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Recent advances in biosensors for cancer diagnosis and analytical strategies

Aug 2026 · Discover Sensors · Vol 2 · 0 citations · 96 references

TL;DR

This review summarizes relevant biosensor types, including electrochemical, optical, mass-sensitive, microfluidic, nano-biosensors (recently developed), and hydrogel-based systems with discussion of their underlying analytical principles and performance for clinically important cancer biomarkers.

Abstract

Around 20 million cancer cases were diagnosed around the world in 2022, killing almost 10 million individuals. Additionally, there are projections for this pattern to continue to rise significantly in years ahead. The methods typically used to provide cancer diagnoses (i.e. imaging, histopathology, centralized immunoassays) have limitations in terms of sensitivity at early stages, high cost, infrastructure requirements, and delays in obtaining timely diagnoses that prevent real-time and ongoing monitoring of disease progression. Biosensors fill these voids by enabling sensitive, specific, and rapid identification of circulating cancer biomarker from low-invasiveness liquid biopsies. As such, biosensors create new opportunities for early diagnosis of cancer and ongoing monitoring. This review summarizes relevant biosensor types, including electrochemical, optical, mass-sensitive, microfluidic, nano-biosensors (recently developed), and hydrogel-based systems with discussion of their underlying analytical principles and performance for clinically important cancer biomarkers. Recent advancements in nanomaterials, micro-engineering, and the processing of data have significantly improved the sensitivity of biosensors and their ability to perform multiplex analysis within oncology at point-of-care locations. Some of the remaining barriers to progress include, matrix effect, standardization for all types of biosensors, large-scale manufacturing capabilities, regulatory approval processes, and overall clinical translation. Areas of future research focus on biosesnsors capable of multiplex, multi-modal use AI-assisted platforms that can be integrated into precision cancer treatment.

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