Optical fibers are gaining increasing attention in biomedical applications due to their unique advantages, including flexibility, biocompatibility, immunity to electromagnetic interference, potential for miniaturization, and the ability to perform remote, real-time, and in situ sensing. Label-free optical fiber biosensors represent a promising alternative to conventional cancer diagnostics, offering comparable sensitivity and specificity while enabling real-time detection at ultra-low concentrations without the need for complex labeling procedures. However, the sensing performance of biosensors is fundamentally governed by surface modification. The choice of optimal functionalization strategy is dictated by the sensor type, target biomarker, and detection environment. This review paper presents a comprehensive and expanded overview of various surface functionalization methods specifically designed for cancer biomarker detection using optical fiber biosensors, including silanization, self-assembled monolayers, polymer-based coatings, and different dimensional nanomaterials (0D, 1D, and 2D). Furthermore, the emerging integration of computational methods and machine learning in optimizing functionalized optical sensing has been discussed. To the best of our knowledge, this is the first work that consolidates existing surface modification approaches into a single, cohesive resource, providing valuable insights for researchers developing next-generation fiber optic biosensors for cancer diagnostics. Moreover, the paper points out the current technical challenges and outlines the future perspectives of optical fiber-based biosensors.

A semi-distributed interferometer is a low-reflectivity device with refractive index sensing capability, exploiting the random reflectivity of a nanoparticle-doped fiber to form a weak distributed cavity. In this work, we extend this concept to an extrinsic semi-distributed interferometer (ESDI), using an overlay made of polydimethylsiloxane (PDMS) around the fiber tip; this structure can then be surface-modified using a thin metallic film or a nanoparticle coating. We report gold-sputtered and gold-nanoparticle-coated ESDI structures for refractive index sensing capability, with the latter achieving superior performances with an average sensitivity of 62.8 dB/RIU (refractive index units) with resolution of 3.9 × 10⁻⁵ RIU over the range of 1.34790–1.35981. We also report a possible biological application using a biofunctionalized version of this probe for the detection of VEGF (vascular endothelial growth factor); the gold-sputtered probe achieves the highest sensitivity, 0.0565 dB for each 10× concentration increase, with 355 fM detection limit.

Introduction: Fiber-optic biosensors generate complex, multichannel interferometric data affected by baseline noise and drift, which must be filtered and converted into standardized performance metrics before they can support cancer cell detection claims. Our group's fiber-optic biosensing programme uses two recognition strategies — a surface plasmon resonance anti-CD44 immunosensor and a PDA-based molecularly imprinted polymer sensor — for TNBC cell lines detection, yet both rely on the same challenge: extracting reliable calibration data from noisy, multi-channel wavelength-shift signals. To address this gap, a computational signal-processing framework was developed and validated that applies baseline correction and noise filtering, automatically identifies high-sensitivity spectral channels, fits calibration curves, and computes limit of detection, coefficient of determination, repeatability, and selectivity, and was applied to quantify and compare the two TNBC biosensor platforms. Materials and Methods: A computational/analytical approach was applied to previously acquired experimental data. Raw transmission spectra from 8-channel quasi-random extrinsic interferometric fiber-optic sensors were used: (1) the SPR-based anti-CD44 immunosensor exposed to HCC1806 TNBC cells and HEK293 control cells, and (2) the PDA-based MIP sensor exposed to HCC1806 and MDA-MB-231 TNBC cells. Raw spectra were first denoised using a 5th-order Butterworth filter combined with baseline subtraction to correct for high-frequency noise and baseline drift prior to peak identification. For each channel, a custom peak/valley-detection algorithm implemented in MATLAB identified interference fringes and tracked wavelength position across cell concentrations. The sub-range with the steepest slope was selected as the optimal operating range per sensor. Calibration curves were fitted by linear regression, with the coefficient of determination (R²) as fit quality. Limit of detection (LoD) was calculated as 3σ of the blank response divided by calibration slope. Repeatability was assessed as coefficient of variation across replicates; selectivity by comparing target-cell versus non-target/control responses. Results and Conclusions: The developed computational framework successfully processed multichannel interferometric data from both the SPR-based immunosensor and the MIP-based biosensor, automatically identifying high-sensitivity spectral channels and extracting calibration-based performance metrics — including limit of detection, coefficient of determination, repeatability, and selectivity — for each. Filtering with the Butterworth and baseline-subtraction steps improved spectral clarity by suppressing high-frequency noise and drift, yielding calibration curves with higher sensitivity and improved linearity. This provided a consistent, standardized basis for comparing the two recognition strategies and demonstrates the framework's potential as a common analytical backbone for future fiber-optic biosensor platforms in oncology, extendable to currently unanalyzed sensor datasets.

Introduction: Triple-negative breast cancer (TNBC) is an aggressive subtype lacking effective targeted therapies, highlighting the need for sensitive and selective cancer cell detection. Optical fiber biosensors offer high sensitivity, compact size, and label-free operation for cancer cell detection. This study developed a quasi-random extrinsic interferometer-based fiber-optic biosensor for label-free TNBC cell detection using two complementary recognition strategies: gold–anti-CD44 antibody functionalization and polydopamine (PDA)-based molecularly imprinted polymer (MIP) recognition. The performance of these functionalized sensors was evaluated for selective recognition of TNBC cells, demonstrating their potential as a sensitive and label-free detection platform. Materials and Methods: A quasi-random extrinsic interferometer-based fiber-optic sensor was fabricated and coated with polydimethylsiloxane (PDMS), a biocompatible polymer widely used in biomedical applications. For antibody-based recognition, a gold-coated surface was functionalized with anti-CD44 antibodies to target HCC1806 breast cancer cells, with HEK293 cells used as a control. For synthetic recognition, dopamine was polymerized on the sensor surface to form a polydopamine (PDA)-based molecularly imprinted polymer (MIP) designed to recognize HCC1806 and MDA-MB-231 cells. Optical responses at different cell concentrations were recorded using a fiber-optic interrogator, and sensor performance and cell attachment were evaluated by calibration analysis and scanning electron microscopy. Results and Conclusions: The developed fiber-optic biosensors show potential for integration into liquid biopsy platforms for label-free detection of circulating tumor cells in biological fluids, providing a rapid and minimally invasive approach for cancer detection and monitoring.

Breast cancer remains a major cause of cancer-related mortality in women, and its substantial molecular heterogeneity highlights the need for methods capable of characterizing tumor cells at the cellular level. CD44, a transmembrane glycoprotein associated with breast cancer progression, metastasis, and cancer stem-like phenotypes, is a promising biomarker for selective recognition of aggressive, basal-like cell populations. However, conventional approaches to evaluating CD44 expression including immunohistochemistry, immunofluorescence, and flow cytometry require labeling and multi-step sample preparation. In this work, a gold-coated, polydimethylsiloxane (PDMS)-based extrinsic semi-distributed interferometric (SDI) optical fiber biosensor functionalized with anti-CD44 antibodies was developed for label-free, selective detection of CD44-expressing HCC1806 breast cancer cells. The functionalized sensor exhibited a concentration-dependent response with a limit of detection (LoD) of 2.51 cells/mL, while the antibody-free control sensor exposed to HCC1806 cells and the anti-CD44-functionalized sensor exposed to CD44-low HEK293 cells showed only minimal responses, supporting selective detection under the tested conditions. These results support the potential of the antibody-functionalized ESDI platform for label-free detection of CD44-expressing cells, providing a groundwork for its validation in clinical samples as a step toward tumor-cell diagnostics.