Terahertz Time-Domain Spectroscopy of Glioma Patient Blood Plasma: Diagnosis and Treatment
Source abstract
Abstract
Gliomas, one of the most severe malignant tumors of the central nervous system, have a high mortality rate and an increased risk of recurrence. Therefore, early glioma diagnosis and the control of treatment have great significance. The blood plasma samples of glioma patients, patients with skull craniectomy defects, and healthy donors were studied using terahertz time-domain spectroscopy (THz-TDS). An analysis of experimental THz data was performed by machine learning (ML). The ML pipeline included (i) THz spectra smoothing using the Savitzky–Golay filter, (ii) dimension reduction with principal component analysis and t-distribution stochastic neighborhood embedding methods; (iii) data separability analyzed using Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The ML models’ performance was evaluated by a k-fold cross validation technique using ROC-AUC, sensitivity, and specificity metrics. It was shown that tree-based ensemble methods work more accurately than SVM. RF and XGBoost provided a better differentiation of the group of patients with glioma from healthy donors and patients with skull craniectomy defects. THz-TDS combined with ML was shown to make it possible to separate the blood plasma of patients before and after tumor removal surgery (AUC = 0.92). Thus, the applicability of THz-TDS and ML for the diagnosis of glioma and treatment monitoring has been shown.
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Research context
OpenAlex topics: Terahertz technology and applications · Gene expression and cancer classification · Spectroscopy Techniques in Biomedical and Chemical Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Institute of Automation and Electrometry, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
Institute on Laser and Information Technologies, Branch of the Federal Scientific Research Centre “Crystallography and Photonics” of RAS, 140700 Shatura, Russia
Laboratory of Laser Molecular Imaging and Machine Learning, Tomsk State University, 634050 Tomsk, Russia
V.E. Zuev Institute of Atmospheric Optics SB RAS, Academician Zuev Square, 1, 634055 Tomsk, Russia
Faculty of Physics, Lomonosov Moscow State University, 119991 Moscow, Russia
Novosibirsk Research Institute of Traumatology and Orthopedics n.a. Ya.L. Tsivyan, 630091 Novosibirsk, Russia
Novosibirsk Division of Rzhanov Institute of Semiconductor Physics Siberian Branch of the Russian Academy of Sciences “Technological Design Institute of Applied Microelectronics”, 630090 Novosibirsk, Russia
Faculty of Radio Engineering and Electronics, Novosibirsk State Technical University, Karl Marks Avenue, 20, 630073 Novosibirsk, Russia
Terahertz Biomedical Laboratory, University of Shanghai for Science and Technology, 516 Jungong Road, Yangpu District, Shanghai 200093, China
Funding
the Ministry of Science and Higher Education of the Russian Federation · a grant under the Decree of the Government of the Russian Federation
References (64)
Show source-provided references
1. Ferlay, J., Colombet, M., Soerjomataram, I., Parkin, D.M., Piñeros, M., Znaor, A., and Bray, F. (2021). Cancer statistics for the year 2020: An overview. Int. J. Cancer.10.1002/ijc.33588 ↗
2. The 2021 WHO Classification of Tumors of the Central Nervous System: A summary10.1093/neuonc/noab106 ↗
3. CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2015–201910.1093/neuonc/noac202 ↗
4. Glioblastoma, a brief review of history, molecular genetics, animal models and novel therapeutic strategies10.1007/s00005-012-0203-0 ↗
5. Ten-year survival in glioblastoma. A systematic review10.1016/j.jocn.2018.05.002 ↗
6. The WHO 2021 Classification of Central Nervous System tumours: A practical update on what neurosurgeons need to know-a minireview10.1007/s00701-022-05301-y ↗
7. Advanced Imaging Techniques for Newly Diagnosed and Recurrent Gliomas10.3389/fnins.2022.787755 ↗
8. Advanced MR techniques in glioblastoma imaging—Upcoming challenges and how to face them10.1007/s00330-021-07978-8 ↗
9. Standard clinical approaches and emerging modalities for glioblastoma imaging10.1093/noajnl/vdac080 ↗
10. Rucco, M., and Viticchi, G. (2020). Fast Glioblastoma Detection in Fluid-attenuated inversion recovery (FLAIR) images by Topological Explainable Automatic Machine Learning. arXiv.10.3390/math8050770 ↗
11. Mărginean, L., Ștefan, P.A., Lebovici, A., Opincariu, I., Csutak, C., Lupean, R.A., Coroian, P.A., and Suciu, B.A. (2022). CT in the Differentiation of Gliomas from Brain Metastases: The Radiomics Analysis of the Peritumoral Zone. Brain Sci., 12.10.3390/brainsci12010109 ↗
12. Complementary but distinct roles for MRI and 18F-Fluoromisonidazole PET in the assessment of human glioblastomas10.2967/jnumed.108.055467 ↗
13. Primary glioblastoma multiforme in youngers patients: A single-instruction experience10.1177/030089160609200507 ↗
14. Identification of Histological Correlates of Overall Survival in Lower Grade Gliomas Using a Bag-of-words Paradigm: A Preliminary Analysis Based on Hematoxylin & Eosin Stained Slides from the Lower Grade Glioma Cohort of The Cancer Genome Atlas10.4103/jpi.jpi_43_16 ↗
15. Cancer collection efforts in the United States provide clinically relevant data on all primary brain and other CNS tumors
16. Malignant astrocytic glioma: Genetics, biology, and paths to treatment10.1101/gad.1596707 ↗
17. Targeting cellular pathways in glioblastoma multiforme10.1038/sigtrans.2017.40 ↗
18. Targeting Immunometabolism in Glioblastoma10.3389/fonc.2021.696402 ↗
19. TGF-beta signaling and its targeting for glioma treatment
20. Mazumder, N., Gangadharan, G., and Kistenev, Y.V. (2022). Advances in Brain Imaging Techniques, Springer.10.1007/978-981-19-1352-5 ↗
21. Liquid biopsy: A step closer to transform diagnosis, prognosis and future of cancer treatments10.1186/s12943-022-01543-7 ↗
22. Ronvaux, L., Riva, M., Coosemans, A., Herzog, M., Rommelaere, G., Donis, N., D’Hondt, L., and Douxfils, J. (2022). Liquid Biopsy in Glioblastoma. Cancers, 14.10.3390/cancers14143394 ↗
23. 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 ↗
24. Altered cellular metabolism in gliomas—An emerging landscape of actionable co-dependency targets10.1038/s41568-019-0226-5 ↗
25. Blood-Based Biomarkers for Glioma in the Context of Gliomagenesis: A Systematic Review10.3389/fonc.2021.665235 ↗
26. Isomers Identification of 2-hydroxyglutarate acid disodium salt (2HG) by Terahertz Time-domain Spectroscopy10.1038/s41598-017-11527-z ↗
27. Advances in terahertz technology for cancer detection applications10.1007/s11082-022-04340-0 ↗
28. Atomic and vibrational origins of mechanical toughness in bioactive cement during setting10.1038/ncomms9631 ↗
29. Resolving nanoscopic structuring and interfacial THz dynamics in setting cements10.1039/D1MA01002F ↗
30. Terahertz biopho-tonics as a tool for studies of dielectric and spectral properties of biological tissues and liquids10.1016/j.pquantelec.2018.10.001 ↗
31. The progress and perspectives of terahertz technology for diagnosis of neoplasms: A review10.1088/2040-8986/ab4dc3 ↗
32. Terahertz spectroscopy of gelatin-embedded human brain gliomas of different grades: A road toward intraoperative THz diagnosis10.1117/1.JBO.24.2.027001 ↗
33. Terahertz Imaging and Spectroscopy in Cancer Diagnostics: A Technical Review10.34133/2020/2547609 ↗
34. Biomedical applications of terahertz spectra in clinical and molecular pathology of human glioma10.1016/j.saa.2022.121933 ↗
35. Application of terahertz spectroscopy in biomolecule detection10.1016/j.flm.2019.05.001 ↗
36. Malignant and benign thyroid nodule differentiation through the analysis of blood plasma with terahertz spectroscopy10.1364/BOE.412715 ↗
37. Diagnosis of Diabetes Based on Analysis of Exhaled Air by Terahertz Spectroscopy and Machine Learning10.1134/S0030400X20060090 ↗
38. Terahertz spectroscopy of diabetic and non-diabetic human blood plasma pellets
39. Diagnosis of oral lichen planus from analysis of saliva samples using terahertz time-domain spectroscopy and chemometrics10.1117/1.JBO.23.4.045001 ↗
40. A complex study of the peculiarities of blood serum absorption of rats with experimental liver cancer10.1134/S0030400X19060183 ↗
41. Vrazhnov, D., Knyazkova, A., Konnikova, M., Shevelev, O., Razumov, I., Zavjalov, E., Kistenev, Y., Shkurinov, A., and Cherkasova, O. (2022). Analysis of Mouse Blood Serum in the Dynamics of U87 Glioblastoma by Terahertz Spectroscopy and Machine Learning. Appl. Sci., 12.10.3390/app122010533 ↗
42. Terahertz spectroscopic diagnosis of early blast-induced traumatic brain injury in rats. Biomed10.1364/BOE.395432 ↗
43. Machine Learning Enhanced Optical Spectroscopy for Disease Detection10.1021/acs.jpclett.2c02193 ↗
44. Stochastic neighbor embedding
45. Visualizing data using t-SNE
46. Vrazhnov, D., Mankova, A., Stupak, E., Kistenev, Y., Shkurinov, A., and Cherkasova, O. (2023). Discovering Glioma Tissue through Its Biomarkers’ Detection in Blood by Raman Spectroscopy and Machine Learning. Pharmaceutics, 15.10.3390/pharmaceutics15010203 ↗
47. Uddin, S., Khan, A., Hossain, E., and Moni, M.A. (2019). Comparing different supervised machine learning algorithms for disease prediction. BMC Med. Inform. Decis. Mak., 19.10.1186/s12911-019-1004-8 ↗
48. Hinton, G., Oriol, V., and Jeff, D. (2015). Distilling the knowledge in a neural network. arXiv.
49. Shapley values for feature selection: The good, the bad, and the axioms10.1109/ACCESS.2021.3119110 ↗
50. Problems with Shapley-value-based explanations as feature importance measures
51. A review of feature selection and feature extraction methods applied on microarray data10.1155/2015/198363 ↗
52. Improving protein-protein interactions prediction accuracy using XGBoost feature selection and stacked ensemble classifier10.1016/j.compbiomed.2020.103899 ↗
53. XGBoost model for chronic kidney disease diagnosis
54. Medical applications of laser molecular imaging and machine learning
55. (2023, April 20). Available online: https://www.teravil.lt/t-spec.php.
56. Analysis of the Spectral Characteristics of Promising Liquid Carriers in the Terahertz Spectral Range10.1007/s11182-019-01726-8 ↗
57. Comparative study of classifiers for human microbiome data10.1016/j.medmic.2020.100013 ↗
58. Comparison of machine learning classifiers for differentiation of grade 1 from higher gradings in meningioma: A multicenter radiomics study10.1016/j.mri.2019.08.011 ↗
59. A comprehensive review of dimensionality reduction techniques for feature selection and feature extraction10.38094/jastt1224 ↗
60. Spectral model for diagnosis of acute leukemias in whole blood and plasma through Raman spectroscopy
61. Detection of circulating tumor DNA in early- and late-stage human malignancies10.1126/scitranslmed.3007094 ↗
62. Park, H., and Son, J.H. (2021). Machine Learning Techniques for THz Imaging and Time-Domain Spectroscopy. Sensors, 21.10.3390/s21041186 ↗
63. Prediction of IDH mutation status of glioma based on terahertz spectral data10.1016/j.saa.2023.122629 ↗
64. Potential impact of invasive surgical procedures on primary tumor growth and metastasis10.1007/s10585-018-9896-8 ↗