Longevity News

Artificial Intelligence-Based Integration of Imaging, Exposome, and Multi-omics Data for Immune-Related Biomarker Discovery and Precision Prevention in Breast Cancer

Source: Frontiers - Health • Published: 04 Sept 2026, 00:00

Artificial Intelligence-Based Integration of Imaging, Exposome, and Multi-omics Data for Immune-Related Biomarker Discovery and Precision Prevention in Breast Cancer

Assess how AI-driven imaging can be combined with exposome and multi-omics data to identify immune-related biomarkers for breast cancer prevention and management.

Key Takeaways
  • You have multiple emails registered with Frontiers: You don't have a Frontiers account ?
  • You can register here Breast cancer management still depends on early detection
  • Imaging is used throughout screening, diagnosis, treatment evaluation, and follow-up
Read Original Source

Continue exploring

Related longevity signals

Frontiers - HealthToward nursing-integrated biomarker surveillance for immune-related adverse events during immune checkpoint inhibitor therapyDescribes nursing-integrated biomarker surveillance strategies to detect immune-related adverse events during immune checkpoint inhibitor therapy and how to operationalize them.Frontiers - HealthDevelopment and Validation of a Machine Learning Model Based on Multi-Source Clinical Data for Predicting the Risk of Early Neurological Deterioration in Patients with Ischemic StrokeA study presents a clinical risk model using machine learning to predict early neurological deterioration after acute ischemic stroke for bedside risk stratification.Frontiers - HealthSpatial immune ecosystems and therapy resistance in large B-cell lymphoma: from single-cell multi-omics to immunotherapy-matched validationReview of how single-cell and spatial multi-omics map tumor and immune architectures in large B-cell lymphoma and guide pathology-ready biomarker development.Frontiers - HealthMachine Learning-Based Assessment of Tumor-Infiltrating Lymphocytes in Breast Cancer Histopathology: A Systematic Review and Evidence-Gap Analysis of Translational ReadinessAssessments of tumor-infiltrating lymphocytes in breast cancer using machine learning are reviewed and mapped for translational readiness and methodological gaps.