Keyword: Breast Neoplasms

ABSTRACT: Background: Dense breast tissue reduces mammographic sensitivity and raises breast cancer risk. Supplemental imaging and artificial intelligence supported mammography reading have been tested prospectively. The European Society of Breast Imaging recommends supplemental magnetic resonance imaging for extremely dense breasts, whereas the American College of Physicians in 2026 advised against it for average risk women with dense breasts. This review summarises what the principal studies measured and found.
Objective: To review the published evidence on both strategies in dense breasts, reporting each study's endpoints and results.
Materials and Methods: Narrative review of primary publications, published online 2019 to 2025, quoted verbatim. Four large prospective multicentre studies reporting on dense breasts were selected, two per strategy: the randomised trials DENSE (Netherlands, magnetic resonance imaging) and BRAID (United Kingdom, abbreviated magnetic resonance imaging, automated ultrasound and contrast-enhanced mammography), and the observational studies PRAIM (Germany, artificial intelligence supported double reading) and ASSURE (United States, artificial intelligence supported single reading with safeguard review).
Results: DENSE randomised 40,373 women with extremely dense breasts and normal mammography to invitation for supplemental magnetic resonance imaging or to mammography alone: interval cancers 2.5 versus 5.0 per 1000; among the 59% accepting, detection was 16.5 and false positives 79.8 per 1000. BRAID randomised 9361 women with dense breasts and a negative mammogram: detection was 17.4 per 1000 examinations with abbreviated magnetic resonance imaging, 19.2 with contrast-enhanced mammography and 4.2 with automated ultrasound, the contrast-based modalities not differing significantly; recall 9.7%, 9.7% and 4.0%, median invasive size 10, 11 and 22 millimetres. In PRAIM (463,094 women), artificial intelligence supported double reading was associated with detection of 6.7 versus 5.7 per 1000 (17.6% higher) and non-inferior recall; in dense breasts the 18.7% increase was not statistically significant. In ASSURE (579,583 tomosynthesis examinations, single reading), detection with the artificial intelligence workflow was 5.6 versus 4.6 per 1000 (21.6% higher), recall 11.1% versus 10.6%, and detection in dense breasts 22.7% higher.
Conclusions: In BRAID, abbreviated magnetic resonance imaging and contrast-enhanced mammography detected three times as many invasive cancers as automated ultrasound, at half the size with more than twice the recall, and did not differ significantly from each other. In DENSE, invitation to supplemental magnetic resonance imaging halved the interval cancer rate. Artificial intelligence support was associated with higher detection in PRAIM without higher recall and in ASSURE with slightly higher recall; neither was randomised. No included study measured breast cancer mortality; survival benefit remains undemonstrated and overdiagnosis unquantified.

ABSTRACT: Introduction: The study of triple-negative breast cancer cells detection is one of the important research fields in modern biomedical engineering, due to its aggressive behavior and limited targeting options. One of the promising methods for cancer cell detection is using Molecularly Imprinted Polymers (MIPs) with polydopamine (PDA) as the primary polymer. The main idea of this combination is based on the properties of dopamine, as it shows good compatibility with cells, biomimicry, and biodegradability. By creating specific cell recognition cavities within this PDA-based MIP layer directly on the fiber surface, the platform shows high selectivity level performance.
Methods and Materials: Using bulk imprinting strategy, dopamine was polymerized under alkaline conditions, where pH was in range of 8.0-9.0. For control group, the non-imprinted polymers (NIPs) were prepared by mixing phosphate buffer solution (PBS) with PDA, while MIPs were functionalized with HCC1806 and MDA-MB231 cancer cell lines. During detection process, the response groups were divided into two categories: target and cross groups. Optical responses of these groups were determined by using fiber-optic interrogator at different cell concentrations and the morphology was evaluated via scanning electron microscopy.
Results and Conclusion: The provided optical fiber technology shows great detection, stability, and biocompatibility levels for novel technology and has a great potential for further development towards the label-free detection platforms’ integration.

ABSTRACT: 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.

ABSTRACT: 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.