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Kalatzis D, Spyratou E, Karnachoriti M, Kouri MA, Stathopoulos I, Danias N, Arkadopoulos N, Orfanoudakis S, Seimenis I, Kontos AG, Efstathopoulos EP. Extended Analysis of Raman Spectra Using Artificial Intelligence Techniques for Colorectal Abnormality Classification. J Imaging 2023; 9:261. [PMID: 38132679 PMCID: PMC10744297 DOI: 10.3390/jimaging9120261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 11/06/2023] [Accepted: 11/22/2023] [Indexed: 12/23/2023] Open
Abstract
Raman spectroscopy (RS) techniques are attracting attention in the medical field as a promising tool for real-time biochemical analyses. The integration of artificial intelligence (AI) algorithms with RS has greatly enhanced its ability to accurately classify spectral data in vivo. This combination has opened up new possibilities for precise and efficient analysis in medical applications. In this study, healthy and cancerous specimens from 22 patients who underwent open colorectal surgery were collected. By using these spectral data, we investigate an optimal preprocessing pipeline for statistical analysis using AI techniques. This exploration entails proposing preprocessing methods and algorithms to enhance classification outcomes. The research encompasses a thorough ablation study comparing machine learning and deep learning algorithms toward the advancement of the clinical applicability of RS. The results indicate substantial accuracy improvements using techniques like baseline correction, L2 normalization, filtering, and PCA, yielding an overall accuracy enhancement of 15.8%. In comparing various algorithms, machine learning models, such as XGBoost and Random Forest, demonstrate effectiveness in classifying both normal and abnormal tissues. Similarly, deep learning models, such as 1D-Resnet and particularly the 1D-CNN model, exhibit superior performance in classifying abnormal cases. This research contributes valuable insights into the integration of AI in medical diagnostics and expands the potential of RS methods for achieving accurate malignancy classification.
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Affiliation(s)
- Dimitris Kalatzis
- 2nd Department of Radiology, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece; (D.K.); (E.S.); (M.A.K.); (I.S.)
| | - Ellas Spyratou
- 2nd Department of Radiology, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece; (D.K.); (E.S.); (M.A.K.); (I.S.)
- Department of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece;
| | - Maria Karnachoriti
- Department of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece;
- School of Applied Mathematical and Physical Sciences, National Technical University Athens, 15780 Athens, Greece;
| | - Maria Anthi Kouri
- 2nd Department of Radiology, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece; (D.K.); (E.S.); (M.A.K.); (I.S.)
- Medical Physics Program, University of Massachusetts Lowell, Lowell, MA 01854, USA
| | - Ioannis Stathopoulos
- 2nd Department of Radiology, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece; (D.K.); (E.S.); (M.A.K.); (I.S.)
| | - Nikolaos Danias
- 4th Department of Surgery, School of Medicine, Attikon University Hospital, University of Athens, 12462 Athens, Greece; (N.D.); (N.A.)
| | - Nikolaos Arkadopoulos
- 4th Department of Surgery, School of Medicine, Attikon University Hospital, University of Athens, 12462 Athens, Greece; (N.D.); (N.A.)
| | - Spyros Orfanoudakis
- Alpha Information Technology S.A., Software & System Development, 68131 Alexandroupolis, Greece;
| | - Ioannis Seimenis
- Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece;
| | - Athanassios G. Kontos
- School of Applied Mathematical and Physical Sciences, National Technical University Athens, 15780 Athens, Greece;
| | - Efstathios P. Efstathopoulos
- 2nd Department of Radiology, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece; (D.K.); (E.S.); (M.A.K.); (I.S.)
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Kouri MA, Karnachoriti M, Spyratou E, Orfanoudakis S, Kalatzis D, Kontos AG, Seimenis I, Efstathopoulos EP, Tsaroucha A, Lambropoulou M. Shedding Light on Colorectal Cancer: An In Vivo Raman Spectroscopy Approach Combined with Deep Learning Analysis. Int J Mol Sci 2023; 24:16582. [PMID: 38068905 PMCID: PMC10706261 DOI: 10.3390/ijms242316582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 11/07/2023] [Accepted: 11/13/2023] [Indexed: 12/18/2023] Open
Abstract
Raman spectroscopy has emerged as a powerful tool in medical, biochemical, and biological research with high specificity, sensitivity, and spatial and temporal resolution. Recent advanced Raman systems, such as portable Raman systems and fiber-optic probes, provide the potential for accurate in vivo discrimination between healthy and cancerous tissues. In our study, a portable Raman probe spectrometer was tested in immunosuppressed mice for the in vivo localization of colorectal cancer malignancies from normal tissue margins. The acquired Raman spectra were preprocessed, and principal component analysis (PCA) was performed to facilitate discrimination between malignant and normal tissues and to highlight their biochemical differences using loading plots. A transfer learning model based on a one-dimensional convolutional neural network (1D-CNN) was employed for the Raman spectra data to assess the classification accuracy of Raman spectra in live animals. The 1D-CNN model yielded an 89.9% accuracy and 91.4% precision in tissue classification. Our results contribute to the field of Raman spectroscopy in cancer diagnosis, highlighting its promising role within clinical applications.
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Affiliation(s)
- Maria Anthi Kouri
- 2nd Department of Radiology, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece; (M.A.K.); (E.S.); (D.K.); (E.P.E.)
- Medical Physics Program, Department of Physics and Applied Physics, Kennedy College of Sciences, University of Massachusetts Lowell, 265 Riverside St., Lowell, MA 01854, USA
| | - Maria Karnachoriti
- Physics Department, School of Applied Mathematical and Physical Sciences, National Technical University of Athens, Iroon Politechniou 9, 15780 Athens, Greece; (M.K.); (S.O.)
| | - Ellas Spyratou
- 2nd Department of Radiology, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece; (M.A.K.); (E.S.); (D.K.); (E.P.E.)
| | - Spyros Orfanoudakis
- Physics Department, School of Applied Mathematical and Physical Sciences, National Technical University of Athens, Iroon Politechniou 9, 15780 Athens, Greece; (M.K.); (S.O.)
| | - Dimitris Kalatzis
- 2nd Department of Radiology, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece; (M.A.K.); (E.S.); (D.K.); (E.P.E.)
| | - Athanassios G. Kontos
- Physics Department, School of Applied Mathematical and Physical Sciences, National Technical University of Athens, Iroon Politechniou 9, 15780 Athens, Greece; (M.K.); (S.O.)
| | - Ioannis Seimenis
- Medical School, National and Kapodistrian University of Athens, 75 Mikras Assias Str., 11527 Athens, Greece;
| | - Efstathios P. Efstathopoulos
- 2nd Department of Radiology, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece; (M.A.K.); (E.S.); (D.K.); (E.P.E.)
| | - Alexandra Tsaroucha
- Laboratory of Bioethics, School of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece
| | - Maria Lambropoulou
- Laboratory of Histology-Embryology, School of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece;
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Karnachoriti M, Stathopoulos I, Kouri M, Spyratou E, Orfanoudakis S, Lykidis D, Lambropoulou Μ, Danias N, Arkadopoulos N, Efstathopoulos EP, Raptis YS, Seimenis I, Kontos AG. Biochemical differentiation between cancerous and normal human colorectal tissues by micro-Raman spectroscopy. Spectrochim Acta A Mol Biomol Spectrosc 2023; 299:122852. [PMID: 37216817 DOI: 10.1016/j.saa.2023.122852] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 04/29/2023] [Accepted: 05/08/2023] [Indexed: 05/24/2023]
Abstract
Human colorectal tissues obtained by ten cancer patients have been examined by multiple micro-Raman spectroscopic measurements in the 500-3200 cm-1 range under 785 nm excitation. Distinct spectral profiles are recorded from different spots on the samples: a predominant 'typical' profile of colorectal tissue, as well as those from tissue topologies with high lipid, blood or collagen content. Principal component analysis identified several Raman bands of amino acids, proteins and lipids which allow the efficient discrimination of normal from cancer tissues, the first presenting plurality of Raman spectral profiles while the last showing off quite uniform spectroscopic characteristics. Tree-based machine learning experiment was further applied on all data as well as on filtered data keeping only those spectra which characterize the largely inseparable data clusters of 'typical' and 'collagen-rich' spectra. This purposive sampling evidences statistically the most significant spectroscopic features regarding the correct identification of cancer tissues and allows matching spectroscopic results with the biochemical changes induced in the malignant tissues.
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Affiliation(s)
- M Karnachoriti
- School of Applied Mathematical and Physical Sciences, National Technical University Athens, 15780 Zografou, Athens, Greece; Department of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece
| | - I Stathopoulos
- 2(nd) Department of Radiology, Medical School, National & Kapodistrian University of Athens, 15772 Athens, Greece
| | - M Kouri
- Department of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece; 2(nd) Department of Radiology, Medical School, National & Kapodistrian University of Athens, 15772 Athens, Greece; Medical Physics Program, University of Massachusetts Lowell, MA 01854, United States
| | - E Spyratou
- Department of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece; 2(nd) Department of Radiology, Medical School, National & Kapodistrian University of Athens, 15772 Athens, Greece
| | - S Orfanoudakis
- School of Applied Mathematical and Physical Sciences, National Technical University Athens, 15780 Zografou, Athens, Greece; Alpha Information Technology S.A., Software & System Development, 68131 Alexandroupolis, Greece
| | - D Lykidis
- Laboratory of Histology-Embryology, Medical Department, Democritus University of Thrace, Alexandroupolis, Greece
| | - Μ Lambropoulou
- Laboratory of Histology-Embryology, Medical Department, Democritus University of Thrace, Alexandroupolis, Greece
| | - N Danias
- 4(th) Department of Surgery, School of Medicine, Attikon University Hospital, Univ. of Athens, 12462 Athens, Greece
| | - N Arkadopoulos
- 4(th) Department of Surgery, School of Medicine, Attikon University Hospital, Univ. of Athens, 12462 Athens, Greece
| | - E P Efstathopoulos
- 2(nd) Department of Radiology, Medical School, National & Kapodistrian University of Athens, 15772 Athens, Greece
| | - Y S Raptis
- School of Applied Mathematical and Physical Sciences, National Technical University Athens, 15780 Zografou, Athens, Greece
| | - I Seimenis
- Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece
| | - A G Kontos
- School of Applied Mathematical and Physical Sciences, National Technical University Athens, 15780 Zografou, Athens, Greece.
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Gkini K, Orfanoudakis S, Tsipas P, Skoulikidou MC, Dimoulas A, Falaras P, Konstantakou M, Stergiopoulos T. ZrCl4 for energy level alignment at the perovskite/TiO2 interface. Electrochim Acta 2022. [DOI: 10.1016/j.electacta.2022.141214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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