Diagnosis of intraoperative hepatocellular carcinoma based on FTIR vibration spectroscopy using supervised machine learning

Wenyu Peng; Gangqiang Cui; Shuo Chen; Xiaoyun Lu

doi:10.1117/12.2574177

5 November 2020 Diagnosis of intraoperative hepatocellular carcinoma based on FTIR vibration spectroscopy using supervised machine learning

Wenyu Peng, Gangqiang Cui, Shuo Chen, Xiaoyun Lu

Author Affiliations +

Proceedings Volume 11566, AOPC 2020: Optical Spectroscopy and Imaging; and Biomedical Optics; 1156604 (2020) https://doi.org/10.1117/12.2574177
Event: Applied Optics and Photonics China (AOPC 2020), 2020, Beijing, China

Abstract

Intraoperative diagnosis plays an essential role in cancer surgery by providing fast and accurate information to clinicians to make a decision. The standard workflow for histopathology based intraoperative diagnosis is generally considered to be time-consuming and labor-intensive. Fourier transform infrared (FTIR) vibration spectroscopy technique has been demonstrated to be a useful tool that yields a molecular fingerprint and provides rapid, nondestructive, high-throughput and clinically relevant diagnostic information. In this study, FTIR spectrometer based on synchrotron radiation was applied to collect the IR spectra of the liver cancer tissues and the adjacent non-cancer tissues of hepatocellular carcinoma (HCC) patients. The FTIR data demonstrated that the ratio of 2959/2926cm^-1, 1654/1548cm^-1, 1084/1548cm^-1and 1455/1398cm^-1 had a significant difference between the two groups, which could serve as indicators to distinguish the liver cancer region from the adjacent non-cancer tissue. Then the supervised machine learning techniques including discriminant analysis coupled with principal component analysis (PCA-DA), support vector machines (SVM) and backpropagation neural networks (BPNN) were applied to classify the spectra data. Finally the performance of these models, such as their precision, sensitivity, specificity and accuracy was assessed, and the results have proved that coupling the FTIR vibration spectroscopy with supervised machine learning techniques could be considered as an accurate and efficient method for the intraoperative diagnosis of HCC.

Citation Download Citation

Wenyu Peng, Gangqiang Cui, Shuo Chen, and Xiaoyun Lu "Diagnosis of intraoperative hepatocellular carcinoma based on FTIR vibration spectroscopy using supervised machine learning", Proc. SPIE 11566, AOPC 2020: Optical Spectroscopy and Imaging; and Biomedical Optics, 1156604 (5 November 2020); https://doi.org/10.1117/12.2574177

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
5 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Tissues

FT-IR spectroscopy

Spectroscopy

Liver cancer

Machine learning

Cancer

Absorbance

Show All Keywords

Keywords/Phrases

Search In:

Publication Years