Cancer diagnoses traditionally require invasive or labor-intensive procedures such as tissue biopsies. Researchers at the Ludwig-Maximilians-Universität München (LMU) have now reported on a method ...
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 ...
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CNN-based system improves lung nodule detection and classification
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.
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 ...
Talk about a breath of fresh air. Researchers have developed a groundbreaking device that may one day make detecting lung cancer as easy as exhaling. “We built a screening tool that could allow ...
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 ...
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