An application of raman spectroscopy in combination with machine learning to determine gastric cancer spectroscopy marker
Copyright © 2023 Elsevier B.V. All rights reserved..
BACKGROUND AND OBJECTIVE: Globally, gastric carcinoma (Gca) ranks fifth in terms of incidence and third in terms of mortality. Higher serum tumor markers (TMs) than those from healthy individuals, led to TMs clinical application as diagnostic biomarkers for Gca. Actually, there is no accurate blood test to diagnose Gca.
METHODS: Raman spectroscopy is applied as an efficient, credible, minimally invasive technique to evaluate the serum TMs levels in blood samples. After curative gastrectomy, serum TMs levels are important in predicting the recurrence of gastric cancer, which must be detected early. The experimentally assesed TMs levels using Raman measurements and ELİSA test were used to develop a prediction model based on machine learning techniques. A total of 70 participants diagnosed with gastric cancer after surgery (n = 26) and healthy (n = 44) were comrpised in this study.
RESULTS: In the Raman spectra of gastric cancer patients, an additional peak at 1182 cm-1 was observed and, the Raman intensity of amide III, II, I, and CH2 proteins as well as lipids functional group was higher. Furthermore, Principal Component Analysis (PCA) showed, that it is possible to distinguish between the control and Gca groups using the Raman range between 800 and 1800 cm-1, as well as between 2700 and 3000 cm-1. The analysis of Raman spectra dynamics in gastric cancer and healthy patients showed, that the vibrations at 1302 and 1306 cm-1 were characteristic for cancer patients. In addition, the selected machine learning methods showed classification accuracy of more than 95%, while obtaining an AUROC of 0.98. Such results were obtained using Deep Neural Networks and the XGBoost algorithm.
CONCLUSIONS: The obtained results suggest, that Raman shifts at 1302 and 1306 cm-1 could be spectroscopic markers of gastric cancer.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:234 |
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| Enthalten in: |
Computer methods and programs in biomedicine - 234(2023) vom: 10. Juni, Seite 107523 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Guleken, Zozan [VerfasserIn] |
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| Links: |
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| Themen: |
Biomarkers |
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| Anmerkungen: |
Date Completed 25.04.2023 Date Revised 25.04.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.cmpb.2023.107523 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM355347881 |
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| 245 | 1 | 3 | |a An application of raman spectroscopy in combination with machine learning to determine gastric cancer spectroscopy marker |
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| 520 | |a Copyright © 2023 Elsevier B.V. All rights reserved. | ||
| 520 | |a BACKGROUND AND OBJECTIVE: Globally, gastric carcinoma (Gca) ranks fifth in terms of incidence and third in terms of mortality. Higher serum tumor markers (TMs) than those from healthy individuals, led to TMs clinical application as diagnostic biomarkers for Gca. Actually, there is no accurate blood test to diagnose Gca | ||
| 520 | |a METHODS: Raman spectroscopy is applied as an efficient, credible, minimally invasive technique to evaluate the serum TMs levels in blood samples. After curative gastrectomy, serum TMs levels are important in predicting the recurrence of gastric cancer, which must be detected early. The experimentally assesed TMs levels using Raman measurements and ELİSA test were used to develop a prediction model based on machine learning techniques. A total of 70 participants diagnosed with gastric cancer after surgery (n = 26) and healthy (n = 44) were comrpised in this study | ||
| 520 | |a RESULTS: In the Raman spectra of gastric cancer patients, an additional peak at 1182 cm-1 was observed and, the Raman intensity of amide III, II, I, and CH2 proteins as well as lipids functional group was higher. Furthermore, Principal Component Analysis (PCA) showed, that it is possible to distinguish between the control and Gca groups using the Raman range between 800 and 1800 cm-1, as well as between 2700 and 3000 cm-1. The analysis of Raman spectra dynamics in gastric cancer and healthy patients showed, that the vibrations at 1302 and 1306 cm-1 were characteristic for cancer patients. In addition, the selected machine learning methods showed classification accuracy of more than 95%, while obtaining an AUROC of 0.98. Such results were obtained using Deep Neural Networks and the XGBoost algorithm | ||
| 520 | |a CONCLUSIONS: The obtained results suggest, that Raman shifts at 1302 and 1306 cm-1 could be spectroscopic markers of gastric cancer | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Biomarkers | |
| 650 | 4 | |a Gastric cancer | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Raman spectroscopy | |
| 650 | 4 | |a Tumor markers | |
| 650 | 7 | |a Biomarkers, Tumor |2 NLM | |
| 700 | 1 | |a Jakubczyk, Paweł |e verfasserin |4 aut | |
| 700 | 1 | |a Paja, Wiesław |e verfasserin |4 aut | |
| 700 | 1 | |a Pancerz, Krzysztof |e verfasserin |4 aut | |
| 700 | 1 | |a Wosiak, Agnieszka |e verfasserin |4 aut | |
| 700 | 1 | |a Yaylım, İlhan |e verfasserin |4 aut | |
| 700 | 1 | |a İnal Gültekin, Güldal |e verfasserin |4 aut | |
| 700 | 1 | |a Tarhan, Nevzat |e verfasserin |4 aut | |
| 700 | 1 | |a Hakan, Mehmet Tolgahan |e verfasserin |4 aut | |
| 700 | 1 | |a Sönmez, Dilara |e verfasserin |4 aut | |
| 700 | 1 | |a Sarıbal, Devrim |e verfasserin |4 aut | |
| 700 | 1 | |a Arıkan, Soykan |e verfasserin |4 aut | |
| 700 | 1 | |a Depciuch, Joanna |e verfasserin |4 aut | |
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