Microchemical Strategies and AI-Enabled Biosensing for Non-Invasive Liquid Biopsy in Precision Oncology
- 1St. Petersburg College
Abstract
Traditional blood-based liquid biopsy platforms have established crucial paradigms in precision oncology by enabling the longitudinal tracking of circulating tumor DNA (ctDNA) and circulating tumor cells (CTCs). However, blood sampling remains minimally invasive, requires dedicated clinical infrastructure, and is restricted by physiological boundaries such as the blood-brain barrier (BBB). Non-Invasive Liquid Biopsy (NILB) addresses these limitations by utilizing painlessly collected alternative biological matrices, including saliva, urine, breath, stool, sweat, sputum, tears, and cervical secretions. From a microchemical and analytical perspective, these biofluids present extreme analytical challenges: target biomarkers exist at attomolar-to-picomolar concentrations within dense, highly variable chemical matrices containing high background noise and interference. This review outlines the microchemical, physical, and bio-sensing strategies developed to overcome these pre-analytical and analytical barriers. We evaluate advanced microfluidic architectures, nanoscale deterministic lateral displacement, functionalized surface chemistry, isothermal signal amplification, and label-free optical spectroscopy. Furthermore, we evaluate how Artificial Intelligence (AI) and chemometric algorithms—such as Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and multivariate spectral extraction—decode low-abundance, fragmented multi-omic signatures from complex noise. Finally, we propose a standardized analytical framework to bridge point-of-care microchemical devices with routine clinical diagnostic pipelines.
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BibTeX
@misc{long2026microchemical,
title = {{Microchemical Strategies and AI-Enabled Biosensing for Non-Invasive Liquid Biopsy in Precision Oncology}},
author = {Samuel Long and Abigail Long},
howpublished = {ChemRxiv},
year = {2026},
month = sep,
doi = {10.26434/chemrxiv.15009373/v1},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009373/v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Microchemical Strategies and AI-Enabled Biosensing for Non-Invasive Liquid Biopsy in Precision Oncology
AU - Samuel Long
AU - Abigail Long
PY - 2026
DA - 2026/09/23
PB - ChemRxiv
DO - 10.26434/chemrxiv.15009373/v1
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009373/v1
AB - Traditional blood-based liquid biopsy platforms have established crucial paradigms in precision oncology by enabling the longitudinal tracking of circulating tumor DNA (ctDNA) and circulating tumor cells (CTCs). However, blood sampling remains minimally invasive, requires dedicated clinical infrastructure, and is restricted by physiological boundaries such as the blood-brain barrier (BBB). Non-Invasive Liquid Biopsy (NILB) addresses these limitations by utilizing painlessly collected alternative biological matrices, including saliva, urine, breath, stool, sweat, sputum, tears, and cervical secretions. From a microchemical and analytical perspective, these biofluids present extreme analytical challenges: target biomarkers exist at attomolar-to-picomolar concentrations within dense, highly variable chemical matrices containing high background noise and interference. This review outlines the microchemical, physical, and bio-sensing strategies developed to overcome these pre-analytical and analytical barriers. We evaluate advanced microfluidic architectures, nanoscale deterministic lateral displacement, functionalized surface chemistry, isothermal signal amplification, and label-free optical spectroscopy. Furthermore, we evaluate how Artificial Intelligence (AI) and chemometric algorithms—such as Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and multivariate spectral extraction—decode low-abundance, fragmented multi-omic signatures from complex noise. Finally, we propose a standardized analytical framework to bridge point-of-care microchemical devices with routine clinical diagnostic pipelines.
ER -