Lung Cancer

CT-based deep learning radiogenomics for predicting key glioma genotypes (IDH, ATRX, EGFR, TP53).

PURPOSE: Molecular subtyping guides diagnosis and targeted therapy for gliomas. Although MRI-the current imaging standard-can be time-consuming and is sometimes contraindicated, computed tomography (CT) is faster, more widely available, and often preferable in emergency and resource-limited settings. We evaluated whether CT-based radiogenomic signatures combined with machine learning could accurat...

Low-kVp techniques for radiation dose optimization in abdominopelvic contrast-enhanced CT: a scoping review.

Contrast-enhanced computed tomography (CECT) of the abdomen and pelvis is widely used for diagnostic imaging but contributes substantially to cumulative medical radiation exposure. Low tube voltage (kVp) imaging has gained attention as a practical strategy for radiation dose optimization while maintaining diagnostic image quality.The study aimed to map the current evidence on low-kVp techniques fo...

Evaluating the VOCORDER device for early disease detection through breath analysis: study protocol for a two-phase clinical study.

INTRODUCTION: The survival rate of patients with life-threatening diseases primarily depends on the speed of diagnosis. Too often, diseases are detect...

ViGNet: A clinical data-supported deep learning approach for NSCLC immunotherapy response prediction in digital pathology.

Histopathology is the cornerstone of oncology diagnosis, while whole-slide images (WSIs) enable the transition to digital, quantitative pathology. Lev...

Prospective germline exome and machine learning-based risk score identify predictive and prognostic biomarkers of immunotherapy outcomes in advanced non-small cell lung cancer.

BACKGROUND: Immune checkpoint inhibitors (ICIs) have transformed the treatment landscape of advanced non-small cell lung cancer (NSCLC). However, a su...

A Foundation Model Based CT Biomarker for Non-Invasive Prediction of Response to Neoadjuvant Immunochemotherapy in Non-Small Cell Lung Cancer.

Predicting pathological complete response (pCR) to neoadjuvant immunochemotherapy in non-small cell lung cancer (NSCLC) is clinically important yet re...

Quantifying the AI readiness gap: An international, multidisciplinary assessment of artificial intelligence literacy in the radiation oncology community.

PURPOSE: The rapid integration of artificial intelligence (AI) into imaging-intensive fields like radiation oncology (RO) is transforming the clinical...

Precision Medicine Gene Network Analyser: part I-cancer driver gene identification through network topology and ensemble machine learning.

PURPOSE: Precision oncology depends on identifying cancer driver genes and linking them to targeted therapies. Current methods using curated gene sets...

Machine learning assessment of pathologic response in lung cancer resections after neoadjuvant therapy - IASLC MPR Project.

INTRODUCTION: Machine learning algorithms may improve efficiency and accuracy of pathologic response (PR) assessment in surgically resected lung cance...

Organ-Specific Cancer-Associated Fibroblast Subtypes Across the Digestive System Linked to Cancer Hallmarks.

Cancer-associated fibroblasts (CAFs) are major stromal components of the tumor microenvironment (TME) and play diverse roles in gastrointestinal (GI) ...

In-Depth Plasma Proteomics Identifies SNCA as a Discriminating Biomarker of PDAC.

Pancreatic ductal adenocarcinoma (PDAC) carries a poor prognosis largely due to lack of efficient diagnostic means. We applied mass spectrometry-based...

Integrative multi-omics analysis identifies a circadian rhythm-associated gene signature for prognosis and therapeutic stratification in lung adenocarcinoma.

BACKGROUND/OBJECTIVES: Circadian rhythm disruption is increasingly implicated in tumor progression and therapy resistance. However, its prognostic val...

Predicting Bacterial Radiation Resistance Through Identifying Novel Genomic Features.

Radiation resistance in bacteria is a critical trait with implications for biotechnology, medicine and environmental science. Deinococcus species poss...

Automatic framework for PD-L1 expression evaluation in Latino patients with non-small cell lung cancer.

Programmed death ligand-1 (PD-L1) expression is a key biomarker for identifying non-small cell lung cancer (NSCLC) patients eligible for immunotherapy...

Rapid autofluorescence based 3D optical imaging of the pancreatic cancer milieu at mesoscopic scale - stain-free volumetric segmentation.

Although major advances have been made in the field of mesoscopic imaging and associated tissue clearing protocols, these applications are greatly cha...

Explainable artificial intelligence in prostate cancer treatment recommendation: A decision support system for oncological expert panels.

BACKGROUND: Therapeutic decisions in clinical oncology are commonly established through interdisciplinary consensus in multidisciplinary cancer confer...

Assessing Image Quality and Diagnostic Performance of Quadruple-Low Coronary CT Angiography with Deep Learning in High-BMI Patients.

RATIONALE AND OBJECTIVES: To examine the feasibility of a quadruple-low protocol in coronary computed tomography angiography (CCTA) assisted by the de...

Deep learning based histomorphological phenotyping and prognostic stratification for combined SCLC and LCNEC.

Prognostic stratification for combined small and large cell neuroendocrine lung carcinoma (cSCLC-LCNEC) remains challenging. We introduce GTBIS, an in...

Machine-learning prediction of 30-day infection-related hospitalization in advanced CKD.

BACKGROUND: Patients with advanced chronic kidney disease (CKD), defined as having an estimated glomerular filtration rate (eGFR) < 45 ml/min/1.73 m²,...

ABCC2 as a novel therapeutic target in lung adenocarcinoma: a machine learning-driven discovery linking ammonia metabolism to prognosis and drug resistance.

BACKGROUND: Ammonia, long regarded as a metabolic waste product, has recently been recognized as a pivotal oncometabolite in the tumor microenvironmen...