Stomach tissue classification using autofluorescence spectroscopy and machine learning
Name
464_2023_10053_ReferencePDF.pdf
Size
1.83 MB
Format
Adobe PDF
Checksum (MD5)
cbb14aba7a4694bdd08a1d0c4627f156
Author(s)
Lim, Soo Y.
Yoon, Hong M.
Kook, Myeong-Cherl
Jang, Jin I.
So, Peter T. C.
Kang, Jeon W.
Kim, Hyung M.
Date Issued
April 13, 2023
Publisher
Springer US
Citation
Lim, Soo Y., Yoon, Hong M., Kook, Myeong-Cherl, Jang, Jin I., So, Peter T. C. et al. 2023. "Stomach tissue classification using autofluorescence spectroscopy and machine learning."
Version
Author's final manuscript
Abstract
Abstract
Background and objectives
Determination of stomach tumor location and invasion depth requires delineation of gastric histological structure, which has hitherto been widely accomplished by histochemical staining. In recent years, alternative histochemical evaluation methods have been pursued to accelerate intraoperative diagnosis, often by bypassing the time-consuming step of dyeing. Owing to strong endogenous signals from coenzymes, metabolites, and proteins, autofluorescence spectroscopy is a favorable candidate technique to achieve this aim.
Materials and methods
We investigated stomach tissue slices and block specimens using a fast fluorescence imaging scanner. To obtain histological information from broad and structureless fluorescence spectra, we analyzed tens of thousands of spectra with multiple machine-learning algorithms and built a tissue classification model trained with dissected gastric tissues.
Results
A machine-learning-based spectro-histological model was built based on the autofluorescence spectra measured from stomach tissue samples with delineated and validated histological structures. The scores from a principal components analysis were employed as input features, and prediction accuracy was confirmed to be 92.0%, 90.1%, and 91.4% for mucosa, submucosa, and muscularis propria, respectively. We investigated the tissue samples in both sliced and block forms using a fast fluorescence imaging scanner.
Conclusion
We successfully demonstrated differentiation of multiple tissue layers of well-defined specimens with the guidance of a histologist. Our spectro-histology classification model is applicable to histological prediction for both tissue blocks and slices, even though only sliced samples were trained.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s00464-023-10053-6
