In Advanced healthcare materials
Extracellular vesicles (EVs) are shed from cancer cells into body fluids, enclosing molecular information about the underlying disease with the potential for being the target cancer biomarker in emerging diagnosis approaches such as liquid biopsy. Still, the study of EVs presents major challenges due to their heterogeneity, complexity, and scarcity. Recently, liquid biopsy platforms have allowed the study of tumor-derived materials, holding great promise for early-stage diagnosis and monitoring of cancer when interfaced with novel adaptations of optical readouts and advanced machine learning analysis. Here, we review recent advances in labeled and label-free optical techniques such as fluorescence, plasmonic, and chromogenic-based systems interfaced with nanostructured sensors like nanoparticles, nanoholes, and nanowires and diverse machine learning analysis. We compare the adaptability of the different optical methods discussed and provide insights into prospective avenues for the translation of the technological approaches for cancer diagnosis. We discuss that the inherent augmented properties of nanostructures enhance the sensitivity of the detection of EVs. We conclude by reviewing recent integrations of nanostructured based optical readouts with diverse machine learning models as novel analysis ventures that can potentially increase the capability of the methods to the point of translation into diagnostic applications. This article is protected by copyright. All rights reserved.
Mata Carolina Del Real, Jeanne Olivia, Jalali Mahsa, Lu Yao, Mahshid Sara
2022-Nov-28
cancer, extracellular vesicles, machine learning, nanostructures, optical readouts
