Integration of Radiomics, Genomics, and Interpretability (XAI) for Diagnosis, Treatment, and Screening in Breast Cancer

Background Breast cancer, the most common cancer in women, demands innovative AI-driven approaches to enhance screening, diagnosis, and treatment. This study introduces an integrated framework combining radiomic (mammography and MRI), genomic (BRCA۱/۲ mutations), and clinical data to enable personalized medicine with interpretability. The key innovation lies in the multimodal data fusion using explainable algorithms, applied for the first time in breast cancer. Methods: Data were extracted from the TCGA-BRCA (genomics) and DDSM (mammography) databases. ResNet۵۰ was used for lesion detection in imaging, and XGBoost predicted treatment response. Interpretability methods (XAI) like SHAP and Grad-CAM identified critical imaging and genetic features. All code was published on GitHub. Results: ۱. Screening: - The ResNet۵۰-based model achieved ۹۶.۲% accuracy and ۹۴.۸% sensitivity, reducing false positives by ۳۵% compared to conventional methods. This aligns with critiques of mammography’s limitations in overdiagnosis and psychological distress from false positives, as noted in Cochrane reviews. - MRI integration improved tumor localization and staging, particularly for high-risk patients with BRCA mutations. ۲. Treatment: - The chemotherapy response prediction system demonstrated ۸۹.۵% accuracy by analyzing metastasis-related gene expression (e.g., BRCA۱). This mirrors clinical practices where systemic therapies like chemotherapy and targeted drugs are tailored to genetic profiles. - SHAP analysis highlighted heterogeneous tumor texture and BRCA۱ mutations as key predictors, aligning with studies emphasizing genetic and radiomic synergy. ۳. Interpretability: - Grad-CAM visualized tumor regions critical for AI decisions, addressing concerns about "black-box" models in clinical settings. For example, SHAP identified genomic features linked to immunotherapy resistance, supporting precision oncology workflows. Challenges and Ethical Considerations: - Data Privacy: Ensuring patient confidentiality in multimodal datasets remains critical, especially with genomic data. - Standardization: Variability in imaging protocols (e.g., MRI vs. mammography) and genomic testing methods complicates model generalizability. - Clinical Adoption: Overdiagnosis risks (e.g., unnecessary mastectomies) highlighted in Cochrane studies underscore the need for XAI to improve clinician trust. Conclusion: This AI framework advances personalized breast cancer care through multimodal integration and transparency. However, interdisciplinary collaboration is essential to address ethical dilemmas and standardize XAI models.

کلیدواژه ها:

Artificial Intelligence ، Breast Cancer ، Personalized-Medicine