Elastic and inelastic light scattering spectroscopy and its possible use for label-free brain tumor typing

Ostertag, Edwin; Stefanakis, Mona; Rebner, Karsten; Kessler, Rudolf W.

doi:10.1007/s00216-017-0614-1

Cited by 13 publications

(16 citation statements)

References 44 publications

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“…Formalin fixation yields even better diagnostic results than native samples for the generation of spectroscopic multimodal datasets. Using this multimodal approach, UV and FTIR spectroscopy acquire the chemical information of the cross sections, whereas the ELS is linked to tissue morphology [33].…”

Section: Discussionmentioning

confidence: 99%

“…erefore, an increasing number of spectroscopic single techniques, especially Raman-and infrared (IR) spectroscopy, emerge, aiming at the classification of brain tumours [29][30][31][32]. Further studies consider elastic light scattering spectroscopy (ELS), which is particularly sensitive to morphological differences in tissues [33,34]. It enables one of the earliest detections of carcinogenesis [35][36][37].…”

Section: Introductionmentioning

confidence: 99%

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Formalin Fixation as Tissue Preprocessing for Multimodal Optical Spectroscopy Using the Example of Human Brain Tumour Cross Sections

Stefanakis

Lorenz

Bartsch

et al. 2021

Journal of Spectroscopy

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Characterization of brain tumours requires neuropathological expertise and is generally performed by histological evaluation and molecular analysis. One emerging technique to assist pathologists in future tumour diagnostics is multimodal optical spectroscopy. In the current clinical routine, tissue preprocessing with formalin is widely established and suitable for spectroscopic investigations since degradation processes impede the measurement of native tissue. However, formalin fixation results in alterations of the tissue chemistry and morphology for example by protein cross-linking. As optical spectroscopy is sensitive to these variations, we evaluate the effects of formalin fixation on multimodal brain tumour data in this proof-of-concept study. Nonfixed and formalin-fixed cross sections of different common human brain tumours were subjected to analysis of chemical variations using ultraviolet and Fourier-transform infrared microspectroscopy. Morphological changes were assessed by elastic light scattering microspectroscopy in the visible wavelength range. Data were analysed with multivariate data analysis and compared with histopathology. Tissue type classifications deduced by optical spectroscopy are highly comparable and independent from the preparation and the fixation protocol. However, formalin fixation leads to slightly better classification models due to improved stability of the tissue. As a consequence, spectroscopic methods represent an appropriate additional contrast for chemical and morphological information in neuropathological diagnosis and should be investigated to a greater extent. Furthermore, they can be included in the clinical workflow even after formalin fixation.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Formalin Fixation as Tissue Preprocessing for Multimodal Optical Spectroscopy Using the Example of Human Brain Tumour Cross Sections

Stefanakis

Lorenz

Bartsch

et al. 2021

Journal of Spectroscopy

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“…In the neurosurgical literature, the most common feature engineering algorithm is principal component analysis (PCA). 56,59,74,76,128,[131][132][133][134][135][136][137][138][139] PCA iteratively finds the orthogonal vectors (or principal components, PC) that maximize the variance in the dataset and then stores the projection of the data point upon each PC [ Fig. 5(b)].…”

Section: Supervised Machine Learningmentioning

confidence: 99%

“…The number of retained PCs ranges between 2 and 40 depending on either predefined criteria such as amount of explained variance 55,56,74,[134][135][136]138,139 or post hoc criteria, e.g., selecting PCs that could better differentiate the different tissue types. 76,[131][132][133] The popularity of PCA in RS can be attributed to its unsupervised nature: as it is agnostic to labels, it is considered unbiased. 140 Moreover, most authors report that over 99% of the variance of their dataset is expressed in the first 2 to 40 PCs 55,56,73,[134][135][136]138,139 and the orthogonality of the extracted features can improve the efficiency of classical multivariate linear models.…”

Section: Supervised Machine Learningmentioning

confidence: 99%

“…147 The features are selected either directly from the processed spectra or from previously extracted features, as part of a two-step feature engineering pipeline. [131][132][133]136,148 Most studies involving brain tissue rely on filter methods to identify important features, 149,150 and in some cases, features are selected manually. 148,151,152 In proteomics and genomics studies, performance-based and optimization-embedded have proven superior to filter methods in recovering truly important features in sparse, high-dimensional datasets.…”

Section: Supervised Machine Learningmentioning

confidence: 99%

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Rise of Raman spectroscopy in neurosurgery: a review

et al. 2020

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Significance: Although the clinical potential for Raman spectroscopy (RS) has been anticipated for decades, it has only recently been used in neurosurgery. Still, few devices have succeeded in making their way into the operating room. With recent technological advancements, however, vibrational sensing is poised to be a revolutionary tool for neurosurgeons. Aim: We give a summary of neurosurgical workflows and key translational milestones of RS in clinical use and provide the optics and data science background required to implement such devices. Approach: We performed an extensive review of the literature, with a specific emphasis on research that aims to build Raman systems suited for a neurosurgical setting. Results: The main translatable interest in Raman sensing rests in its capacity to yield label-free molecular information from tissue intraoperatively. Systems that have proven usable in the clinical setting are ergonomic, have a short integration time, and can acquire high-quality signal even in suboptimal conditions. Moreover, because of the complex microenvironment of brain tissue, data analysis is now recognized as a critical step in achieving high performance Raman-based sensing. Conclusions: The next generation of Raman-based devices are making their way into operating rooms and their clinical translation requires close collaboration between physicians, engineers, and data scientists.

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Inline and Online Process Analytical Technology with a Focus on Optical Spectroscopy

Kessler¹,

Maiwald²,

Kessler³

2022

Encyclopedia of Analytical Chemistry

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Process analytical technology (PAT) is a cross‐sectional technology and thus essential for future smart production. While in the past decades, the focus of process optimization strategies was on increasing efficiency, in the future, the focus will be on the sustainability of a production and its products. In addition, products will be increasingly personalized in order to match the property profile exactly to the intended use. PAT is able to provide context‐sensitive information at the molecular level for process control. Spectroscopic sensors can determine inline and simultaneously both the chemical composition and its sub‐microscopic morphology. The article will focus on the optical spectroscopy and therefore starts with a brief introduction on the basic concepts of molecular spectroscopy. In addition, the particularities of measuring liquids, surfaces, or particulate systems in PAT applications are described. This should enable the reader to select the appropriate method for the specific problem. Many examples from everyday industrial practice illustrate the applications. The areas covered are the manufacturing industry, process and pharmaceutical industry, food industry, as well as biotechnology and medical technology. Future will show that PAT is especially important for applications in the field of medicine (point of care) circular economy (recycling, water–wastewater, etc.). It is important to emphasize that sustainability in industrial production can only be successful with an inter‐ and transdisciplinary close exchange between the different disciplines.

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Elastic and inelastic light scattering spectroscopy and its possible use for label-free brain tumor typing

Cited by 13 publications

References 44 publications

Formalin Fixation as Tissue Preprocessing for Multimodal Optical Spectroscopy Using the Example of Human Brain Tumour Cross Sections

Formalin Fixation as Tissue Preprocessing for Multimodal Optical Spectroscopy Using the Example of Human Brain Tumour Cross Sections

Rise of Raman spectroscopy in neurosurgery: a review

Inline and Online Process Analytical Technology with a Focus on Optical Spectroscopy

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