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Sun J, Cheng W, Guo S, Cai R, Liu G, Wu A, Yin J. A ratiometric SERS strategy for the prediction of cancer cell proportion and guidance of glioma surgical resection. Biosens Bioelectron 2024; 261:116475. [PMID: 38852324 DOI: 10.1016/j.bios.2024.116475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 06/01/2024] [Accepted: 06/05/2024] [Indexed: 06/11/2024]
Abstract
Rapid and accurate identification of tumor boundaries is critical for the cure of glioma, but it is difficult due to the invasive nature of glioma cells. This paper aimed to explore a rapid diagnostic strategy based on a label-free surface-enhanced Raman scattering (SERS) technique for the quantitative detection of glioma cell proportion intraoperatively. With silver nanoparticles as substrate, an in-depth SERS analysis was performed on simulated clinical samples containing normal brain tissue and different concentrations of patient-derived glioma cells. The results revealed two universal characteristic peaks of 655 and 717 cm-1, which strongly correlated with glioma cell proportion regardless of individual differences. Based on the intensity ratio of the two peaks, a ratiometric SERS strategy for the quantification of glioma cells was established by employing an artificial neuron network model and a polynomial regression model. Such a strategy accurately estimated the proportion of glioma cells in simulated clinical samples (R2 = 0.98) and frozen samples (R2 = 0.85). More importantly, it accurately facilitated the delineation of tumor margins in freshly obtained samples. Taken together, this SERS-based method ensured a rapid and more detailed identification of tumor margins during surgical resection, which could be beneficial for intraoperative decision-making and pathological evaluation.
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Affiliation(s)
- Jiaojiao Sun
- CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, 215163, PR China
| | - Wen Cheng
- Department of Neurosurgery, Shengjing Hospital of China Medical University, Shenyang, Liaoning, 110004, PR China
| | - Songyi Guo
- Department of Neurosurgery, Shengjing Hospital of China Medical University, Shenyang, Liaoning, 110004, PR China
| | - Ruikai Cai
- Department of Neurosurgery, Shengjing Hospital of China Medical University, Shenyang, Liaoning, 110004, PR China
| | - Guangxing Liu
- CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, 215163, PR China.
| | - Anhua Wu
- Department of Neurosurgery, Shengjing Hospital of China Medical University, Shenyang, Liaoning, 110004, PR China.
| | - Jian Yin
- CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, 215163, PR China.
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Tołpa B, Paja W, Trojnar E, Łach K, Gala-Błądzińska A, Kowal A, Gumbarewicz E, Frączek P, Cebulski J, Depciuch J. FT-Raman spectra in combination with machine learning and multivariate analyses as a diagnostic tool in brain tumors. NANOMEDICINE : NANOTECHNOLOGY, BIOLOGY, AND MEDICINE 2024; 57:102737. [PMID: 38341010 DOI: 10.1016/j.nano.2024.102737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 12/28/2023] [Accepted: 01/31/2024] [Indexed: 02/12/2024]
Abstract
Brain tumors are one of the most dangerous, because the position of these are in the organ that governs all life processes. Moreover, a lot of brain tumor types were observed, but only one main diagnostic method was used - histopathology, for which preparation of sample was long. Consequently, a new, quicker diagnostic method is needed. In this paper, FT-Raman spectra of brain tissues were analyzed by Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), four different machine learning (ML) algorithms to show possibility of differentiating between glioblastoma G4 and meningiomas, as well as two different types of meningiomas (atypical and angiomatous). Obtained results showed that in meningiomas additional peak around 1503 cm-1 and higher level of amides was noticed in comparison with glioblastoma G4. In the case of meningiomas differentiation, in angiomatous meningiomas tissues lower level of lipids and polysaccharides were visible than in atypical meningiomas. Moreover, PCA analyses showed higher distinction between glioblastoma G4 and meningiomas in the FT-Raman range between 800 cm-1 and 1800 cm-1 and between two types of meningiomas in the range between 2700 cm-1 and 3000 cm-1. Decision trees showed, that the most important peaks to differentiate glioblastoma and meningiomas were at 1151 cm-1 and 2836 cm-1 while for angiomatous and atypical meningiomas - 1514 cm-1 and 2875 cm-1. Furthermore, the accuracy of obtained results for glioblastoma G4 and meningiomas was 88 %, while for meningiomas - 92 %. Consequently, obtained data showed possibility of using FT-Raman spectroscopy in diagnosis of different types of brain tumors.
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Affiliation(s)
- Bartłomiej Tołpa
- Department of Neurosurgery, Clinical Hospital No 2 in Rzeszów, Lwowska 60, 35-309 Rzeszów, Poland
| | - Wiesław Paja
- Institute of Computer Science, College of Natural Sciences, University of Rzeszów, Poland
| | - Elżbieta Trojnar
- Clinical Department of Pathomorphology, Clinical Hospital No 2, Rzeszów, Poland
| | - Kornelia Łach
- Department of Pediatrics, Institute of Medical Sciences, University of Rzeszów, 35-310 Rzeszów, Poland
| | | | - Aneta Kowal
- Doctoral School, Institute of Medical Sciences, University of Rzeszów, 35-310 Rzeszów, Poland
| | - Ewelina Gumbarewicz
- Department of Biochemistry and Molecular Biology, Medical University of Lublin, 20-093 Lublin, Poland
| | - Paulina Frączek
- Department of Human Immunology, Institute of Medical Sciences, Medical College of Rzeszów University, University of Rzeszów, Rzeszów, Poland
| | - Józef Cebulski
- Institute of Physics, College of Natural Sciences, University of Rzeszów, PL-35959 Rzeszów, Poland
| | - Joanna Depciuch
- Department of Biochemistry and Molecular Biology, Medical University of Lublin, 20-093 Lublin, Poland; Institute of Nuclear Physics, Polish Academy of Sciences, 31-342 Krakow, Poland.
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Li L, Yu M, Li X, Ma X, Zhu L, Zhang T. A deep learning method for multi-task intelligent detection of oral cancer based on optical fiber Raman spectroscopy. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2024; 16:1659-1673. [PMID: 38419435 DOI: 10.1039/d3ay02250a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/02/2024]
Abstract
In the fight against oral cancer, innovative methods like Raman spectroscopy and deep learning have become powerful tools, particularly in integral tasks encompassing tumor staging, lymph node staging, and histological grading. These aspects are essential for the development of effective treatment strategies and prognostic assessment. However, it is important to note that most research so far has focused on solutions to one of these problems and has not taken full advantage of the potential wealth of information in the data. To compensate for this shortfall, we conceived a method that combines Raman spectroscopy with deep learning for simultaneous processing of multiple classification tasks, including tumor staging, lymph node staging, and histological grading. To achieve this innovative approach, we collected 1750 Raman spectra from 70 tissue samples, including normal and cancerous tissue samples from 35 patients with oral cancer. In addition, we used a deep neural network architecture to design four distinct multi-task network (MTN) models for intelligent oral cancer diagnosis, named MTN-Alexnet, MTN-Googlenet, MTN-Resnet50, and MTN-Transformer. To determine their effectiveness, we compared these multitask models to each other and to single-task models and traditional machine learning methods. The preliminary experimental results show that our multi-task network model has good performance, among which MTN-Transformer performs best. Specifically, MTN-Transformer has an accuracy of 81.5%, a precision of 82.1%, a sensitivity of 80.2%, and an F1_score of 81.1% in terms of tumor staging. In the field of lymph node staging, the accuracy, precision, sensitivity, and F1_score of MTN-Transformer are 81.3%, 83.0%, 80.1%, and 81.5% respectively. Similarly, for the histological grading classification tasks, the accuracy was 83.0%, the precision 84.3%, the sensitivity 76.7%, and the F1_score 80.2%. This code is available at https://github.com/ISCLab-Bistu/MultiTask-OralRamanSystem.
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Affiliation(s)
- Lianyu Li
- Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing 100192, China.
| | - Mingxin Yu
- Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing 100192, China.
- Beijing Laboratory of Biomedical Detection Technology and Instrument, Tsinghua University, Beijing 100084, China
- Guangzhou Nansha Intelligent Photonic Sensing Research Institute, Guang Zhou, Guang Dong 511462, China
| | - Xing Li
- Department of Stomatology, Peking Union Medical College Hospital, No. 1 Shuaifuyuan Wangfujing, Dongcheng District, Beijing 100730, China.
| | - Xinsong Ma
- Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing 100192, China.
| | - Lianqing Zhu
- Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing 100192, China.
| | - Tao Zhang
- Department of Stomatology, Peking Union Medical College Hospital, No. 1 Shuaifuyuan Wangfujing, Dongcheng District, Beijing 100730, China.
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Ranc V, Pavlacka O, Kalita O, Vaverka M. Discrimination of resected glioma tissues using surface enhanced Raman spectroscopy and Au@ZrO 2 plasmonic nanosensor. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 305:123521. [PMID: 37862838 DOI: 10.1016/j.saa.2023.123521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 10/06/2023] [Accepted: 10/11/2023] [Indexed: 10/22/2023]
Abstract
Gliomas present one of the most prevalent malignant tumors related to the central nervous system. Surgical extraction is still a preferred route for glioma treatment. Nonetheless, neurosurgeons still have a considerable challenge to detect actual margins of the targeted glioma intraoperatively and correctly because of its great natural infiltration. Here we evaluated the possibility of using surface-enhanced Raman spectroscopy to analyze freshly resected brain tissues. The developed method is based on the application of Au@ZrO2 nanosensor. The plasmonic properties of the sensor were first tested on the analysis of Rhodamine 6G, where concentrations down to 10-7 mol/L can be successfully detected. We also compared the performance of the nanosensor with silver plasmonic nanoparticles, where similar results were obtained regarding the reduction of the fluorescence background and enhancement of the intensity of the measured analytical signal. However, application of silver nanospheres led to increased variations in spectral data due to its probable aggregation. Applied ZrO2@Au nanosensor thus dramatically lowers the fluorescence present in the Raman data, and considerably improves the quality of the measured signal. The developed method allows for rapid discrimination between the glioma's periphery and central parts, which could serve as a steppingstone toward highly precise neurosurgery.
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Affiliation(s)
- Vaclav Ranc
- Institute of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, Palacký University and Faculty Hospital Olomouc, Hněvotínská 5, 775 15, Olomouc, Czech Republic.
| | - Ondrej Pavlacka
- Department of Mathematical Analysis and Applications of Mathematics, Faculty of Science, Palacký, University Olomouc, 17. Listopadu 12, Olomouc, Czech Republic
| | - Ondrej Kalita
- Department of Neurosurgery, Faculty Hospital Olomouc, I.P. Pavlova 6, 775 20, Olomouc, Czechia; Department of Health Care Science, Faculty of Humanities, T. Bata University in Zlín, Štefanikova 5670, 760 01 Zlín, Czechia
| | - Miroslav Vaverka
- Department of Neurosurgery, Faculty Hospital Olomouc, I.P. Pavlova 6, 775 20, Olomouc, Czechia
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Galli R, Lehner F, Richter S, Kirsche K, Meinhardt M, Juratli TA, Temme A, Kirsch M, Warta R, Herold-Mende C, Ricklefs FL, Lamszus K, Sievers P, Sahm F, Eyüpoglu IY, Uckermann O. Prediction of WHO grade and methylation class of aggressive meningiomas: Extraction of diagnostic information from infrared spectroscopic data. Neurooncol Adv 2024; 6:vdae082. [PMID: 39006162 PMCID: PMC11245706 DOI: 10.1093/noajnl/vdae082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/16/2024] Open
Abstract
Background Infrared (IR) spectroscopy allows intraoperative, optical brain tumor diagnosis. Here, we explored it as a translational technology for the identification of aggressive meningioma types according to both, the WHO CNS grading system and the methylation classes (MC). Methods Frozen sections of 47 meningioma were examined by IR spectroscopic imaging and different classification approaches were compared to discern samples according to WHO grade or MC. Results IR spectroscopic differences were more pronounced between WHO grade 2 and 3 than between MC intermediate and MC malignant, although similar spectral ranges were affected. Aggressive types of meningioma exhibited reduced bands of carbohydrates (at 1024 cm-1) and nucleic acids (at 1080 cm-1), along with increased bands of phospholipids (at 1240 and 1450 cm-1). While linear discriminant analysis was able to discern spectra of WHO grade 2 and 3 meningiomas (AUC 0.89), it failed for MC (AUC 0.66). However, neural network classifiers were effective for classification according to both WHO grade (AUC 0.91) and MC (AUC 0.83), resulting in the correct classification of 20/23 meningiomas of the test set. Conclusions IR spectroscopy proved capable of extracting information about the malignancy of meningiomas, not only according to the WHO grade, but also for a diagnostic system based on molecular tumor characteristics. In future clinical use, physicians could assess the goodness of the classification by considering classification probabilities and cross-measurement validation. This might enhance the overall accuracy and clinical utility, reinforcing the potential of IR spectroscopy in advancing precision medicine for meningioma characterization.
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Affiliation(s)
- Roberta Galli
- Faculty of Medicine, Medical Physics and Biomedical Engineering, Technische Universität Dresden, Dresden, Germany
| | - Franz Lehner
- Department of Neurosurgery, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Sven Richter
- Department of Neurosurgery, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- Else Kroener Fresenius Center for Digital Health, Technische Universität Dresden, Dresden, Germany
| | - Katrin Kirsche
- Department of Neurosurgery, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Matthias Meinhardt
- Faculty of Medicine, Department of Pathology, Technische Universität Dresden, Dresden, Germany
| | - Tareq A Juratli
- Department of Neurosurgery, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | | | - Matthias Kirsch
- Department of Neurosurgery, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Rolf Warta
- Division of Experimental Neurosurgery, Department of Neurosurgery, Heidelberg University, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany
| | - Christel Herold-Mende
- Division of Experimental Neurosurgery, Department of Neurosurgery, Heidelberg University, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany
| | - Franz L Ricklefs
- Laboratory for Brain Tumor Research, Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Katrin Lamszus
- Laboratory for Brain Tumor Research, Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Philipp Sievers
- Department of Neuropathology, University Hospital Heidelberg, Heidelberg, Germany
- CCU Neuropathology, German Consortium for Translational Cancer Research (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Felix Sahm
- Department of Neuropathology, University Hospital Heidelberg, Heidelberg, Germany
- CCU Neuropathology, German Consortium for Translational Cancer Research (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Ilker Y Eyüpoglu
- Department of Neurosurgery, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Ortrud Uckermann
- Department of Neurosurgery, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- Else Kroener Fresenius Center for Digital Health, Technische Universität Dresden, Dresden, Germany
- Department of Psychiatry and Psychotherapy, Division of Medical Biology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
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Wurm LM, Fischer B, Neuschmelting V, Reinecke D, Fischer I, Croner RS, Goldbrunner R, Hacker MC, Dybaś J, Kahlert UD. Rapid, label-free classification of glioblastoma differentiation status combining confocal Raman spectroscopy and machine learning. Analyst 2023; 148:6109-6119. [PMID: 37927114 DOI: 10.1039/d3an01303k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2023]
Abstract
Label-free identification of tumor cells using spectroscopic assays has emerged as a technological innovation with a proven ability for rapid implementation in clinical care. Machine learning facilitates the optimization of processing and interpretation of extensive data, such as various spectroscopy data obtained from surgical samples. The here-described preclinical work investigates the potential of machine learning algorithms combining confocal Raman spectroscopy to distinguish non-differentiated glioblastoma cells and their respective isogenic differentiated phenotype by means of confocal ultra-rapid measurements. For this purpose, we measured and correlated modalities of 1146 intracellular single-point measurements and sustainingly clustered cell components to predict tumor stem cell existence. By further narrowing a few selected peaks, we found indicative evidence that using our computational imaging technology is a powerful approach to detect tumor stem cells in vitro with an accuracy of 91.7% in distinct cell compartments, mainly because of greater lipid content and putative different protein structures. We also demonstrate that the presented technology can overcome intra- and intertumoral cellular heterogeneity of our disease models, verifying the elevated physiological relevance of our applied disease modeling technology despite intracellular noise limitations for future translational evaluation.
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Affiliation(s)
- Lennard M Wurm
- Department of Neurosurgery, University Hospital Düsseldorf and Medical Faculty Heinrich-Heine University, Düsseldorf, Germany
- Department of Neurosurgery, University Hospital Cologne, Cologne, Germany
| | - Björn Fischer
- Institute of Pharmaceutics and Biopharmaceutics, University of Düsseldorf, Düsseldorf, Germany
- FISCHER GmbH, Raman Spectroscopic Services, 40667 Meerbusch, Germany
| | | | - David Reinecke
- Department of Neurosurgery, University Hospital Cologne, Cologne, Germany
| | - Igor Fischer
- Department of Neurosurgery, University Hospital Düsseldorf and Medical Faculty Heinrich-Heine University, Düsseldorf, Germany
| | - Roland S Croner
- Clinic of General- Visceral-, Vascular and Transplantation Surgery, Department of Molecular and Experimental Surgery, University Hospital Magdeburg and Medical Faculty Otto-von-Guericke University, Magdeburg, Germany.
| | - Roland Goldbrunner
- Department of Neurosurgery, University Hospital Cologne, Cologne, Germany
| | - Michael C Hacker
- Institute of Pharmaceutics and Biopharmaceutics, University of Düsseldorf, Düsseldorf, Germany
| | - Jakub Dybaś
- Jagiellonian Center for Experimental Therapeutics, Jagiellonian University, Krakow, Poland
| | - Ulf D Kahlert
- Clinic of General- Visceral-, Vascular and Transplantation Surgery, Department of Molecular and Experimental Surgery, University Hospital Magdeburg and Medical Faculty Otto-von-Guericke University, Magdeburg, Germany.
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Bonosi L, Marrone S, Benigno UE, Buscemi F, Musso S, Porzio M, Silven MP, Torregrossa F, Grasso G. Maximal Safe Resection in Glioblastoma Surgery: A Systematic Review of Advanced Intraoperative Image-Guided Techniques. Brain Sci 2023; 13:brainsci13020216. [PMID: 36831759 PMCID: PMC9954589 DOI: 10.3390/brainsci13020216] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Revised: 01/15/2023] [Accepted: 01/24/2023] [Indexed: 01/31/2023] Open
Abstract
Glioblastoma multiforme (GBM) represents the most common and aggressive central nervous system tumor associated with a poor prognosis. The aim of this study was to depict the role of intraoperative imaging techniques in GBM surgery and how they can ensure the maximal extent of resection (EOR) while preserving the functional outcome. The authors conducted a systematic review following PRISMA guidelines on the PubMed/Medline and Scopus databases. A total of 1747 articles were identified for screening. Studies focusing on GBM-affected patients, and evaluations of EOR and functional outcomes with the aid of advanced image-guided techniques were included. The resulting studies were assessed for methodological quality using the Risk of Bias in Systematic Review tool. Open Science Framework registration DOI 10.17605/OSF.IO/3FDP9. Eighteen studies were eligible for this systematic review. Among the selected studies, eight analyzed Sodium Fluorescein, three analyzed 5-aminolevulinic acid, two evaluated IoMRI imaging, two evaluated IoUS, and three evaluated multiple intraoperative imaging techniques. A total of 1312 patients were assessed. Gross Total Resection was achieved in the 78.6% of the cases. Follow-up time ranged from 1 to 52 months. All studies assessed the functional outcome based on the Karnofsky Performance Status scale, while one used the Neurologic Assessment in Neuro-Oncology score. In 77.7% of the cases, the functional outcome improved or was stable over the pre-operative assessment. Combining multiple intraoperative imaging techniques could provide better results in GBM surgery than a single technique. However, despite good surgical outcomes, patients often present a neurocognitive decline leading to a marked deterioration of the quality of life. Advanced intraoperative image-guided techniques can allow a better understanding of the anatomo-functional relationships between the tumor and the surrounding brain, thus maximizing the EOR while preserving functional outcomes.
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Ranasinghe JC, Wang Z, Huang S. Raman Spectroscopy on Brain Disorders: Transition from Fundamental Research to Clinical Applications. BIOSENSORS 2022; 13:27. [PMID: 36671862 PMCID: PMC9855372 DOI: 10.3390/bios13010027] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 12/13/2022] [Accepted: 12/23/2022] [Indexed: 06/17/2023]
Abstract
Brain disorders such as brain tumors and neurodegenerative diseases (NDs) are accompanied by chemical alterations in the tissues. Early diagnosis of these diseases will provide key benefits for patients and opportunities for preventive treatments. To detect these sophisticated diseases, various imaging modalities have been developed such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). However, they provide inadequate molecule-specific information. In comparison, Raman spectroscopy (RS) is an analytical tool that provides rich information about molecular fingerprints. It is also inexpensive and rapid compared to CT, MRI, and PET. While intrinsic RS suffers from low yield, in recent years, through the adoption of Raman enhancement technologies and advanced data analysis approaches, RS has undergone significant advancements in its ability to probe biological tissues, including the brain. This review discusses recent clinical and biomedical applications of RS and related techniques applicable to brain tumors and NDs.
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Cutler CB, King P, Khan M, Olowofela B, Lucke-Wold B. Innovation in Neurosurgery: Lessons Learned, Obstacles, and Potential Funding Sources. NEURONS AND NEUROLOGICAL DISORDERS 2022; 1:003. [PMID: 36848305 PMCID: PMC9956204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Figures] [Subscribe] [Scholar Register] [Indexed: 03/01/2023]
Abstract
Innovation is central to neurosurgery and has dramatically increased over the last twenty years. Although the specialty innovates as a whole, only 3-4.7% of practicing neurosurgeons hold patents. Various roadblocks to innovation impede this process such as lack of understanding, increasing regulatory complexity, and lack of funding. Newly emerging technologies allow us to understand how to innovate and how to learn from other medical specialties. By further understanding the process of innovation, and the funding that supports it, Neurosurgery can continue to hold innovation as one of its's central tenets.
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Affiliation(s)
| | - Patrick King
- Rosalind Franklin University of Medicine and Science, Chicago, IL, USA
| | - Majid Khan
- University of Nevada, Reno School of Medicine, Reno, NV, USA
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10
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Jabarkheel R, Ho CS, Rodrigues AJ, Jin MC, Parker JJ, Mensah-Brown K, Yecies D, Grant GA. Rapid intraoperative diagnosis of pediatric brain tumors using Raman spectroscopy: A machine learning approach. Neurooncol Adv 2022; 4:vdac118. [PMID: 35919071 PMCID: PMC9341441 DOI: 10.1093/noajnl/vdac118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Background Surgical resection is a mainstay in the treatment of pediatric brain tumors to achieve tissue diagnosis and tumor debulking. While maximal safe resection of tumors is desired, it can be challenging to differentiate normal brain from neoplastic tissue using only microscopic visualization, intraoperative navigation, and tactile feedback. Here, we investigate the potential for Raman spectroscopy (RS) to accurately diagnose pediatric brain tumors intraoperatively. Methods Using a rapid acquisition RS device, we intraoperatively imaged fresh ex vivo brain tissue samples from 29 pediatric patients at the Lucile Packard Children’s Hospital between October 2018 and March 2020 in a prospective fashion. Small tissue samples measuring 2-4 mm per dimension were obtained with each individual tissue sample undergoing multiple unique Raman spectra acquisitions. All tissue samples from which Raman spectra were acquired underwent individual histopathology review. A labeled dataset of 678 unique Raman spectra gathered from 160 samples was then used to develop a machine learning model capable of (1) differentiating normal brain from tumor tissue and (2) normal brain from low-grade glioma (LGG) tissue. Results Trained logistic regression model classifiers were developed using our labeled dataset. Model performance was evaluated using leave-one-patient-out cross-validation. The area under the curve (AUC) of the receiver-operating characteristic (ROC) curve for our tumor vs normal brain model was 0.94. The AUC of the ROC curve for LGG vs normal brain was 0.91. Conclusions Our work suggests that RS can be used to develop a machine learning-based classifier to differentiate tumor vs non-tumor tissue during resection of pediatric brain tumors.
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Affiliation(s)
- Rashad Jabarkheel
- Department of Neurosurgery, Stanford University , Stanford, California , USA
- Department of Neurosurgery, University of Pennsylvania , Philadelphia, Pennsylvania , USA
| | - Chi-Sing Ho
- Department of Applied Physics, Stanford University , Stanford, California , USA
| | - Adrian J Rodrigues
- Department of Neurosurgery, Stanford University , Stanford, California , USA
| | - Michael C Jin
- Department of Neurosurgery, Stanford University , Stanford, California , USA
| | - Jonathon J Parker
- Department of Neurosurgery, Stanford University , Stanford, California , USA
| | - Kobina Mensah-Brown
- Department of Neurosurgery, University of Pennsylvania , Philadelphia, Pennsylvania , USA
| | - Derek Yecies
- Department of Neurosurgery, Stanford University , Stanford, California , USA
| | - Gerald A Grant
- Department of Neurosurgery, Stanford University , Stanford, California , USA
- Department of Neurosurgery, Duke University , Durham, North Carolina , USA
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Livermore LJ, Isabelle M, Bell IM, Edgar O, Voets NL, Stacey R, Ansorge O, Vallance C, Plaha P. Raman spectroscopy to differentiate between fresh tissue samples of glioma and normal brain: a comparison with 5-ALA-induced fluorescence-guided surgery. J Neurosurg 2021; 135:469-479. [PMID: 33007757 DOI: 10.3171/2020.5.jns20376] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Accepted: 05/22/2020] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Raman spectroscopy is a biophotonic tool that can be used to differentiate between different tissue types. It is nondestructive and no sample preparation is required. The aim of this study was to evaluate the ability of Raman spectroscopy to differentiate between glioma and normal brain when using fresh biopsy samples and, in the case of glioblastomas, to compare the performance of Raman spectroscopy to predict the presence or absence of tumor with that of 5-aminolevulinic acid (5-ALA)-induced fluorescence. METHODS A principal component analysis (PCA)-fed linear discriminant analysis (LDA) machine learning predictive model was built using Raman spectra, acquired ex vivo, from fresh tissue samples of 62 patients with glioma and 11 glioma-free brain samples from individuals undergoing temporal lobectomy for epilepsy. This model was then used to classify Raman spectra from fresh biopsies from resection cavities after functional guided, supramaximal glioma resection. In cases of glioblastoma, 5-ALA-induced fluorescence at the resection cavity biopsy site was recorded, and this was compared with the Raman spectral model prediction for the presence of tumor. RESULTS The PCA-LDA predictive model demonstrated 0.96 sensitivity, 0.99 specificity, and 0.99 accuracy for differentiating tumor from normal brain. Twenty-three resection cavity biopsies were taken from 8 patients after supramaximal resection (6 glioblastomas, 2 oligodendrogliomas). Raman spectroscopy showed 1.00 sensitivity, 1.00 specificity, and 1.00 accuracy for predicting tumor versus normal brain in these samples. In the glioblastoma cases, where 5-ALA-induced fluorescence was used, the performance of Raman spectroscopy was significantly better than the predictive value of 5-ALA-induced fluorescence, which showed 0.07 sensitivity, 1.00 specificity, and 0.24 accuracy (p = 0.0009). CONCLUSIONS Raman spectroscopy can accurately classify fresh tissue samples into tumor versus normal brain and is superior to 5-ALA-induced fluorescence. Raman spectroscopy could become an important intraoperative tool used in conjunction with 5-ALA-induced fluorescence to guide extent of resection in glioma surgery.
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Affiliation(s)
- Laurent J Livermore
- 1Nuffield Department of Clinical Neurosciences, and
- 3Department of Neurosurgery, Oxford University Hospitals NHS Foundation Trust, Oxford
| | - Martin Isabelle
- 4Renishaw plc, Spectroscopy Products Division, Gloucestershire
| | - Ian M Bell
- 4Renishaw plc, Spectroscopy Products Division, Gloucestershire
| | - Oliver Edgar
- 1Nuffield Department of Clinical Neurosciences, and
| | - Natalie L Voets
- 2Nuffield Department of Surgery, University of Oxford, John Radcliffe Hospital, Oxford
- 6FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom
| | - Richard Stacey
- 3Department of Neurosurgery, Oxford University Hospitals NHS Foundation Trust, Oxford
| | - Olaf Ansorge
- 1Nuffield Department of Clinical Neurosciences, and
| | | | - Puneet Plaha
- 2Nuffield Department of Surgery, University of Oxford, John Radcliffe Hospital, Oxford
- 3Department of Neurosurgery, Oxford University Hospitals NHS Foundation Trust, Oxford
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12
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Pekmezci M, Morshed RA, Chunduru P, Pandian B, Young J, Villanueva-Meyer JE, Tihan T, Sloan EA, Aghi MK, Molinaro AM, Berger MS, Hervey-Jumper SL. Detection of glioma infiltration at the tumor margin using quantitative stimulated Raman scattering histology. Sci Rep 2021; 11:12162. [PMID: 34108566 PMCID: PMC8190264 DOI: 10.1038/s41598-021-91648-8] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Accepted: 05/10/2021] [Indexed: 11/23/2022] Open
Abstract
In the management of diffuse gliomas, the identification and removal of tumor at the infiltrative margin remains a central challenge. Prior work has demonstrated that fluorescence labeling tools and radiographic imaging are useful surgical adjuvants with macroscopic resolution. However, they lose sensitivity at the tumor margin and have limited clinical utility for lower grade histologies. Fiber-laser based stimulated Raman histology (SRH) is an optical imaging technique that provides microscopic tissue characterization of unprocessed tissues. It remains unknown whether SRH of tissues taken from the infiltrative glioma margin will identify microscopic residual disease. Here we acquired glioma margin specimens for SRH, histology, and tumor specific tissue characterization. Generalized linear mixed models were used to evaluate agreement. We find that SRH identified residual tumor in 82 of 167 margin specimens (49%), compared to IHC confirming residual tumor in 72 of 128 samples (56%), and H&E confirming residual tumor in 82 of 169 samples (49%). Intraobserver agreements between all 3 modalities were confirmed. These data demonstrate that SRH detects residual microscopic tumor at the infiltrative glioma margin and may be a promising tool to enhance extent of resection.
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Affiliation(s)
- Melike Pekmezci
- Department of Pathology, University of California, San Francisco, CA, USA
| | - Ramin A Morshed
- Department of Neurological Surgery, University of California, 505 Parnassus Ave., Rm. M-779, San Francisco, CA, 94143-0112, USA
| | - Pranathi Chunduru
- Department of Neurological Surgery, University of California, 505 Parnassus Ave., Rm. M-779, San Francisco, CA, 94143-0112, USA.,Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | - Balaji Pandian
- Department of Neurological Surgery, University of California, 505 Parnassus Ave., Rm. M-779, San Francisco, CA, 94143-0112, USA.,Invenio Imaging, Inc, Santa Clara, CA, USA
| | - Jacob Young
- Department of Neurological Surgery, University of California, 505 Parnassus Ave., Rm. M-779, San Francisco, CA, 94143-0112, USA
| | | | - Tarik Tihan
- Department of Pathology, University of California, San Francisco, CA, USA
| | - Emily A Sloan
- Department of Pathology, University of California, San Francisco, CA, USA
| | - Manish K Aghi
- Department of Neurological Surgery, University of California, 505 Parnassus Ave., Rm. M-779, San Francisco, CA, 94143-0112, USA
| | - Annette M Molinaro
- Department of Neurological Surgery, University of California, 505 Parnassus Ave., Rm. M-779, San Francisco, CA, 94143-0112, USA.,Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | - Mitchel S Berger
- Department of Neurological Surgery, University of California, 505 Parnassus Ave., Rm. M-779, San Francisco, CA, 94143-0112, USA
| | - Shawn L Hervey-Jumper
- Department of Neurological Surgery, University of California, 505 Parnassus Ave., Rm. M-779, San Francisco, CA, 94143-0112, USA.
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13
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Cameron JM, Conn JJA, Rinaldi C, Sala A, Brennan PM, Jenkinson MD, Caldwell H, Cinque G, Syed K, Butler HJ, Hegarty MG, Palmer DS, Baker MJ. Interrogation of IDH1 Status in Gliomas by Fourier Transform Infrared Spectroscopy. Cancers (Basel) 2020; 12:E3682. [PMID: 33302429 PMCID: PMC7762605 DOI: 10.3390/cancers12123682] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 11/23/2020] [Accepted: 12/04/2020] [Indexed: 12/12/2022] Open
Abstract
Mutations in the isocitrate dehydrogenase 1 (IDH1) gene are found in a high proportion of diffuse gliomas. The presence of the IDH1 mutation is a valuable diagnostic, prognostic and predictive biomarker for the management of patients with glial tumours. Techniques involving vibrational spectroscopy, e.g., Fourier transform infrared (FTIR) spectroscopy, have previously demonstrated analytical capabilities for cancer detection, and have the potential to contribute to diagnostics. The implementation of FTIR microspectroscopy during surgical biopsy could present a fast, label-free method for molecular genetic classification. For example, the rapid determination of IDH1 status in a patient with a glioma diagnosis could inform intra-operative decision-making between alternative surgical strategies. In this study, we utilized synchrotron-based FTIR microanalysis to probe tissue microarray sections from 79 glioma patients, and distinguished the positive class (IDH1-mutated) from the IDH1-wildtype glioma, with a sensitivity and specificity of 82.4% and 83.4%, respectively. We also examined the ability of attenuated total reflection (ATR)-FTIR spectroscopy in detecting the biomolecular events and global epigenetic and metabolic changes associated with mutations in the IDH1 enzyme, in blood serum samples collected from an additional 72 brain tumour patients. Centrifugal filtration enhanced the diagnostic ability of the classification models, with balanced accuracies up to ~69%. Identification of the molecular status from blood serum prior to biopsy could further direct some patients to alternative treatment strategies.
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Affiliation(s)
- James M. Cameron
- WestCHEM, Department of Pure and Applied Chemistry, Technology and Innovation Centre, University of Strathclyde, 99 George St., Glasgow G1 1RD, UK; (J.M.C.); (C.R.); (A.S.)
- ClinSpec Diagnostics, Technology and Innovation Centre, University of Strathclyde, 99 George St., Glasgow G1 1RD, UK; (J.J.A.C.); (H.J.B.); (M.G.H.); (D.S.P.)
| | - Justin J. A. Conn
- ClinSpec Diagnostics, Technology and Innovation Centre, University of Strathclyde, 99 George St., Glasgow G1 1RD, UK; (J.J.A.C.); (H.J.B.); (M.G.H.); (D.S.P.)
| | - Christopher Rinaldi
- WestCHEM, Department of Pure and Applied Chemistry, Technology and Innovation Centre, University of Strathclyde, 99 George St., Glasgow G1 1RD, UK; (J.M.C.); (C.R.); (A.S.)
| | - Alexandra Sala
- WestCHEM, Department of Pure and Applied Chemistry, Technology and Innovation Centre, University of Strathclyde, 99 George St., Glasgow G1 1RD, UK; (J.M.C.); (C.R.); (A.S.)
| | - Paul M. Brennan
- Department of Clinical Neurosciences, Translational Neurosurgery, Western General Hospital, Edinburgh EH4 2XU, UK;
| | - Michael D. Jenkinson
- Institute of Systems, Molecular and Integrated Biology, University of Liverpool & The Walton Centre NHS Foundation Trust, Lower Lane, Fazakerley, Liverpool L9 7LJ, UK;
| | - Helen Caldwell
- Institute of Genetics and Molecular Medicine, University of Edinburgh, Division of Pathology, Western General Hospital, Crewe Road South, Edinburgh EH4 2XR, UK;
| | - Gianfelice Cinque
- Diamond Light Source, Harwell Science and Innovation Campus, Chilton, Oxfordshire OX11 0DE, UK;
| | - Khaja Syed
- Walton Research Tissue Bank, Neurosciences Laboratories, The Walton Centre NHS Foundation Trust, Lower Lane, Fazakerley, Liverpool L9 7LJ, UK;
| | - Holly J. Butler
- ClinSpec Diagnostics, Technology and Innovation Centre, University of Strathclyde, 99 George St., Glasgow G1 1RD, UK; (J.J.A.C.); (H.J.B.); (M.G.H.); (D.S.P.)
| | - Mark G. Hegarty
- ClinSpec Diagnostics, Technology and Innovation Centre, University of Strathclyde, 99 George St., Glasgow G1 1RD, UK; (J.J.A.C.); (H.J.B.); (M.G.H.); (D.S.P.)
| | - David S. Palmer
- ClinSpec Diagnostics, Technology and Innovation Centre, University of Strathclyde, 99 George St., Glasgow G1 1RD, UK; (J.J.A.C.); (H.J.B.); (M.G.H.); (D.S.P.)
- WestCHEM, Department of Pure and Applied Chemistry, Thomas Graham Building, University of Strathclyde, 295 Cathedral Str., Glasgow G1 1XL, UK
| | - Matthew J. Baker
- WestCHEM, Department of Pure and Applied Chemistry, Technology and Innovation Centre, University of Strathclyde, 99 George St., Glasgow G1 1RD, UK; (J.M.C.); (C.R.); (A.S.)
- ClinSpec Diagnostics, Technology and Innovation Centre, University of Strathclyde, 99 George St., Glasgow G1 1RD, UK; (J.J.A.C.); (H.J.B.); (M.G.H.); (D.S.P.)
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14
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Sekhar LN, Juric-Sekhar G, Qazi Z, Patel A, McGrath LB, Pridgeon J, Kalavakonda N, Hannaford B. The Future of Skull Base Surgery: A View Through Tinted Glasses. World Neurosurg 2020; 142:29-42. [PMID: 32599213 PMCID: PMC7319930 DOI: 10.1016/j.wneu.2020.06.172] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Revised: 06/19/2020] [Accepted: 06/21/2020] [Indexed: 01/06/2023]
Abstract
In the present report, we have broadly outlined the potential advances in the field of skull base surgery, which might occur within the next 20 years based on the many areas of current research in biology and technology. Many of these advances will also be broadly applicable to other areas of neurosurgery. We have grounded our predictions for future developments in an exploration of what patients and surgeons most desire as outcomes for care. We next examined the recent developments in the field and outlined several promising areas of future improvement in skull base surgery, per se, as well as identifying the new hospital support systems needed to accommodate these changes. These include, but are not limited to, advances in imaging, Raman spectroscopy and microscopy, 3-dimensional printing and rapid prototyping, master-slave and semiautonomous robots, artificial intelligence applications in all areas of medicine, telemedicine, and green technologies in hospitals. In addition, we have reviewed the therapeutic approaches using nanotechnology, genetic engineering, antitumor antibodies, and stem cell technologies to repair damage caused by traumatic injuries, tumors, and iatrogenic injuries to the brain and cranial nerves. Additionally, we have discussed the training requirements for future skull base surgeons and stressed the need for adaptability and change. However, the essential requirements for skull base surgeons will remain unchanged, including knowledge, attention to detail, technical skill, innovation, judgment, and compassion. We believe that active involvement in these rapidly evolving technologies will enable us to shape some of the future of our discipline to address the needs of both patients and our profession.
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Affiliation(s)
- Laligam N Sekhar
- Department of Neurosurgery, University of Washington, Seattle, Washington, USA.
| | | | - Zeeshan Qazi
- Department of Neurosurgery, University of Washington, Seattle, Washington, USA
| | - Anoop Patel
- Department of Neurosurgery, University of Washington, Seattle, Washington, USA
| | - Lynn B McGrath
- Department of Neurosurgery, University of Washington, Seattle, Washington, USA
| | - James Pridgeon
- Department of Neurosurgery, University of Washington, Seattle, Washington, USA
| | - Niveditha Kalavakonda
- Department of Electrical and Computer Engineering, University of Washington, Seattle, Washington, USA
| | - Blake Hannaford
- Department of Electrical and Computer Engineering, University of Washington, Seattle, Washington, USA
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15
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Payne TD, Moody AS, Wood AL, Pimiento PA, Elliott JC, Sharma B. Raman spectroscopy and neuroscience: from fundamental understanding to disease diagnostics and imaging. Analyst 2020; 145:3461-3480. [PMID: 32301450 DOI: 10.1039/d0an00083c] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Neuroscience would directly benefit from more effective detection techniques, leading to earlier diagnosis of disease. The specificity of Raman spectroscopy is unparalleled, given that a molecular fingerprint is attained for each species. It also allows for label-free detection with relatively inexpensive instrumentation, minimal sample preparation, and rapid sample analysis. This review summarizes Raman spectroscopy-based techniques that have been used to advance the field of neuroscience in recent years.
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Affiliation(s)
- Taylor D Payne
- University of Tennessee, Knoxville, 1420 Circle Drive, Knoxville, TN 37996, USA.
| | - Amber S Moody
- National Center of Toxicological Research, 3900 NCTR Rd, Jefferson, AR 72079, USA
| | - Avery L Wood
- University of Tennessee, Knoxville, 1420 Circle Drive, Knoxville, TN 37996, USA.
| | - Paula A Pimiento
- University of Tennessee, Knoxville, 1420 Circle Drive, Knoxville, TN 37996, USA.
| | - James C Elliott
- University of Tennessee, Knoxville, 1420 Circle Drive, Knoxville, TN 37996, USA.
| | - Bhavya Sharma
- University of Tennessee, Knoxville, 1420 Circle Drive, Knoxville, TN 37996, USA.
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16
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Galli R, Meinhardt M, Koch E, Schackert G, Steiner G, Kirsch M, Uckermann O. Rapid Label-Free Analysis of Brain Tumor Biopsies by Near Infrared Raman and Fluorescence Spectroscopy-A Study of 209 Patients. Front Oncol 2019; 9:1165. [PMID: 31750251 PMCID: PMC6848276 DOI: 10.3389/fonc.2019.01165] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Accepted: 10/17/2019] [Indexed: 01/09/2023] Open
Abstract
In brain surgery, novel technologies are continuously developed to achieve better tumor delineation and maximize the extent of resection. Raman spectroscopy is an optical method that enables to retrieve a molecular signature of tissue biochemical composition in order to identify tumor and normal tissue. Here, the translation of Raman spectroscopy to the surgical practice for discerning a variety of different tumor entities from non-neoplastic brain parenchyma was investigated. Fresh unprocessed biopsies obtained from brain tumor surgery were analyzed over 1.5 years including all patients that gave consent. Measurements were performed with a Raman microscope by medical personnel as routine activity. The Raman and fluorescence signals of the acquired spectra were analyzed by principal component analysis, followed by supervised classification to discriminate non-tumor tissue vs. tumor and distinguish tumor entities. Histopathology of the measured biopsies was performed as reference. Classification led to the correct recognition of all non-neoplastic biopsies (7/7) and of 97% of the investigated tumor biopsies (195/202). For instance, GBM was recognized as tumor with a correct rate of 94% if primary, and of 100% if recurrent. Astrocytoma and oligodendroglioma were recognized as tumor with correct rates of 86 and 90%, respectively. All brain metastases, meningioma and schwannoma were correctly recognized as tumor and distinguished from non-neoplastic brain tissue. Furthermore, metastases were discerned from glioma with correct rate of 90%. Oligodendroglioma and astrocytoma IDH1-mutant, which differ in the presence of 1p/19q codeletion, were discerned with a correct rate of 81%. These results demonstrate the feasibility of rapid brain tumors recognition and extraction of diagnostic information by Raman spectroscopy, using a protocol that can be easily included in the routine surgical workflow.
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Affiliation(s)
- Roberta Galli
- Clinical Sensoring and Monitoring, Anesthesiology and Intensive Care Medicine, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Matthias Meinhardt
- Neuropathology, Institute of Pathology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Edmund Koch
- Clinical Sensoring and Monitoring, Anesthesiology and Intensive Care Medicine, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Gabriele Schackert
- Neurosurgery, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Gerald Steiner
- Clinical Sensoring and Monitoring, Anesthesiology and Intensive Care Medicine, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Matthias Kirsch
- Neurosurgery, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Ortrud Uckermann
- Neurosurgery, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
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17
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Stummer W, Koch R, Valle RD, Roberts DW, Sanai N, Kalkanis S, Hadjipanayis CG, Suero Molina E. Intraoperative fluorescence diagnosis in the brain: a systematic review and suggestions for future standards on reporting diagnostic accuracy and clinical utility. Acta Neurochir (Wien) 2019; 161:2083-2098. [PMID: 31363920 PMCID: PMC6739423 DOI: 10.1007/s00701-019-04007-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2019] [Accepted: 07/05/2019] [Indexed: 12/24/2022]
Abstract
Background Surgery for gliomas is often confounded by difficulties in distinguishing tumor from surrounding normal brain. For better discrimination, intraoperative optical imaging methods using fluorescent dyes are currently being explored. Understandably, such methods require the demonstration of a high degree of diagnostic accuracy and clinical benefit. Currently, clinical utility is determined by tissue biopsies which are correlated to optical signals, and quantified using measures such as sensitivity, specificity, positive predictive values, and negative predictive values. In addition, surgical outcomes, such as extent of resection rates and/or survival (progression-free survival (PFS) and overall survival (OS)) have been measured. These assessments, however, potentially involve multiple biases and confounders, which have to be minimized to ensure reproducibility, generalizability and comparability of test results. Test should aim at having a high internal and external validity. The objective of this article is to analyze how diagnostic accuracy and outcomes are utilized in available studies describing intraoperative imaging and furthermore, to derive recommendations for reliable and reproducible evaluations. Methods A review of the literature was performed for assessing the use of measures of diagnostic accuracy and outcomes of intraoperative optical imaging methods. From these data, we derive recommendations for designing and reporting future studies. Results Available literature indicates that potential confounders and biases for reporting the diagnostic accuracy and usefulness of intraoperative optical imaging methods are seldom accounted for. Furthermore, methods for bias reduction are rarely used nor reported. Conclusions Detailed, transparent, and uniform reporting on diagnostic accuracy of intraoperative imaging methods is necessary. In the absence of such reporting, studies will not be comparable or reproducible. Future studies should consider some of the recommendations given here. Electronic supplementary material The online version of this article (10.1007/s00701-019-04007-y) contains supplementary material, which is available to authorized users.
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18
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Bury D, Morais CLM, Ashton KM, Dawson TP, Martin FL. Ex Vivo Raman Spectrochemical Analysis Using a Handheld Probe Demonstrates High Predictive Capability of Brain Tumour Status. BIOSENSORS 2019; 9:E49. [PMID: 30934999 PMCID: PMC6627213 DOI: 10.3390/bios9020049] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2019] [Revised: 03/25/2019] [Accepted: 03/29/2019] [Indexed: 12/18/2022]
Abstract
With brain tumour incidence increasing, there is an urgent need for better diagnostic tools. Intraoperatively, brain tumours are diagnosed using a smear preparation reported by a neuropathologist. These have many limitations, including the time taken for the specimen to reach the pathology department and for results to be communicated to the surgeon. There is also a need to assist with resection rates and identifying infiltrative tumour edges intraoperatively to improve clearance. We present a novel study using a handheld Raman probe in conjunction with gold nanoparticles, to detect primary and metastatic brain tumours from fresh brain tissue sent for intraoperative smear diagnosis. Fresh brain tissue samples sent for intraoperative smear diagnosis were tested using the handheld Raman probe after application of gold nanoparticles. Derived Raman spectra were inputted into forward feature extraction algorithms to build a predictive model for sensitivity and specificity of outcome. These results demonstrate an ability to detect primary from metastatic tumours (especially for normal and low grade lesions), in which accuracy, sensitivity and specificity were respectively equal to 98.6%, 94.4% and 99.5% for normal brain tissue; 96.1%, 92.2% and 97.0% for low grade glial tumours; 90.3%, 89.7% and 90.6% for high grade glial tumours; 94.8%, 63.9% and 97.1% for meningiomas; 95.4%, 79.2% and 98.8% for metastases; and 99.6%, 88.9% and 100% for lymphoma, based on smear samples (κ = 0.87). Similar results were observed when compared to the final formalin-fixed paraffin embedded tissue diagnosis (κ = 0.85). Overall, our results have demonstrated the ability of Raman spectroscopy to match results provided by intraoperative smear diagnosis and raise the possibility of use intraoperatively to aid surgeons by providing faster diagnosis. Moving this technology into theatre will allow it to develop further and thus reach its potential in the clinical arena.
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Affiliation(s)
- Danielle Bury
- School of Pharmacy and Biomedical Sciences, University of Central Lancashire, Preston PR1 2HE, UK.
| | - Camilo L M Morais
- School of Pharmacy and Biomedical Sciences, University of Central Lancashire, Preston PR1 2HE, UK.
| | - Katherine M Ashton
- Neuropathology, Royal Preston Hospital, Lancashire Teaching Hospitals NHS Trust, Sharoe Green Lane, Preston PR2 9HT, UK.
| | - Timothy P Dawson
- Neuropathology, Royal Preston Hospital, Lancashire Teaching Hospitals NHS Trust, Sharoe Green Lane, Preston PR2 9HT, UK.
| | - Francis L Martin
- School of Pharmacy and Biomedical Sciences, University of Central Lancashire, Preston PR1 2HE, UK.
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19
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Santos IP, Barroso EM, Bakker Schut TC, Caspers PJ, van Lanschot CGF, Choi DH, van der Kamp MF, Smits RWH, van Doorn R, Verdijk RM, Noordhoek Hegt V, von der Thüsen JH, van Deurzen CHM, Koppert LB, van Leenders GJLH, Ewing-Graham PC, van Doorn HC, Dirven CMF, Busstra MB, Hardillo J, Sewnaik A, Ten Hove I, Mast H, Monserez DA, Meeuwis C, Nijsten T, Wolvius EB, Baatenburg de Jong RJ, Puppels GJ, Koljenović S. Raman spectroscopy for cancer detection and cancer surgery guidance: translation to the clinics. Analyst 2018; 142:3025-3047. [PMID: 28726868 DOI: 10.1039/c7an00957g] [Citation(s) in RCA: 104] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Oncological applications of Raman spectroscopy have been contemplated, pursued, and developed at academic level for at least 25 years. Published studies aim to detect pre-malignant lesions, detect cancer in less invasive stages, reduce the number of unnecessary biopsies and guide surgery towards the complete removal of the tumour with adequate tumour resection margins. This review summarizes actual clinical needs in oncology that can be addressed by spontaneous Raman spectroscopy and it provides an overview over the results that have been published between 2007 and 2017. An analysis is made of the current status of translation of these results into clinical practice. Despite many promising results, most of the applications addressed in scientific studies are still far from clinical adoption and commercialization. The main hurdles are identified, which need to be overcome to ensure that in the near future we will see the first Raman spectroscopy-based solutions being used in routine oncologic diagnostic and surgical procedures.
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Affiliation(s)
- Inês P Santos
- Center for Optical Diagnostics and Therapy, Department of Dermatology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.
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20
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IDH1 mutation in human glioma induces chemical alterations that are amenable to optical Raman spectroscopy. J Neurooncol 2018; 139:261-268. [PMID: 29761368 DOI: 10.1007/s11060-018-2883-8] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2018] [Accepted: 04/23/2018] [Indexed: 12/12/2022]
Abstract
INTRODUCTION Mutations in the isocytrate dehydrogenase 1 (IDH1) gene are early genetic events in glioma pathogenesis and cause profound metabolic changes. Because this genotype is found in virtually every tumor cell, therapies targeting mutant IDH1 protein are being developed. The intraoperative administration of those therapies would require fast technologies for the determination of IDH1 genotype. As of today, there is no such diagnostic test available. Recently, infrared spectroscopy was shown to bridge this gap. Here, we tested Raman spectroscopy for analysis of IDH1 genotype in glioma, which constitutes an alternative contact-free technique with the potential of being applicable in situ. METHODS Human glioma samples (n = 36) were obtained during surgery and cryosections were prepared. IDH1 mutations were assessed using DNA sequencing and 100 Raman spectra were obtained for each sample. RESULTS Analysis of Raman spectra revealed increased intensities in spectral bands related to DNA in IDH1 mutant glioma while bands assigned to molecular vibrations of lipids were significantly decreased. Moreover, intensities of Raman bands assigned to proteins differed in IDH1 mutant and IDH1 wild-type glioma, suggesting alterations in the protein profile. The selection of five bands (498, 826, 1003, 1174 and 1337 cm-1) allowed the classification of Raman spectra according to IDH1 genotype with a correct rate of 89%. CONCLUSION Raman spectroscopy constitutes a simple, rapid and safe procedure for determination of the IDH1 mutation that shows great promise for clinically relevant in situ diagnostics.
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21
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Foster MT, Harishchandra LS, Mallucci C. Pediatric Central Nervous System Tumors: State-of-the-Art and Debated Aspects. Front Pediatr 2018; 6:309. [PMID: 30443540 PMCID: PMC6223202 DOI: 10.3389/fped.2018.00309] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/24/2018] [Accepted: 10/01/2018] [Indexed: 01/23/2023] Open
Abstract
Pediatric neuro-oncology surgery continues to progress in sophistication, largely driven by advances in technology used to aid the following aspects of surgery: operative planning (advanced MRI techniques including fMRI and DTI), intraoperative navigation [preoperative MRI, intra-operative MRI (ioMRI) and intra-operative ultrasound (ioUS)], tumor visualization (microscopy, endoscopy, fluorescence), tumor resection techniques (ultrasonic aspirator, micro-instruments, micro-endoscopic instruments), delineation of the resection extent (ioMRI, ioUS, and fluorescence), and intraoperative safety (neurophysiological monitoring, ioMRI). This article discusses the aforementioned technological advances, and their multimodal use to optimize safe pediatric neuro-oncology surgery.
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Affiliation(s)
- Mitchell T Foster
- Department of Neurosurgery, Alder Hey NHS Foundation Trust, Liverpool, United Kingdom
| | | | - Conor Mallucci
- Department of Neurosurgery, Alder Hey NHS Foundation Trust, Liverpool, United Kingdom
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Uckermann O, Juratli TA, Galli R, Conde M, Wiedemuth R, Krex D, Geiger K, Temme A, Schackert G, Koch E, Steiner G, Kirsch M. Optical Analysis of Glioma: Fourier-Transform Infrared Spectroscopy Reveals the IDH1 Mutation Status. Clin Cancer Res 2017; 24:2530-2538. [PMID: 29259030 DOI: 10.1158/1078-0432.ccr-17-1795] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2017] [Revised: 10/16/2017] [Accepted: 12/14/2017] [Indexed: 11/16/2022]
Abstract
Purpose: Somatic mutations in the human cytosolic isocitrate dehydrogenase 1 (IDH1) gene cause profound changes in cell metabolism and are a common feature of gliomas with unprecedented predictive and prognostic impact. Fourier-transform infrared (FT-IR) spectroscopy addresses the molecular composition of cells and tissue and was investigated to deduct the IDH1 mutation status.Experimental Design: We tested the technique on human cell lines that were transduced with wild-type IDH1 or mutated IDH1 and on 34 human glioma samples. IR spectra were acquired at 256 positions from cell pellets or tissue cryosections. Moreover, IR spectra were obtained from fresh, unprocessed biopsies of 64 patients with glioma.Results:IDH1 mutation was linked to changes in spectral bands assigned to molecular groups of lipids and proteins in cell lines and human glioma. The spectra of cryosections of brain tumor samples showed high interpatient variability, for example, bands related to calcifications at 1113 cm-1 However, supervised classification recognized relevant spectral regions at 1103, 1362, 1441, 1485, and 1553 cm-1 and assigned 88% of the tumor samples to the correct group. Similar spectral positions allowed the classification of spectra of fresh biopsies with an accuracy of 86%.Conclusions: Here, we show that vibrational spectroscopy reveals the IDH1 genotype of glioma. Because it can provide information in seconds, an implementation into the intraoperative workflow might allow simple and rapid online diagnosis of the IDH1 genotype. The intraoperative confirmation of IDH1 mutation status might guide the decision to pursue definitive neurosurgical resection and guide future in situ therapies of infiltrative gliomas. Clin Cancer Res; 24(11); 2530-8. ©2017 AACRSee related commentary by Hollon and Orringer, p. 2467.
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Affiliation(s)
- Ortrud Uckermann
- Neurosurgery, University Hospital Carl Gustav Carus, Technische Universität (TU) Dresden, Dresden, Germany.,German Cancer Consortium (DKTK) Dresden, National Center for Tumor Diseases (NCT), Partner Site Dresden, Dresden, Germany
| | - Tareq A Juratli
- Neurosurgery, University Hospital Carl Gustav Carus, Technische Universität (TU) Dresden, Dresden, Germany
| | - Roberta Galli
- Clinical Sensoring and Monitoring, Department of Anesthesiology and Intensive Care Medicine, Faculty of Medicine, TU Dresden, Dresden, Germany
| | - Marina Conde
- Neurosurgery, University Hospital Carl Gustav Carus, Technische Universität (TU) Dresden, Dresden, Germany
| | - Ralf Wiedemuth
- Neurosurgery, University Hospital Carl Gustav Carus, Technische Universität (TU) Dresden, Dresden, Germany
| | - Dietmar Krex
- Neurosurgery, University Hospital Carl Gustav Carus, Technische Universität (TU) Dresden, Dresden, Germany.,German Cancer Consortium (DKTK) Dresden, National Center for Tumor Diseases (NCT), Partner Site Dresden, Dresden, Germany
| | - Kathrin Geiger
- Neuropathology, University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Achim Temme
- Neurosurgery, University Hospital Carl Gustav Carus, Technische Universität (TU) Dresden, Dresden, Germany.,German Cancer Consortium (DKTK) Dresden, National Center for Tumor Diseases (NCT), Partner Site Dresden, Dresden, Germany
| | - Gabriele Schackert
- Neurosurgery, University Hospital Carl Gustav Carus, Technische Universität (TU) Dresden, Dresden, Germany.,German Cancer Consortium (DKTK) Dresden, National Center for Tumor Diseases (NCT), Partner Site Dresden, Dresden, Germany
| | - Edmund Koch
- Clinical Sensoring and Monitoring, Department of Anesthesiology and Intensive Care Medicine, Faculty of Medicine, TU Dresden, Dresden, Germany.,CRTD/DFG-Center for Regenerative Therapies Dresden - Cluster of Excellence, Dresden, Germany
| | - Gerald Steiner
- Clinical Sensoring and Monitoring, Department of Anesthesiology and Intensive Care Medicine, Faculty of Medicine, TU Dresden, Dresden, Germany.
| | - Matthias Kirsch
- Neurosurgery, University Hospital Carl Gustav Carus, Technische Universität (TU) Dresden, Dresden, Germany. .,German Cancer Consortium (DKTK) Dresden, National Center for Tumor Diseases (NCT), Partner Site Dresden, Dresden, Germany.,CRTD/DFG-Center for Regenerative Therapies Dresden - Cluster of Excellence, Dresden, Germany
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Lin Y, Xing Z, She D, Yang X, Zheng Y, Xiao Z, Wang X, Cao D. IDH mutant and 1p/19q co-deleted oligodendrogliomas: tumor grade stratification using diffusion-, susceptibility-, and perfusion-weighted MRI. Neuroradiology 2017; 59:555-562. [PMID: 28474187 PMCID: PMC5446560 DOI: 10.1007/s00234-017-1839-6] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2017] [Accepted: 04/18/2017] [Indexed: 12/24/2022]
Abstract
Purpose Currently, isocitrate dehydrogenase (IDH) mutation and 1p/19q co-deletion are proven diagnostic biomarkers for both grade II and III oligodendrogliomas (ODs). Non-invasive diffusion-weighted imaging (DWI), susceptibility-weighted imaging (SWI), and dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) are widely used to provide physiological information (cellularity, hemorrhage, calcifications, and angiogenesis) of neoplastic histology and tumor grade. However, it is unclear whether DWI, SWI, and DSC-PWI are able to stratify grades of IDH-mutant and 1p/19q co-deleted ODs. Methods We retrospectively reviewed the conventional MRI (cMRI), DWI, SWI, and DSC-PWI obtained on 33 patients with IDH-mutated and 1p/19q co-deleted ODs. Features of cMRI, normalized ADC (nADC), intratumoral susceptibility signals (ITSSs), normalized maxim CBV (nCBV), and normalized maximum CBF (nCBF) were compared between low-grade ODs (LGOs) and high-grade ODs (HGOs). Receiver operating characteristic curve and logistic regression were applied to determine diagnostic performances. Results HGOs tended to present with prominent edema and enhancement. nADC, ITSSs, nCBV, and nCBF were significantly different between groups (all P < 0.05). The combination of SWI and DSC-PWI for grading resulted in sensitivity and specificity of 100.00 and 93.33%, respectively. Conclusions IDH-mutant and 1p/19q co-deleted ODs can be stratified by grades using cMRI and advanced magnetic resonance imaging techniques including DWI, SWI, and DSC-PWI. Combined ITSSs with nCBV appear to be a promising option for grading molecularly defined ODs in clinical practice.
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Affiliation(s)
- Yu Lin
- Department of Radiology, First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, Fujian, 350005, People's Republic of China
| | - Zhen Xing
- Department of Radiology, First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, Fujian, 350005, People's Republic of China
| | - Dejun She
- Department of Radiology, First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, Fujian, 350005, People's Republic of China
| | - Xiefeng Yang
- Department of Radiology, First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, Fujian, 350005, People's Republic of China
| | - Yingyan Zheng
- Department of Radiology, First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, Fujian, 350005, People's Republic of China
| | - Zebin Xiao
- Department of Radiology, First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, Fujian, 350005, People's Republic of China
| | - Xingfu Wang
- Department of Pathology, First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Dairong Cao
- Department of Radiology, First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, Fujian, 350005, People's Republic of China.
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