Integrating Nanotherapeutic Platforms to Image Guided Approaches for Management of Cancer

Cancer is a leading cause of mortality worldwide, accounting for 8.8 million deaths in 2015. The landscape of cancer therapeutics is rapidly advancing with development of new and sophisticated approaches to diagnostic testing. Treatment plan for early diagnosed patients include radiation therapy, tumor ablation, surgery, immunotherapy and chemotherapy. However the treatment can only be initiated when the cancer has been diagnosed thoroughly. Theranostics is a term that combines diagnostics with therapeutics. It embraces multiple techniques to arrive at comprehensive diagnosis, molecular images and an individualized treatment regimen. Recently, there is an effort to tangle the emerging approach with nanotechnologies, in an attempt to develop theranostic nanoplatforms and methodologies. Theranostic approach to management of cancer offers numerous advantages. They are designed to monitor cancer treatment in real time. A wide variety of theranostic nanoplatforms that are based on diverse nanostructures like magnetic nanoparticles, carbon nanotubes, gold nanomaterials, polymeric nanoparticles and silica nanoparticles showed great potential as cancer theranostics. Nano therapeutic platforms have been successful in integrating image guidance with targeted approach to treat cancer.

Application of Deep Learning in Automated Analysis of Molecular Images in Cancer: A Survey

Molecular imaging enables the visualization and quantitative analysis of the alterations of biological procedures at molecular and/or cellular level, which is of great significance for early detection of cancer. In recent years, deep leaning has been widely used in medical imaging analysis, as it overcomes the limitations of visual assessment and traditional machine learning techniques by extracting hierarchical features with powerful representation capability. Research on cancer molecular images using deep learning techniques is also increasing dynamically. Hence, in this paper, we review the applications of deep learning in molecular imaging in terms of tumor lesion segmentation, tumor classification, and survival prediction. We also outline some future directions in which researchers may develop more powerful deep learning models for better performance in the applications in cancer molecular imaging.