Analysis of Mouse Blood Serum in the Dynamics of U87 Glioblastoma by Terahertz Spectroscopy and Machine Learning

Vrazhnov, D.; Knyazkova, A.; Konnikova, M.; Shevelev, O.; Razumov, I.; Zavjalov, E.; Kistenev, Y.; Shkurinov, A.; Cherkasova, O.

Авторы: Vrazhnov Denis, Knyazkova Anastasia, Konnikova Maria, Shevelev Oleg, Razumov Ivan, Zavjalov Evgeny, Kistenev Yury, Shkurinov Alexander, Cherkasova Olga
Журнал: Applied Sciences
Том: 12
Номер: 20
Год издания: 2022
Издательство: MDPI
Местоположение издательства: Basel, Switzerland
DOI: 10.3390/app122010533
Аннотация: In this research, an experimental U87 glioblastoma small animal model was studied. The association between glioblastoma stages and the spectral patterns of mouse blood serum measured in the terahertz range was analyzed by terahertz time-domain spectroscopy (THz-TDS) and machine learning. The THz spectra preprocessing included (i) smoothing using the Savitsky–Golay filter, (ii) outlier removing using isolation forest (IF), and (iii) Z-score normalization. The sequential informative feature-selection approach was developed using a combination of principal component analysis (PCA) and a support vector machine (SVM) model. The predictive data model was created using SVM with a linear kernel. This model was tested using k-fold cross-validation. Achieved prediction accuracy, sensitivity, specificity were over 90%. Also, a relation was established between tumor size and the THz spectral profile of blood serum samples. Thereby, the possibility of detecting glioma stages using blood serum spectral patterns in the terahertz range was demonstrated.
Добавил в систему: Шкуринов Александр Павлович

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Analysis of Mouse Blood Serum in the Dynamics of U87 Glioblastoma by Terahertz Spectroscopy and Machine Learningстатья

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