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 ...

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.

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 ...

Survival outcomes in non-small cell lung cancer: Real-world analysis of immunotherapy era vs pre-immunotherapy era, with insights into treatment settings, racial disparities, and socioeconomic impacts ...

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 ...

Scientists have created a new low-cost, fast response sensor to detect lung cancer biomarkers, paving the way for the development of screening devices to spot the disease even before symptoms occur.

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.

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 ...