Background and objectives Lung cancer remains the leading cause of cancer-related mortality worldwide. Early detection of pulmonary nodules is crucial for timely diagnosis and effective treatment.

The application of deep learning techniques in lung nodule detection represents a significant advance in the early diagnosis and management of lung cancer. Recent developments have harnessed the power ...

Novel Graph Neural Network (N-GNN) Model Achieves Superior Accuracy in Early Lung Cancer Detection, Paving the Way for Enhanced Diagnostic Capabilities. The research, a collaboration between BioMark's ...

(a) Deep learning-based circulating exosome analysis for lung cancer detection (copyright American Chemical Society, 2024); (b) The operational mechanism of the Förster resonance energy transfer (FRET ...

Development and Validation of an Ipsilateral Breast Tumor Recurrence Risk Estimation Tool Incorporating Real-World Data and Evidence From Meta-Analyses: A Retrospective Multicenter Cohort Study Data ...

A new study published in JCO Clinical Cancer Informatics demonstrates that machine learning models incorporating patient-reported outcomes and wearable sensor data can predict which patients with ...

Please provide your email address to receive an email when new articles are posted on . Machine learning models can predict which patients receiving lung cancer therapy may need urgent care visits.

Dr. Nathan Pennell and Dr. Cheryl Czerlanis discuss challenges in lung cancer screening and potential solutions to increase screening rates, including the use of AI to enhance risk prediction and ...

A new CRISPR-powered light sensor can detect the faintest whispers of cancer in a single drop of blood.

Lung cancer symptoms are often non-specific, leading to late detection and misattribution to less severe conditions. The GO2 for Lung Cancer provides resources, policy advocacy, and access to clinical ...