Sensor technologies in cancer research for new directions in diagnosis and treatment: and exploratory analysis
Auteurs : Mario Coccia, Saeed Roshani, Melika Mosleh
Résumé : The goal of this study is an exploratory analysis concerning main sensor technologies applied in cancer research to detect new directions in diagnosis and treatments. The study focused on types of cancer having a high incidence and mortality worldwide: breast, lung, colorectal and prostate. Data of the Web of Science (WOS) core collection database are used to retrieve articles related to sensor technologies and cancer research over 1991-2021 period. We utilized Gephi software version 0.9.2 to visualize the co-word networks of the interaction between sensor technologies and cancers under study. Results show main clusters of interaction per typology of cancer. Biosensor is the only type of sensor that plays an essential role in all types of cancer: breast cancer, lung cancer, prostate cancer, and colorectal cancer. Electrochemical sensor is applied in all types of cancer under study except lung cancer. Electrochemical biosensor is used in breast cancer, lung cancer, and prostate cancer research but not colorectal cancer. Optical sensor can also be considered one of the sensor technologies that significantly is used in breast cancer, prostate cancer, and colorectal cancer. This study shows that this type of sensor is applied in more diversified approaches. Moreover, the oxygen sensor is mostly studied in lung cancer and breast cancer due to the usage in breath analysis for the treatment process. Finally, Cmos sensor is a technology used mainly in lung cancer and colorectal cancer. Results here suggest new directions for the evolution of science and technology of sensors in cancer research to support innovation and research policy directed to new technological trajectories having a potential of accelerated growth and positive social impact for diagnosis and treatments of cancer.
Explorez l'arbre d'article
Cliquez sur les nœuds de l'arborescence pour être redirigé vers un article donné et accéder à leurs résumés et assistant virtuel
Recherchez des articles similaires (en version bêta)
En cliquant sur le bouton ci-dessus, notre algorithme analysera tous les articles de notre base de données pour trouver le plus proche en fonction du contenu des articles complets et pas seulement des métadonnées. Veuillez noter que cela ne fonctionne que pour les articles pour lesquels nous avons généré des résumés et que vous pouvez le réexécuter de temps en temps pour obtenir un résultat plus précis pendant que notre base de données s'agrandit.