Lung Cancer

Enhancing mesenteric vascular imaging with dual-energy CTA: a comparison of DLIR and ASIR-V using low contrast agent and low radiation dose protocols.

BACKGROUND: Timely diagnosis of mesenteric vascular diseases, especially acute mesenteric ischemia (AMI) due to embolism in the superior mesenteric artery (SMA), is crucial for effective intervention. Dual-energy computed tomography angiography (DE-CTA) is a key diagnostic tool; however, concerns about contrast-induced nephropathy and radiation exposure persist. OBJECTIVES: This study assesses the...

A Simulation-Free Radiation Therapy Workflow Using Synthetic Computed Tomography Generated from Diagnostic Magnetic Resonance Imaging for Personalized Hippocampal-Sparing Whole-Brain Treatment.

PURPOSE: The conventional computed tomography (CT)-based consultation to simulation process for hippocampal-sparing whole-brain radiation therapy (HS-WBRT) typically requires several days, delaying treatment initiation for patients who would benefit from expedited care. We developed a simulation-free, magnetic resonance imaging (MRI)-only workflow for HS-WBRT to accelerate treatment start and main...

Artificial intelligence construction: a review of the bridge between CT imaging features of lung ground-glass nodules adenocarcinoma and carcinogenic driver genes.

Lung ground-glass nodules (GGNs) represent a critical early imaging manifestation of lung adenocarcinoma, and exploring the relationship between their...

A multimodal approach integrating NK cell-associated gene signatures and pathomics to predict colon adenocarcinoma prognosis.

FDA-authorized oncology artificial intelligence and machine learning devices and their clinical evidence: A cross-sectional analysis.

BACKGROUND: Artificial intelligence (AI) and machine learning (ML) tools are increasingly embedded in cancer care, yet the scope of U.S. Food and Drug...

MRI-based machine learning model to distinguish hippocampal sclerosis (HS) ILAE type 1 and no HS gliosis only in medial temporal lobe epilepsy.

PURPOSE: Despite recent advances in preoperative work-up of drug resistant medial temporal lobe epilepsy (MTLE), predicting post-surgical seizure and ...

Online adaptive radiotherapy: Clinical utility, challenges and perspectives.

Online adaptive radiotherapy (oART) represents a major evolution in radiation oncology, enabling daily plan adaptation to account for anatomical varia...

Hybrid supervised deep learning for lung adenocarcinoma diagnosis to optimize surgical strategies.

Intraoperative frozen section pathological diagnosis of lung adenocarcinoma serves as the gold standard for determining the extent of surgical resecti...

AI-based synthetic CT attenuation correction enables reliable quantitative SPECT in unilateral condylar hyperplasia.

PURPOSE: Unilateral condylar hyperplasia (UCH) is a rare mandibular growth disorder in which accurate assessment of condylar metabolic activity is ess...

Cauchy Lotus Optimization-based feature selection and ResNet 101 based XceptionNet architecture for radiation pneumonitis prediction in lung cancer patients.

Thoracic radiotherapy for lung cancer patients followed by radiation pneumonitis (RP) has significant clinical side effects. Risk-adaptive treatment p...

Combining cutting edge computational and experimental methods for targeting kras mutations in non-small cell lung cancer.

INTRODUCTION: Historically, KRAS mutations have been notoriously difficult to target despite their status as the most commonly mutated oncogene in the...

Clinician-deployable deep hypergraph model integrating clinical and CT radiomics predicts immunotherapy outcomes in NSCLC.

Current image-based deep learning models that predict the benefits of immunotherapy in non-small cell lung cancer (NSCLC) require high-performance har...

Advancing precision medicine in pancreatic cancer using novel biomarkers and clinical targets.

Precision-guided therapy is imperative in the battle against pancreatic ductal adenocarcinoma (PDAC), one of the most lethal solid malignancies with l...

Clinical Machine Learning Model for Predicting Pathological Complete Response in Patients with Esophageal and Gastroesophageal Junction Adenocarcinoma After Trimodality Therapy.

BACKGROUND: Accurate prediction of pathological complete response (pCR) after preoperative chemoradiation therapy, followed by surgery (trimodality th...

A review of clinical trial radiotherapy quality assurance: value, evolution and lessons.

BACKGROUND AND PURPOSE: Deviations in radiotherapy quality can significantly affect clinical trial outcomes, including overall survival. Radiotherapy ...

Chest Tube Learning Synthesis and Evaluation Assistant (CheLSEA): A Prospective Observational Trial of an Intelligent Decision Support System.

OBJECTIVE: This study evaluates the performance of an artificial intelligence predictive clinical decision support system (CheLSEA) in generating ches...

Exploring the potential targets and mechanisms of malachite green induced lung adenocarcinoma through network toxicology and molecular docking.

Malachite green (MG), a synthetic dye, has turned into a major risk to human health, because of its toxicity of teratogenic, genotoxic, carcinogenic, ...

Clustering by patients-related characteristics identifies specific phenotypes with different prognosis after resection of non-small cell lung cancer: a French nationwide study from the Epithor database.

INTRODUCTION: Lung cancer remains the leading cause of cancer mortality worldwide despite advances in treatment. Patient-related factors beyond tumour...

Oral microbiome signatures predict biological age and host health.

Identifying robust, non-invasive biomarkers of biological age is key to preventive medicine. While gut aging clocks exist, the oral microbiome remains...

Identifying environmental factors associated with tetrodotoxin contamination in bivalve mollusks using eXplainable AI.

Since 2012, tetrodotoxin (TTX) has been found in seafoods such as bivalve mollusks in temperate European waters. TTX contamination leads to food safet...