Wearable Sensors for Continuous Monitoring of Tumor Markers: A Paradigm Shift in Cancer Diagnostics and Management
Background: Cancer remains a leading global health challenge, with early detection and continuous monitoring of tumor markers critical to improving outcomes. Conventional serum-based assays such as ELISA, while sensitive, are limited by their intermittent and invasive nature. Wearable biosensors offer a non-invasive, real-time alternative for monitoring biomarkers such as PSA, CEA, CA-125, and CA 15-3 in peripheral biofluids. Materials and Methods: Flexible, nanomaterial-enhanced wearable biosensors were fabricated using graphene-coated gold nanoparticle electrodes functionalized with monoclonal antibodies. Biofluids (sweat, saliva, interstitial fluid) were collected via skin patches, unstimulated drooling, and microneedle-assisted extraction. Calibration was performed with recombinant standards, and results were benchmarked against ELISA. Subjects and Methods: The study enrolled 60 cancer patients (prostate, ovarian, colorectal, breast) and 20 healthy controls, aged 18–75 years, under IEC/IRB approval (IEC/ONC/2025/014) with informed consent. Participants wore the sensors continuously for 7 days, with skin compatibility and user comfort assessed. Interpretation and Statistical Analysis: Wearable biosensors achieved high concordance with ELISA (r ≥ 0.89) and detection limits between 0.15–0.30 ng/mL. Machine learning-assisted data analysis improved classification accuracy for early detection (91.9%), treatment monitoring (89.0%), and recurrence detection (90.7%). Statistical analyses were performed using SPSS v27 and ROC curve evaluation, with p < 0.05 considered significant. Conclusion: Wearable biosensors demonstrate strong potential for accurate, continuous tumor marker monitoring, offering a non-invasive, patient-friendly approach that supports proactive oncology care and precision medicine, particularly in resource-limited settings...
Publication Details
- Journal
- Journal of Carcinogenesis
- Published
- 2025-09-09
- DOI
- https://doi.org/10.64149/j.carcinog.24.2s.1020-1031
- Primary Topic
- Data Analysis with R
- Type
- article
- Field-Weighted Citation Impact
- 0.00