Characterisation of MRI Indeterminate Breast Lesions Using Dedicated Breast PET and Prone FDG PET-CT in Patients with Breast Cancer—A Proof-of-Concept Study

Upconversion and NIR-II luminescent rare earth nanoparticles combined with machine learning for cancer theranostics

How to develop contrast agents for cancer theranostics is a meaningful and challenging endeavor, and rare earth nanoparticles (RENPs) may provide a possible solution. In this study, we initially modified RENPs through the application of photodynamic agents (ZnPc) and targeted the bevacizumab antibody for cancer theranostics, which was aimed at improving the therapeutic targeting and efficacy. Subsequently, we amalgamated anthocyanin with the modified RENPs, creating a potential cancer diagnosis platform. When the spectral data were obtained from the composite of cells, the crucial information was extracted through a competitive adaptive reweighted sampling feature algorithm. Then, we employed a machine learning classification model and classified both the individual spectral data and fused spectral data to accurately predict distinctions between breast cancer and normal tissue. The results indicate that the amalgamation of fusion techniques with machine learning algorithms provides highly precise predictions for molecular-level breast cancer detection. Finally, in vitro and in vivo experiments were carried out to validate the near-infrared luminescence and therapeutic effectiveness of the modified nanomedicine. This research not only underscores the targeted effects of nanomedicine but also demonstrates the potent synergy between optical spectral technology and machine learning. This innovative approach offers a comprehensive strategy for the integrated treatment of breast cancer.