A Miniaturized Graphene-Based THz Biosensor for Noninvasive Early-Stage Breast Cancer Detection and TNM Staging

IF 3 4区 计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Mahesh Kumar Aghwariya, Vanshika Goel, Sasmita Dash, Sudhi Agarwal Kamboj, Tushar Goel
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Abstract

Breast cancer (BC) is one of the predominant causes of cancer-related mortality among women worldwide. The rising incidence of BC highlights the critical need for portable medical devices that can detect malignancies in breast tissue at an early stage. This paper presents a miniaturized, noninvasive THz biosensor designed to detect the development of tumors in breast tissue at an early stage and determine the TNM stage of BC. The proposed graphene-based biosensor is placed over a SiO2 substrate using metal radiators and graphene loads. The unique properties of graphene allow frequency reconfiguration ranging from 3.13 to 5.72 THz by adjusting its chemical potential ( μ c $$ {\mu}_c $$ ) through an external DC bias voltage. The proposed biosensor achieves an ultra-wide frequency band of 1.83 THz, a peak directivity of 7 dBi, and a peak gain of 6.1 dBi. Furthermore, a human breast phantom model, mimicking the dielectric characteristics of human breast tissue and nearby lymph nodes in the THz regime, has been developed. Reflectometry analysis is used to evaluate the biosensor's performance in detecting tumors ranging from 1 to 10 mm within breast tissue. Its performance is also evaluated at different angles and positions relative to the tissue, as well as in the presence of cancerous cells in nearby lymph nodes to determine the TNM (Tumor, Node, and Metastasis) staging of BC. The analysis results demonstrate that the proposed biosensor has the potential to detect tumor development in breast tissue at an early stage and determine the TNM stage of BC, making it suitable for both in vivo and ex vivo diagnostic applications.

小型石墨烯基太赫兹生物传感器用于无创早期乳腺癌检测和TNM分期
乳腺癌(BC)是全球女性癌症相关死亡的主要原因之一。BC发病率的上升凸显了对便携式医疗设备的迫切需求,这些设备可以在早期阶段检测乳腺组织中的恶性肿瘤。本文介绍了一种小型、无创太赫兹生物传感器,用于早期检测乳腺组织中肿瘤的发展,并确定乳腺癌的TNM阶段。所提出的基于石墨烯的生物传感器使用金属散热器和石墨烯负载放置在SiO2衬底上。石墨烯的独特性质允许通过外部直流偏置电压调节其化学势(μ c $$ {\mu}_c $$)来重新配置频率,范围从3.13到5.72太赫兹。该生物传感器实现了1.83 THz的超宽频段,7 dBi的峰值指向性和6.1 dBi的峰值增益。此外,一个人体乳房幻影模型,模仿人体乳房组织和附近淋巴结的介电特性在太赫兹体制,已经开发。反射分析用于评估生物传感器在检测乳腺组织内1至10毫米肿瘤方面的性能。在相对于组织的不同角度和位置,以及在附近淋巴结存在癌细胞的情况下,评估其性能,以确定BC的TNM(肿瘤,淋巴结和转移)分期。分析结果表明,所提出的生物传感器具有早期检测乳腺组织肿瘤发展和确定乳腺癌TNM阶段的潜力,使其适合体内和体外诊断应用。
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来源期刊
International Journal of Imaging Systems and Technology
International Journal of Imaging Systems and Technology 工程技术-成像科学与照相技术
CiteScore
6.90
自引率
6.10%
发文量
138
审稿时长
3 months
期刊介绍: The International Journal of Imaging Systems and Technology (IMA) is a forum for the exchange of ideas and results relevant to imaging systems, including imaging physics and informatics. The journal covers all imaging modalities in humans and animals. IMA accepts technically sound and scientifically rigorous research in the interdisciplinary field of imaging, including relevant algorithmic research and hardware and software development, and their applications relevant to medical research. The journal provides a platform to publish original research in structural and functional imaging. The journal is also open to imaging studies of the human body and on animals that describe novel diagnostic imaging and analyses methods. Technical, theoretical, and clinical research in both normal and clinical populations is encouraged. Submissions describing methods, software, databases, replication studies as well as negative results are also considered. The scope of the journal includes, but is not limited to, the following in the context of biomedical research: Imaging and neuro-imaging modalities: structural MRI, functional MRI, PET, SPECT, CT, ultrasound, EEG, MEG, NIRS etc.; Neuromodulation and brain stimulation techniques such as TMS and tDCS; Software and hardware for imaging, especially related to human and animal health; Image segmentation in normal and clinical populations; Pattern analysis and classification using machine learning techniques; Computational modeling and analysis; Brain connectivity and connectomics; Systems-level characterization of brain function; Neural networks and neurorobotics; Computer vision, based on human/animal physiology; Brain-computer interface (BCI) technology; Big data, databasing and data mining.
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