Latest AI and machine learning research in lung cancer for healthcare professionals.
Cellular senescence is a heterogeneous cell state induced by diverse stressors, including telomere a...
Lung adenocarcinoma (LUAD) grading depends on accurately identifying growth patterns, which are indi...
Rational design of covalent inhibitors requires simultaneously optimizing multiple properties, such ...
MRI is preferred over CT in paediatric imaging because it avoids ionising radiation, but its use in ...
Lung cancer remains one of the leading causes of cancer-related mortality worldwide. Conventional co...
Objectives: Among surgically resected non-small cell lung cancer (NSCLC) patients with similar stage...
Background: Current deep learning models in computational pathology, radiology, and digital patholog...
Purpose: Automated medical image-based prediction of clinical outcomes, such as overall survival (OS...
Multiple Instance Learning (MIL) is the dominant framework for gigapixel whole-slide image (WSI) cla...
Background Cardiovascular adverse events (CVAEs) after chemoradiotherapy (CRT) for lung cancer are m...
Introduction: Podocyte injury is central to the pathogenesis of most glomerulonephritides (GN) and c...
Purpose: Manual verification of AI-based auto-contouring is labor-intensive and prone to fatigue-rel...
In complex environments, infrared object detection exhibits broad applicability and stability across...
Cancer data standardization requires converting unstructured pathology reports into structured regis...
The Brain Tumor Reporting and Data System (BT-RADS) standardizes post-treatment MRI response assessm...
A precise spatial delivery of the radiation dose is crucial for the treatment success in radiotherap...
Deletions in Exon-19 of the epidermal growth factor receptor (EGFR) play a pivotal role in the patho...
Major pathological response (pR) following neoadjuvant therapy is a clinically meaningful endpoint i...
We present a fairness-aware framework for multi-class lung disease diagnosis from chest CT volumes, ...
The differentiation between tumor recurrence and radiation-induced contrast enhancements in post-tre...
Background: Previous research has shown that radiomics-based machine learning models are promising p...