AI-Powered Insights: Predicting Treatment Response and Prognosis in Breast Cancer

Breast cancer remains a global challenge, marked by diverse patient responses to treatments and varying prognoses. The integration of Artificial Intelligence (AI) and Machine Learning (ML) offers transformative possibilities, enabling precise predictions of treatment efficacy and patient outcomes. These technologies analyze vast datasets, such as medical imaging, genomics, and clinical records, uncovering patterns that enhance personalized care.
For example, AI-driven models can forecast chemotherapy response using histopathological images, facilitating tailored treatment plans that improve patient outcomes. By empowering clinicians with actionable insights, AI and ML have the potential to revolutionize decision-making and optimize breast cancer management.
This Research Topic seeks to highlight groundbreaking advancements in AI and ML applications for predicting treatment responses and clinical outcomes in breast cancer, driving progress in precision oncology and personalized medicine. We welcome submissions that explore innovative AI/ML approaches in breast cancer care, emphasizing personalized and precision medicine. Key areas of interest include:
• Predicting Treatment Response: AI/ML models for forecasting patient-specific responses to therapies, including chemotherapy, immunotherapy, and targeted treatments.
• Prognostic Models: AI-driven tools to predict disease progression, recurrence risk, and survival outcomes, integrating clinical, genetic, and imaging data.
• Multi-Omics Integration: Approaches that combine genomic, transcriptomic, and proteomic data with clinical insights for personalized therapy predictions.
• AI in Imaging: AI applications in mammography, MRI, ultrasound, and histopathology for early detection, prognosis, and treatment planning.
• Liquid Biopsy and Biomarker Discovery: AI’s role in analyzing circulating tumor DNA and identifying biomarkers predictive of treatment efficacy.
• Survival and Recurrence Models: Predicting long-term survival and recurrence to guide follow-up strategies.
• Advanced ML Techniques: Innovative methodologies, including deep learning and ensemble methods, for enhancing predictive accuracy in breast cancer care.
• Big Data Approaches: Leveraging multi-modal data to create robust models that inform clinical decision-making.
• Clinical Validation and Translation: Addressing real-world challenges in model validation, generalizability, and clinical integration.
• Explainability and Integration: Enhancing model transparency and embedding AI tools into clinical workflows.
• Personalized Medicine: AI-driven design of targeted therapies and drug regimens tailored to individual molecular profiles.
• Early Intervention and Risk Stratification: Predicting early signs of recurrence or treatment resistance to enable timely interventions.
We invite original research, reviews, and clinical studies that present innovative AI applications in breast cancer care. Submissions should demonstrate clinical relevance and robust validation of AI models, address challenges in translation, including explainability and workflow integration, and highlight potential impacts on patient outcomes.
This Research Topic encourages collaboration from oncologists, data scientists, bioinformaticians, and imaging specialists. Together, we aim to explore AI’s transformative potential in breast cancer, advancing precision medicine and improving patient care worldwide.
Please note: manuscripts that are solely based on bioinformatics or computational analysis of public databases without validation (independent cohort or biological validation in vitro or in vivo) are out of scope for this section and will not be accepted as part of this Research Topic.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Case Report
• Clinical Trial
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Case Report
• Clinical Trial
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Review
• Study Protocol
• Systematic Review
• Technology and Code

Keywords: Breast Cancer, Artificial Intelligence, Machine Learning, Precision Oncology, Personalized Medicine, Treatment Response Prediction, Prognostic Models, AI in Imaging, Biomarker Discovery, Risk Stratification

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.