Analysis of Mouse Blood Serum in the Dynamics of U87 Glioblastoma by Terahertz Spectroscopy and Machine Learning
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Terahertz technology and applications · Spectroscopy Techniques in Biomedical and Chemical Research · Advanced Chemical Sensor Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Laboratory of Biophotonics, Tomsk State University, 634050 Tomsk, Russia
V.E. Zuev Institute of Atmospheric Optics of Siberian Branch of the Russian Academy of Sciences, Academician Zuev Square, 1, 634055 Tomsk, Russia
Faculty of Physics, Lomonosov Moscow State University, 119991 Moscow, Russia
Institute on Laser and Information Technologies, Branch of the Federal Scientific Research Centre “Crystallography and Photonics” of the Russian Academy of Sciences, 140700 Shatura, Russia
Federal Research Center “Institute of Cytology and Genetics of the Siberian Branch of the RAS”, 630090 Novosibirsk, Russia
Institute of Laser Physics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
Faculty of Automation and Computer Engineering, Novosibirsk State Technical University, 630073 Novosibirsk, Russia
Funding
Russian Foundation for Basic Research and National Natural Science Foundation of China · Ministry of Science and Higher Education of the Russian Federation · Interdisciplinary Scientific and Educational School of Moscow University · Tomsk State University Development Program
References (46)
Show source-provided references
1. Glioblastoma, a brief review of history, molecular genetics, animal models and novel therapeutic strategies10.1007/s00005-012-0203-0 ↗
2. CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2014–201810.1093/neuonc/noab200 ↗
3. Ten-year survival in glioblastoma. A systematic review10.1016/j.jocn.2018.05.002 ↗
4. Diagnosis and Treatment of Early-Stage Glioblastoma10.4103/ajns.AJNS_18_19 ↗
5. Application of Metabolomics in Cancer Research: As a Powerful Tool to Screen Biomarker for Diagnosis, Monitoring and Prognosis of Cancer
6. Quantitation of isocitrate dehydrogenase (IDH)-induced D and L enantiomers of 2-hydroxyglutaric acid in biological fluids by a fully validated liquid tandem mass spectrometry method, suitable for clinical applications10.1016/j.jchromb.2016.04.030 ↗
7. Emerging circulating biomarkers in glioblastoma: Promises and challenges
8. Miyauchi, E., Furuta, T., Ohtsuki, S., Tachikawa, M., Uchida, Y., Sabit, H., Obuchi, W., Baba, T., Watanabe, M., and Terasaki, T. (2018). Identification of blood biomarkers in glioblastoma by SWATH mass spectrometry and quantitative targeted absolute proteomics. PLoS ONE, 13.10.1371/journal.pone.0193799 ↗
9. Metabolomic profiling of blood plasma in patients with primary brain tumours: Basal plasma metabolites correlated with tumour grade and plasma biomarker analysis predicts feasibility of the successful statistical discrimination from healthy subjects—A preliminary study10.1002/iub.2149 ↗
10. Metabolic profiling of human gliomas assessed with NMR10.1016/j.jocn.2019.07.078 ↗
11. Detection of glioblastoma in biofluids10.3171/2017.3.JNS162280 ↗
12. Development of high-throughput ATR-FTIR technology for rapid triage of brain cancer10.1038/s41467-019-12527-5 ↗
13. Theakstone, A.G., Brennan, P.M., Jenkinson, M.D., Mills, S.J., Syed, K., Rinaldi, C., Xu, Y., Goodacre, R., Butler, H.J., and Palmer, D.S. (2021). Rapid Spectroscopic Liquid Biopsy for the Universal Detection of Brain Tumours. Cancers, 13.10.3390/cancers13153851 ↗
14. Cherkasova, O., Peng, Y., Konnikova, M., Kistenev, Y., Shi, C., Vrazhnov, D., Shevelev, O., Zavjalov, E., Kuznetsov, S., and Shkurinov, A. (2021). Diagnosis of Glioma Molecular Markers by Terahertz Technologies. Photonics, 8.10.3390/photonics8010022 ↗
15. A complex study of the peculiarities of blood serum absorption of rats with experimental liver cancer10.1134/S0030400X19060183 ↗
16. Malignant and benign thyroid nodule differentiation through the analysis of blood plasma with terahertz spectroscopy10.1364/BOE.412715 ↗
17. MRI of Mouse Models for Gliomas Shows Similarities to Humans and Can Be Used to Identify Mice for Preclinical Trials10.1038/sj.neo.7900269 ↗
18. In vivo MRI Visualization of U87 Glioblastoma Development Dynamics in the Model of Orthotopic Xenotransplantation to the SCID Mouse10.1134/S2079059716040225 ↗
19. Epidermal growth factor receptor and Ink4a/Arf: Convergent mechanisms governing terminal differentiation and transformation along the neural stem cell to astrocyte axis10.1016/S1535-6108(02)00046-6 ↗
20. Terahertz biophotonics as a tool for studies of dielectric and spectral properties of biological tissues and liquids10.1016/j.pquantelec.2018.10.001 ↗
21. A Comprehensive Study of Albumin Solutions in the Extended Terahertz Frequency Range10.1007/s10762-018-0513-3 ↗
22. Koul, S.K., and Kaurav, P. (2022). Machine Learning and Biomedical Sub-Terahertz/Terahertz Technology. Sub-Terahertz Sensing Technology for Biomedical Applications. Biological and Medical Physics, Biomedical Engineering, Springer.10.1007/978-981-19-3140-6 ↗
23. Park, H., and Son, J.-H. (2021). Machine Learning Techniques for THz Imaging and Time-Domain Spectroscopy. Sensors, 21.10.3390/s21041186 ↗
24. Paraffin-embedded prostate cancer tissue grading using terahertz spectroscopy and machine learning10.1007/s10762-020-00673-7 ↗
25. Terahertz spectroscopy in biomedical field: A review on signal-to-noise ratio improvemen10.1186/s43074-020-00011-z ↗
26. Terahertz spectroscopic diagnosis of early blast-induced traumatic brain injury in rats10.1364/BOE.395432 ↗
27. Smoothing method to directly denoise terahertz signals in rare-earth orthoferrite antiferromagnets10.1111/jace.17712 ↗
28. Smoothing and differentiation of data by simplified least squares procedures10.1021/ac60214a047 ↗
29. Stephani, H., Jonuscheit, J., Robiné, C., and Heise, B. (2010, January 23–26). Automatically detecting peaks in terahertz time-domain spectroscopy. Proceedings of the 20th International Conference on Pattern Recognition 2010, Istanbul, Turkey.10.1109/ICPR.2010.1085 ↗
30. Kalman filtering in the problem of noise reduction in the absorption spectra of exhaled air
31. Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference10.1038/s41467-018-05892-0 ↗
32. Explanation of machine learning models using shapley additive explanation and application for real data in hospital10.1016/j.cmpb.2021.106584 ↗
33. An introduction to variable and feature selection
34. Study of Blood Serum in Rats with Transplanted Cholangiocarcinoma Using Raman Spectroscopy10.1134/S0030400X20070115 ↗
35. Analysis of the Spectral Characteristics of Promising Liquid Carriers in the Terahertz Spectral Range10.1007/s11182-019-01726-8 ↗
36. Optical Properties of 3D Printable Plastics in the THz Regime and their Application for 3D Printed THz Optics10.1007/s10762-014-0113-9 ↗
37. Liu, F.T., Ting, K.M., and Zhou, Z.H. (2008, January 15–19). Isolation forest. Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, Pisa, Italy.10.1109/ICDM.2008.17 ↗
38. Anomaly detection: A survey10.1145/1541880.1541882 ↗
39. Regression shrinkage and selection via the lasso10.1111/j.2517-6161.1996.tb02080.x ↗
40. Raman spectroscopy of blood plasma for cancer diagnosis
41. Cherkasova, O., Konnikova, M., Heinz, T., Nazarov, M., Kuryanova, A., Aksenova, N., Mankova, A., Vrazhnov, D., Kistenev, Y., and Peng, Y. (September, January 29). Terahertz and infrared absorption spectra of mouse blood plasma in glioma development. Proceedings of the 46th International Conference on Infrared, Millimeter and Terahertz Waves (IRMMW-THz 2021), Chengdu, China.10.1109/IRMMW-THz50926.2021.9567163 ↗
42. Problems with Shapley-value-based explanations as feature importance measures
43. (1994). Accuracy (Trueness and Precision) of Measurement Methods and Results—Part 1: General Principles and Definitions (Standard No. BS ISO 5725-1). Available online: https://www.iso.org/obp/ui/#iso:std:iso:5725:-1:ed-1:v1:en.
44. On the performance of Matthews correlation coefficient (MCC) for imbalanced dataset10.1016/j.patrec.2020.03.030 ↗
45. Toward evidence-based medical statistics. 1: The P value fallacy10.7326/0003-4819-130-12-199906150-00008 ↗
46. The fickle P value generates irreproducible results10.1038/nmeth.3288 ↗