Breast Cancer

Diagnostic Accuracy of Artificial Intelligence for Predicting MGMT Promoter Methylation in Glioblastoma Using MR Imaging: A Systematic Review.

PURPOSE: Glioblastoma (GBM) is an aggressive brain tumor with poor prognosis. O6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation is a critical biomarker for guiding chemotherapy decisions, yet current testing requires invasive tissue sampling. This study aimed to systematically evaluate the diagnostic accuracy of artificial intelligence (AI) models using MRI for non-invasive predic...

Deciphering lung adenocarcinoma heterogeneity: a multi-omics approach reveals nuclear division fibroblasts as prognosticators and therapeutic targets.

BACKGROUND: Lung adenocarcinoma (LUAD) is a predominant contributor to cancer‑related mortality globally. Lung‑associated fibroblasts (LAFs) are intricately linked to tumorigenesis and the tumor microenvironment (TME), but their heterogeneity and prognostic relevance in LUAD remain incompletely understood. This study aimed to systematically characterize LAF subsets across the spectrum of pulmonary...

AI-assisted tumor board decision-making in pancreatic oncology.

BACKGROUND: Pancreatic cancer requires nuanced, multidisciplinary treatment planning typically conducted within tumor boards. While Large Language Mod...

Novel genomic risk stratification model for primary high-grade malignant peripheral nerve sheath tumor (MPNST).

Risk stratification across the three main clinical subsets of malignant peripheral nerve sheath tumor (MPNST), neurofibromatosis type I (NF1-related),...

Contrasting roles of PD-L1 expression in tumor and stroma: Biological and prognostic implications in esophageal squamous cell carcinoma.

Programmed death-ligand 1 (PD-L1) plays a central role in immune regulation in esophageal squamous cell carcinoma (ESCC) and has been widely used as a...

Stereotactic Body Radiotherapy for Spinal Metastases: Outcomes and Development of a Risk Assessment Score to Predict Local Tumor Progression.

BACKGROUND AND OBJECTIVES: Stereotactic body radiotherapy (SBRT) has emerged as an effective treatment modality for spinal metastases. However, high-p...

Classification of histological types in ovarian cancer using deep learning.

BACKGROUND: Histological typing of carcinomas is crucial considering the varying progression, prognosis, and treatment efficacy. Clear cell and mucino...

A programmable matrix-robust plasmonic MetaRing biosensor for rapid SERS-based chemotherapeutic response profiling.

Rapid assessment of chemotherapeutic response is essential for precision oncology but remains hindered by tumor heterogeneity and complex biological m...

Second-line chemotherapy after gemcitabine plus nab-paclitaxel in metastatic pancreatic cancer: comparative outcomes and AI-guided treatment selection.

BACKGROUND: International guidelines recommend 5FU/LV, Nal-IRI + 5FU/LV, FOLFIRI, FOLFOX, or (m)FOLFIRINOX as second-line (2 L) chemotherapy for patie...

177Lu-PSMA dosimetry made easy: from theory to practical implementation.

177Lu-PSMA-targeted radioligand therapy (TRT) represents a major advance in managing metastatic castration-resistant prostate cancer (mCRPC), exploiti...

Antibody-drug conjugate design and mechanisms of action for cancer treatment: state of the art and beyond.

Antibody-drug conjugates (ADCs) are a leading area of targeted cancer therapeutics, typically combining a tumour-associated antigen-specific antibody ...

Detecting Distant Metastases in Prostate Cancer Using Whole-body MR Imaging Together with DWIBS (Diffusion-weighted Imaging with Background Body Signal Suppression).

Whole-body MRI (WB-MRI) has evolved over the past 2 decades as a noninvasive imaging technique for detecting distant metastases in prostate cancer. Si...

Explainable artificial intelligence for personalized prognosis in pancreatic cancer: A nationwide study from Taiwan.

Pancreatic cancer is highly aggressive with poor outcomes; current artificial intelligence (AI) prognostic models often lack interpretability and unde...

Effects of similarity networks in graph-based multi-omics classification.

Accurate classification of disease subtypes is a fundamental requirement of precision medicine especially for complex and heterogeneous conditions suc...

Veteran oropharyngeal cancer outcomes in the modern era: a multi-institutional retrospective analysis.

OBJECTIVE: To define oncologic outcomes in Veterans in the modern era using a multi-institutional cohort designed to support development and validatio...

Pretreatment Radiation Esophagitis Prediction Using Quantum Machine Learning in Patients With Esophageal Cancer.

PURPOSE: To introduce a hybrid quantum-classical machine learning approach and validate its feasibility and accuracy for pretreatment radiation-induce...

The role of clinical competency-based education in training in radiation science: A scoping review.

BACKGROUND: Clinical competency-based education (CBE) has emerged as a critical strategy to enhance workforce readiness in radiation sciences. Despite...

AI-enhanced precision: boosting pathologist confidence in HER2 ultra-low diagnosis through intelligent re-screening.

Accurate assessment of Human Epidermal Growth Factor Receptor 2 (HER2) immunohistochemistry (IHC) expression, particularly the precise identification ...

Innovative integration of molecular docking and machine learning for drug discovery: from virtual screening to nanomolar inhibitors.

This review highlights our group's systematic approach to integrating molecular docking, pharmacophore modeling, and machine learning methodologies fo...

A deep learning-based framework for patient-specific radiation dose prediction in beta-emitting radionuclide therapies.

OBJECTIVE: Conventional image-based models for radionuclide therapy dosimetry are typically radionuclide-specific and rely on nuclear medicine (NM) im...