Nina Reistad
Universitetslektor
Distinguishing tumor from healthy tissue in human liver ex vivo using machine learning and multivariate analysis of diffuse reflectance spectra
Författare
Summary, in English
The aim of this work was to evaluate the capability of diffuse reflectance spectroscopy to distinguish malignant liver tissues from surrounding tissues, and to determine whether an extended wavelength range (450–1550 nm) offers any advantages over using the conventional wavelength range. Furthermore, multivariate analysis combined with a machine learning algorithm, either linear discriminant analysis or the more advanced support vector machine, was used to discriminate between and classify freshly excised human liver specimens from 18 patients. Tumors were distinguished from surrounding liver tissues with a sensitivity of 99%, specificity of 100%, classification rate of 100%, and a Matthews correlation coefficient of 100% using the extended wavelength range and a combination of principal component analysis and support vector techniques. The results indicate that this technology may be useful in clinical applications for real-time tissue diagnostics of tumor margins where rapid classification is important.
Avdelning/ar
- Atomfysik
Publiceringsår
2022-07-12
Språk
Engelska
Publikation/Tidskrift/Serie
Journal of Biophotonics
Volym
15
Issue
10
Dokumenttyp
Artikel i tidskrift
Förlag
John Wiley & Sons Inc.
Ämne
- Radiology, Nuclear Medicine and Medical Imaging
Nyckelord
- diffuse reflectance spectroscopy
- extended wavelength region
- human liver tissues
- multivariate analysis
- discriminant analysis
- linear discriminant analysis
- support vector machine
- machine learning
Status
Published
ISBN/ISSN/Övrigt
- ISSN: 1864-0648