Lung Cancer

AI-based prediction of recurrence after carbon ion radiotherapy for early stage non-small cell lung cancer.

Lung cancer is a leading cause of cancer-related deaths. Carbon ion radiotherapy (CIRT) is a treatment modality for patients with inoperable conditions or who decline surgery, but there is room for research to identify patients at high risk of recurrence. The use of artificial intelligence (AI)-based predictive models in healthcare is growing, yet their application in predicting outcomes after CIR...

Multi-structure CT radiomics-based consensus model for the diagnosis of pancreatic ductal adenocarcinoma and vascular involvement.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy, with accurate preoperative assessment of vascular involvement critical for determining resectability and treatment planning. Conventional contrast-enhanced CT relies on qualitative evaluations, leading to interobserver variability and diagnostic uncertainty. Existing radiomics studies for PDAC mostly focus on single...

Deep Learning Reconstruction Combined with Contrast-Enhancement Boost Technique in "Quadruple-low" CCTA Protocol: Evaluation of Image Quality and Diagnostic Accuracy.

RATIONALE AND OBJECTIVES: To evaluate the impact of a deep learning reconstruction (DLR) algorithm combined with contrast-enhancement boost (CE-boost)...

NIAID Biodosimetry Strategic Plan.

In the face of emerging threats from natural disasters, nuclear accidents, and potential malicious use of radiation, the National Institute of Allergy...

Integrative analysis of immune and microbial subtypes predicts immunotherapy response in stomach adenocarcinoma.

The tumor immune microenvironment and intratumoral microbiota play critical roles in cancer progression and immunotherapy response, yet their integrat...

Cardiovascular Risk Assessment Tools in Chronic Kidney Disease.

Cardiovascular (CV) risk calculators estimate the likelihood of CV events by integrating factors such as age, sex, blood pressure, lipids, smoking, an...

Integrative single-cell and machine learning framework reveals prognostic fibroblast subtypes and constructs a fibroblast-related risk signature in lung adenocarcinoma.

Deep learning for synthetic PET imaging: a systematic mapping review of techniques, metrics, and clinical relevance.

BACKGROUND: Synthetic positron emission tomography (PET) imaging, enabled by deep learning, represents a promising approach to minimize radiation expo...

Spatial and multi-omics transcriptomic dissects platinum resistance in lung adenocarcinoma: a five-gene predictive model with tumor microenvironment dynamics.

The scarcity of reliable biomarkers and predictive models for platinum resistance in lung adenocarcinoma (LUAD) poses a significant clinical challenge...

Halide perovskite scintillators for X-ray detection: from structure to engineering.

Halide perovskites (HPs) and their derivatives are emerging as a prominent class of materials for ionizing radiation detection. A unique combination o...

Interpretable deep learning model of circulating genomics for quantitative survival prediction in advanced non-small cell lung cancer.

PURPOSE: Accurate quantitative survival prediction in advanced non-small cell lung cancer (NSCLC) remains an unmet clinical need. While liquid biopsy ...

CT-based radiomics for predicting PD-L1 expression status in non-small cell lung cancer using a hybrid machine learning model.

PURPOSE: Programmed cell death ligand-1 (PD-L1) is a key prognostic and predictive biomarker for immunotherapy in non-small cell lung cancer (NSCLC). ...

Evaluation of deliverable dose-mimicking automated volumetric arc radiation therapy planning for stage III non-small cell lung cancer patients: comparison with a commercial DVH-predicted automated planning system.

This study aimed to evaluate the clinical validity of a dose-mimicking automated planning for volumetric-modulated arc therapy (VMAT) in patients with...

Validation of a Deep-Learning Coregistration Framework for Long-Axial-Field-of-View PET/CT Using Low-Radiation-Exposure Protocols Across Various Tracers.

The advent of long-axial-field-of-view (LAFOV) PET/CT systems has significantly improved whole-body imaging by providing higher sensitivity and extend...

Quantification of PET activation in adipose tissue from non-contrast CT scans.

Brown adipose tissue (BAT) plays a key role in energy metabolism and cardiometabolic health. Its detection typically relies on 18F-FDG PET, which is c...

Risk assessment in cardiac surgery: Exploring machine learning and laboratory indices as adjunctive tools.

BACKGROUND: Post-operative outcomes of cardiovascular surgery vary greatly among patients for a variety of reasons. While the specific reasons are oft...

Revealing the limits of covalent docking and advancing affinity prediction with covalent-aware multi-task learning.

Targeted covalent inhibitors (TCIs) have become an important modality in modern drug discovery, but computational tools for covalent pose prediction a...

CT Radiation Dose Reduction With Preserved Diagnostic Performance: How Far Have We Come Over 25 Years?

Concerns about the risk of radiation from CT have driven a spectrum of major advances in radiation dose reduction technology since the 2000s, includin...

Rapid, label-free cancer detection in fresh pancreatic tissue using deep learning and multispectral Mueller matrix polarimetry.

BACKGROUND: Frozen section (FS) tissue assessment is essential for guiding intraoperative surgical decision-making in oncology, particularly in proced...

Enhancing diagnostic safety with low iodine, low radiation CTPA classification using deep learning.

Pulmonary embolism (PE) is a life-threatening condition for which computed tomography pulmonary angiography (CTPA) is the standard diagnostic modality...