{"title":"A Miniaturized Graphene-Based THz Biosensor for Noninvasive Early-Stage Breast Cancer Detection and TNM Staging","authors":"Mahesh Kumar Aghwariya, Vanshika Goel, Sasmita Dash, Sudhi Agarwal Kamboj, Tushar Goel","doi":"10.1002/ima.70385","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>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 SiO<sub>2</sub> 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 (<span></span><math>\n <semantics>\n <mrow>\n <msub>\n <mi>μ</mi>\n <mi>c</mi>\n </msub>\n </mrow>\n <annotation>$$ {\\mu}_c $$</annotation>\n </semantics></math>) 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.</p>\n </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"36 4","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2026-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Imaging Systems and Technology","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/ima.70385","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
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 () 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.
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.