assessment. However, its clinical use is limited by h...

Automated CT-derived body composition predicts pathologic response to neoadjuvant immunotherapy in non-small cell lung cancer.

Tumor-intrinsic biomarkers alone insufficiently predict pathological complete response (pCR) to neoadjuvant immunochemotherapy (NICT) in non-small cel...

Advancements in radiation dose reduction for pediatric CT head Imaging: A scoping review of emerging Technologies, Protocols, and optimization strategies.

Computed tomography (CT) is an important imaging modality that provides cross-sectional images, aiding in the detailed visualization of internal struc...

[Chronic skin damage from UV radiation].

Ultraviolet (UV) radiation is the primary risk factor for the development of both melanocytic and nonmelanocytic skin cancer. In particular, UVA and U...

Single‑cell mapping of neutrophil extracellular trap signatures in lung adenocarcinoma reveals immune landscapes, prognostic potential, and therapeutic targets.

Neutrophil extracellular traps (NETs) have emerged as key modulators in the tumor microenvironment, yet their cellular heterogeneity, molecular mechan...

Deep learning image reconstruction improves 40 keV virtual monoenergetic image quality in rectal cancer.

BACKGROUND: Accurate preoperative evaluation of rectal cancer is essential for staging and treatment planning. Low-energy virtual monoenergetic imagin...

Artificial intelligence in diagnostic, prognostic, and predictive genomic biomarkers for prostate cancer: Ready for prime time?

INTRODUCTION: Until recently, the widespread use of genetic markers in prostate cancer (PCa) has been limited by the complexities and cost of genomic ...

Anomaly detection in gamma-ray spectra using autoencoders with small form factor CZT detectors.

The detection of weak radioactive sources in fluctuating background environments is a critical task for nuclear security, environmental monitoring, an...

Unraveling the molecular mechanisms of air pollution-induced lung adenocarcinoma through machine learning, network toxicology, and molecular docking.

OBJECTIVE: Airborne environmental contaminants are established carcinogens. This investigation elucidates the mechanistic contributions to pulmonary a...

Pectin from Phyllanthus emblica L.: Structure elucidation and synergistic in vitro anti-stomach adenocarcinoma effects with 5-fluorouracil.

In recent years, polysaccharides with potential anticancer activity have attracted widespread attention. In this study, a homogeneous polysaccharide f...

Diagnostic accuracy of ¹⁸F-FDG PET/CT radiomics for non-invasive prediction of PD-L1 expression in non-small cell lung cancer: A systematic review and meta-analysis.

To evaluate the diagnostic performance, methodological quality, and clinical feasibility of ¹⁸F-FDG PET/CT-based radiomics machine learning models for...

Causal Inference and Prognostic Modeling in Colorectal Cancer through Integration of Computational Pathology and Mendelian Randomization.

BACKGROUND: While traditional pathology supports the diagnosis and staging of colorectal cancer (CRC), computational pathology provides novel prognost...

Proteomic Profiling of Plasma Extracellular Vesicles Combined with Multivariate Modeling Identified Potential Biomarkers for Distinguishing Benign Pulmonary Nodules from Early-Stage Lung Adenocarcinoma.

Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer and is difficult to distinguish from benign pulmonary nodules (BPNs), particularl...

Ensemble learning-based radiomics model for predicting radiation-induced temporal lobe injury in nasopharyngeal carcinoma.

PURPOSE: To develop and validate a multimodal ensemble machine learning model integrating multi-sequence magnetic resonance imaging (MRI) radiomics, c...