Latest AI and machine learning research in lung cancer for healthcare professionals.
Radiomics and deep learning both offer powerful tools for quantitative medical imaging, but most existing fusion approaches only leverage global radiomic features and overlook the complementary value of spatially resolved radiomic parametric maps. We propose a unified framework that first selects discriminative radiomic features and then injects them into a radiomics-enhanced nnUNet at both the gl...
Computed Tomography (CT) is one of the largest contributors to radiation exposure from medical imaging, which can induce DNA damage and increase cancer risk. Reducing CT radiation dose to improve patient safety inherently increases image noise and artifacts. Generative adversarial networks (GANs) have shown promise for unsupervised low-dose CT (LDCT) denoising. Building on this, RDBCycleGAN-CBAM, ...
Radiogenomics enables the non invasive characterisation of the genomic and molecular properties of tumours, with epidermal growth factor receptor (EGF...
Background: Pancreatic ductal adenocarcinoma is one of the most aggressive and lethal malignancies of the gastrointestinal tract. The poor prognosis i...
Lung cancer is the leading cause of cancer-related mortality worldwide, predominantly affects older individuals, with non-small cell lung cancer (NSCL...
IMPORTANCE: Although angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) are recommended for people with chronic...
Widespread screening for Adolescent Idiopathic Scoliosis (AIS) is critical for timely intervention but is currently constrained by the radiation risks...
Pancreatic ductal adenocarcinoma remains one of the most lethal malignancies, largely due to the absence of reliable early stage biomarkers. Here, we ...
Purpose: To develop SCOPE (Small-lesion COntextual Pancreatic Evaluator), a deep learning model designed to improve CT detection of small pancreatic l...
DNA methylation is a central epigenetic modification that regulates gene expression, maintains genomic stability, and guides cellular differentiation....
Pancreatic ductal adenocarcinoma (PDAC), one of the deadliest solid malignancies, is often detected at a late and inoperable stage. Retrospective revi...
Multimodal evidence is critical in computational pathology: gigapixel whole slide images capture tumor morphology, while patient-level clinical descri...
Protein expression within oncogenic or suppressive pathways is a hallmark indicator of oncogenesis. While traditional AI models in digital pathology a...
Accurate prediction of mutational dependencies to model tumor evolution can improve our understanding of cancer progression and is crucial for early d...
Deep learning models in computational pathology often fail to generalize across cohorts and institutions due to domain shift. Existing approaches eith...
Despite a decade of immunotherapy, treatment selection in non-small cell lung cancer (NSCLC) still relies on subgroup analyses and clinical scores. I3...
Supervised deep learning models often achieve excellent performance within their training distribution but struggle to generalize beyond it. In cancer...
Understanding the role of tertiary lymphoid structures (TLS) is crucial in non-small cell lung cancer (NSCLC), as they are associated with patient pro...
In the deformation measurement of high-temperature structures, image degradation caused by thermal radiation and random errors introduced by heat haze...
Peptides, as therapeutic molecules, offer unique advantages in targeting complex protein surfaces, yet their rational design remains limited by the va...