Leveraging mid-infrared spectroscopic imaging and deep learning for tissue subtype classification in ovarian cancer-Reference-Cited by-同舟云学术

Leveraging mid-infrared spectroscopic imaging and deep learning for tissue subtype classification in ovarian cancer

Published:2023 Issue:12 Volume:148 Page:2699-2708
ISSN:0003-2654
Container-title:The Analyst
language:en
Short-container-title:Analyst

Author:

Gajjela Chalapathi Charan¹,Brun Matthew²^ORCID,Mankar Rupali¹,Corvigno Sara³,Kennedy Noah⁴,Zhong Yanping⁵³,Liu Jinsong³,Sood Anil K.³,Mayerich David¹^ORCID,Berisha Sebastian⁴,Reddy Rohith¹^ORCID

Affiliation:

1. University of Houston, 4226 Martin Luther King Boulevard, N308 Engineering Building 1, Houston, TX, 77584, USA

2. Rice University, Houston, TX, USA

3. The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA

4. Milwaukee School of Engineering, Milwaukee, WI, USA

5. The First Hospital of Jilin University, Changchun, Jilin, 130021, China

Abstract

This study introduces label-free, automated ovarian tissue cell recognition using O-PTIR imaging, offering 10× better resolution than FTIR. It outperforms FTIR, achieving 0.98 classification accuracy. This work aids early ovarian cancer diagnosis.

Funder

American Cancer Society

Frank McGraw Memorial Chair in Cancer Research

Cancer Prevention and Research Institute of Texas

National Science Foundation

U.S. National Library of Medicine

National Institutes of Health

Publisher

Royal Society of Chemistry (RSC)

Subject

Electrochemistry,Spectroscopy,Environmental Chemistry,Biochemistry,Analytical Chemistry

Link