Breast Cancer: Innovations, Clinical Course, Nanomedicine, Relapse and Optimal Management of Breast Cancer Using Sodium MRI-PET imaging, Artificial Intelligence and Virtual Reality Methods.

Abstract

Advanced breast cancer poses a life threat. It represents lumps, mammary cell carcinoma, mammary gland nodules and metastasis with direct clinical course. Such metastases may pose resistance to chemo/immunotherapy with only option left of careful theranostic multiparametric tumor monitoring, tumor killing and surgery. The paper focuses on nano-radiotracer assisted sodium MRI-PET apoptosis evaluation, accumulated intracellular sodium based breast tumor classification, use of nanomedicine in theranosis of human breast tumor, deep learning by reduced noise and minimal artifacts with a applying Compressed Sensing Reconstruction, review of dictionary learning as sparsifying transformation for breast cancer sodium MRI-PET based classification, clinical trials and new physician’s guideline. Major therapy options are: local therapy, immune check point inhibitor therapy and antivascular therapy with upcoming genomic agents and ablation therapy for metastases either relapse or residual disease. A three-dimensional dictionary-learning by compressed sensing reconstruction algorithm (3D-DLCS) and K-singular-value-decomposition (K-SVD) sparifying transform algorithm assess high-quality reconstructions of apoptosis rich areas showing intracellular 23 Na peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) characteristics. The PubMed search on sodium MRI/PET clinical trials, Deep learning AI, virtual reality, bioinspired approaches serve as navigation guideline. Conclusion. The nano-radiotracer assisted 23 Na MRI-18F PET clinical tool is choice in breast cancer navigation theranosis. The 3D-DLCS deep learning algorithm on  23 Na MRI data classifies and navigates tumors with least noise and less artifact levels.