TY - GEN
T1 - Angular Photodiode Array for the Identification of Colorectal Carcinoma by Mie Scatter
AU - Bills, Matthew
AU - Yoon, Jeong Yeol
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - Cancer tissues differ from normal tissues in a myriad of ways including cellular changes in size, morphology, and type/density of membrane and cytosolic proteins, which will accordingly change the refractive index of cells. There are also important differences in tissue organization, perhaps most notably the increase and morphologic changes in capillary vessels and extracellular matrix organization. Our laboratory has recently developed a novel technology and early prototype that can be used to detect some of these physiologic changes by collecting the light scattering intensities over a range of scattering angles from animal and human colonic tissues. To classify tumors, we employed multivariate analysis tools including Principal Component Analysis and Support Vector Machines to help discriminate between healthy and cancerous tissue samples. The preliminary results revealed a consistent and relatively accurate classification of tissues. These findings show promise for the noninvasive, label free, low cost, and rapid (under a minute) detection of surface tumors.
AB - Cancer tissues differ from normal tissues in a myriad of ways including cellular changes in size, morphology, and type/density of membrane and cytosolic proteins, which will accordingly change the refractive index of cells. There are also important differences in tissue organization, perhaps most notably the increase and morphologic changes in capillary vessels and extracellular matrix organization. Our laboratory has recently developed a novel technology and early prototype that can be used to detect some of these physiologic changes by collecting the light scattering intensities over a range of scattering angles from animal and human colonic tissues. To classify tumors, we employed multivariate analysis tools including Principal Component Analysis and Support Vector Machines to help discriminate between healthy and cancerous tissue samples. The preliminary results revealed a consistent and relatively accurate classification of tissues. These findings show promise for the noninvasive, label free, low cost, and rapid (under a minute) detection of surface tumors.
KW - Cancer detection
KW - Label-free
KW - Mie scatter
KW - Principal Component Analysis
KW - Support Vector Machines
UR - http://www.scopus.com/inward/record.url?scp=85078702285&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85078702285&partnerID=8YFLogxK
U2 - 10.1109/SENSORS43011.2019.8956839
DO - 10.1109/SENSORS43011.2019.8956839
M3 - Conference contribution
AN - SCOPUS:85078702285
T3 - Proceedings of IEEE Sensors
BT - 2019 IEEE Sensors, SENSORS 2019 - Conference Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th IEEE Sensors, SENSORS 2019
Y2 - 27 October 2019 through 30 October 2019
ER -