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Article

Raman Spectroscopy and Machine Learning for IDH Genotyping of Unprocessed Glioma Biopsies

1
Unit of Oncological Neurosurgery, Humanitas Clinical and Research Center—IRCCS, 20089 Rozzano, Italy
2
Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, 20122 Milano, Italy
3
The Hamlyn Centre for Robotic Surgery, Institute of Global Health Innovation, Imperial College London, Exhibition Road, London SW7 2AZ, UK
4
Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
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The BioRobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant’Anna, 56127 Pisa, Italy
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Department of Neuroradiology, IRCCS Ospedale San Raffaele, 20132 Milan, Italy
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Neuroradiology Unit, IRCCS San Raffaele and Vita-Salute San Raffaele University, 20132 Milan, Italy
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Unit of Pathology, Humanitas Clinical and Research Center—IRCCS, Via Manzoni 56, 20089 Rozzano, Italy
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Department of Medical Biotechnology and Translational Medicine, Università degli Studi di Milano, 20122 Milan, Italy
*
Author to whom correspondence should be addressed.
These authors contributed equally to the work.
Academic Editor: Antonio Randazzo
Cancers 2021, 13(16), 4196; https://doi.org/10.3390/cancers13164196
Received: 16 July 2021 / Revised: 12 August 2021 / Accepted: 16 August 2021 / Published: 20 August 2021
(This article belongs to the Special Issue Perioperative Imaging and Mapping Methods in Glioma Patients)
Isocitrate dehydrogenase (IDH) mutation is one of the most important prognostic markers in glioma tumors. Raman spectroscopy (RS) is an optical technique with great potential in intraoperative molecular diagnosis and surgical guidance. We analyzed RS’s ability to detect the IDH mutation onto unprocessed glioma biopsies. A total of 2073 Raman spectra were extracted from 38 tumor specimens. From the 103 Raman shifts screened, we identified 52 shifts (related to lipids, collagen, DNA and cholesterol/phospholipids) with the highest performance in the distinction of the two groups. We described 18 shifts never used before for IDH detection with RS in fresh or frozen samples. We were able to distinguish between IDH-mutated and IDH-wild-type tumors with an accuracy and precision of 87%. RS showed optimal accuracy and precision in discriminating IDH-mutated glioma from IDH-wild-type tumors ex-vivo onto fresh surgical specimens.
Isocitrate dehydrogenase (IDH) mutational status is pivotal in the management of gliomas. Patients with IDH-mutated (IDH-MUT) tumors have a better prognosis and benefit more from extended surgical resection than IDH wild-type (IDH-WT). Raman spectroscopy (RS) is a minimally invasive optical technique with great potential for intraoperative diagnosis. We evaluated the RS’s ability to characterize the IDH mutational status onto unprocessed glioma biopsies. We extracted 2073 Raman spectra from thirty-eight unprocessed samples. The classification performance was assessed using the eXtreme Gradient Boosted trees (XGB) and Support Vector Machine with Radial Basis Function kernel (RBF-SVM). Measured Raman spectra displayed differences between IDH-MUT and IDH-WT tumor tissue. From the 103 Raman shifts screened as input features, the cross-validation loop identified 52 shifts with the highest performance in the distinction of the two groups. Raman analysis showed differences in spectral features of lipids, collagen, DNA and cholesterol/phospholipids. We were able to distinguish between IDH-MUT and IDH-WT tumors with an accuracy and precision of 87%. RS is a valuable and accurate tool for characterizing the mutational status of IDH mutation in unprocessed glioma samples. This study improves RS knowledge for future personalized surgical strategy or in situ target therapies for glioma tumors. View Full-Text
Keywords: raman spectroscopy; neuro-oncology; classification; glioma; machine learning; isocitrate dehydrogenase (IDH) raman spectroscopy; neuro-oncology; classification; glioma; machine learning; isocitrate dehydrogenase (IDH)
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MDPI and ACS Style

Sciortino, T.; Secoli, R.; d’Amico, E.; Moccia, S.; Conti Nibali, M.; Gay, L.; Rossi, M.; Pecco, N.; Castellano, A.; De Momi, E.; Fernandes, B.; Riva, M.; Bello, L. Raman Spectroscopy and Machine Learning for IDH Genotyping of Unprocessed Glioma Biopsies. Cancers 2021, 13, 4196. https://doi.org/10.3390/cancers13164196

AMA Style

Sciortino T, Secoli R, d’Amico E, Moccia S, Conti Nibali M, Gay L, Rossi M, Pecco N, Castellano A, De Momi E, Fernandes B, Riva M, Bello L. Raman Spectroscopy and Machine Learning for IDH Genotyping of Unprocessed Glioma Biopsies. Cancers. 2021; 13(16):4196. https://doi.org/10.3390/cancers13164196

Chicago/Turabian Style

Sciortino, Tommaso, Riccardo Secoli, Ester d’Amico, Sara Moccia, Marco Conti Nibali, Lorenzo Gay, Marco Rossi, Nicolò Pecco, Antonella Castellano, Elena De Momi, Bethania Fernandes, Marco Riva, and Lorenzo Bello. 2021. "Raman Spectroscopy and Machine Learning for IDH Genotyping of Unprocessed Glioma Biopsies" Cancers 13, no. 16: 4196. https://doi.org/10.3390/cancers13164196

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