A Meta-Analytical and Quantitative Study of Biosensor Technologies in Cancer Diagnostics

Abstract

Recent advancements in biosensor technology have significantly transformed the landscape of cancer diagnostics by enabling early, rapid, and accurate detection of malignancies. This paper presents a comparative meta-analysis assessing the diagnostic performance of various biosensor platforms across eight major cancer types: breast, lung, prostate, ovarian, colorectal, pancreatic, liver, and gastric cancer. The analysis focuses on key performance metrics such as sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC) to evaluate the efficacy of biosensors in detecting specific cancer biomarkers, including HER2, CEA, PSA, CA-125, miRNA-21, MUC1, AFP, and miRNA-106a. Data were extracted from peer-reviewed literature that reported biosensor-based detection using different sensing modalities—such as electrochemical, optical, and nanowire field-effect transistor sensors—applied to a range of biological media, including serum, plasma, saliva, and whole blood. The pooled analysis revealed consistently high diagnostic performance, with most biosensors achieving AUC values above 0.90, indicating excellent accuracy. Electrochemical and optical biosensors showed particularly strong performance, likely due to their superior signal transduction capabilities and compatibility with nanomaterial enhancements. These findings highlight the growing clinical relevance of biosensors in oncology, suggesting their readiness for integration into routine diagnostic workflows. Their advantages—portability, low cost, fast detection, and minimal sample requirement—make them ideal for point-of-care applications and early-stage cancer screening. The study supports continued development and clinical validation of biosensor technologies, as well as future integration with artificial intelligence to enhance diagnostic precision and personalize patient care in oncology.