Magnetoresistance and magneto-plasmonic sensors for the detection of cancer biomarkers: A bibliometric analysis and recent advances

The conventional approaches to diagnosing cancer are expensive, often involve exposure to radiation, and struggle to identify early-stage lung cancer. As a result, the five-year survival rate is significantly reduced. Fortunately, promising alternatives using magnetoresistance (MR) and magneto-plasmonic sensors have emerged for swiftly, accurately, and inexpensively detecting cancer in its initial phases. These sensor technologies offer numerous advantages over their counterparts, such as minimal background noise, immunity to environmental influences, compatibility with nanofabrication methods, ability to detect multiple substances simultaneously, straightforward integration, high specificity, distinctive identifying capabilities, real-time monitoring, stability, label-free detection, and remarkable sensitivity for detecting individual molecules. Nevertheless, since the use of these techniques for cancer biomarker detection is relatively new, it is essential to conduct a bibliometric analysis and review recent literature to offer guidance to both early-career and established researchers in this domain. Consequently, this study performs a scientometric evaluation of the literature related to cancer biomarker detection using MR and magneto-plasmonic methods. The objective is to pinpoint current preferred techniques and challenges by examining statistics such as publication numbers, authors, countries, journals, and research interests. Furthermore, the paper also presents the latest advancements in MR and magneto-plasmonic sensors for cancer biomarker detection, with a focus on the last decade. In addition, an overview of the ongoing research in the field of MR and magneto-plasmonic sensors for detecting cancer biomarkers is highlighted. Finally, a summary on the level of current research including the significant accomplishments, challenges, and outlooks of MR and magneto-plasmonic sensors for the detection of cancer biomarkers are highlighted. © 2023