Latest AI and machine learning research in lung cancer for healthcare professionals.
Introduction: Podocyte injury is central to the pathogenesis of most glomerulonephritides (GN) and causes segmental glomerulosclerotic lesions that predict progression in IgA Nephropathy (IgAN). Recent advances in high-resolution microscopy and AI-assisted image analysis have enabled detailed quantification of podocyte foot process (FP) morphology. However, whether nanoscale podocyte morphometrics...
Purpose: Manual verification of AI-based auto-contouring is labor-intensive and prone to fatigue-related errors. This study developed the large language model (LLM)-based automated Quality Assurance (QA) for auto-contouring (LAQUA) system using a multimodal LLM, Gemini 2.5 Pro, and evaluated its feasibility as a clinical primary screening tool to streamline the QA workflow. Methods: Twenty male pe...
In complex environments, infrared object detection exhibits broad applicability and stability across diverse scenarios. However, infrared object detec...
Cancer data standardization requires converting unstructured pathology reports into structured registry variables, a mostly manual and resource-intens...
The Brain Tumor Reporting and Data System (BT-RADS) standardizes post-treatment MRI response assessment in patients with diffuse gliomas but requires ...
A precise spatial delivery of the radiation dose is crucial for the treatment success in radiotherapy. In the lung and upper abdominal region, respira...
Deletions in Exon-19 of the epidermal growth factor receptor (EGFR) play a pivotal role in the pathogenesis of non-small cell lung cancer (NSCLC), inf...
Major pathological response (pR) following neoadjuvant therapy is a clinically meaningful endpoint in non-small cell lung cancer, strongly associated ...
We present a fairness-aware framework for multi-class lung disease diagnosis from chest CT volumes, developed for the Fair Disease Diagnosis Challenge...
The differentiation between tumor recurrence and radiation-induced contrast enhancements in post-treatment glioblastoma patients remains a major clini...
Background: Previous research has shown that radiomics-based machine learning models are promising precision medicine tools for lesion-level predictio...
Accurate localization of tumor regions from hematoxylin and eosin-stained whole-slide images is fundamental for translational research including spati...
Quantitative systems pharmacology (QSP) models require calibration data from published literature, yet manual curation produces inconsistent documenta...
Predicting tumor evolution during radiotherapy is a clinically critical challenge, particularly when longitudinal changes are driven by both anatomy a...
Artificial intelligence-based radiation therapy (RT) planning has the potential to reduce planning time and inter-planner variability, improving effic...
Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy characterized by profound molecular heterogeneity and inconsistent responses to ge...
The application of large vision-language models to computational pathology holds great promise for diagnostic assistants but faces a critical computat...
Background: Kidney volumetry derived from CT has been proposed as a surrogate of renal function in living kidney donor evaluation. However, clinical i...
Cancer driver mutations shape the tumor microenvironment (TME), yet whether TME composition alone can predict genotype has not been systematically eva...
The image purification strategy constructs an intermediate distribution with aligned anatomical structures, which effectively corrects the spatial mis...