In vivo detection of head and neck tumors by hyperspectral imaging combined with deep learning methods

Eggert, Dennis; Bengs, Marcel; Westermann, Stephan; Gessert, Nils; Gerstner, Andreas O. H.; Mueller, N; Bewarder, J; Schlaefer, Alexander; Betz, Christian; Laffers, Wiebke

doi:10.1002/jbio.202100167

Cited by 24 publications

(21 citation statements)

References 32 publications

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“…However, while HSI was found to be more accurate in detecting cancer margins in conventional squamous cell HNC across nearly all contexts, autofluorescence proved to be superior in most aspects of HPV-positive HNC margin detection. In a smaller in vivo experiment of 24 subjects utilizing HSI imaging coupled with a 3D reconstructive algorithm, the accuracy, sensitivity, and specificity in determining tumorous tissues against healthy samples were found to be 81.3%, 83.3%, and 79.2%, respectively [ 46 ]. Similarly with other in vivo studies, limitations of this investigation included motion artifact and image noise from unamenable causes, such as patient pulse.…”

Section: Resultsmentioning

confidence: 99%

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Intraoperative In Vivo Imaging Modalities in Head and Neck Cancer Surgical Margin Delineation: A Systematic Review

Young

Kejriwal

et al. 2022

Cancers

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Surgical margin status is one of the strongest prognosticators in predicting patient outcomes in head and neck cancer, yet head and neck surgeons continue to face challenges in the accurate detection of these margins with the current standard of care. Novel intraoperative imaging modalities have demonstrated great promise for potentially increasing the accuracy and efficiency in surgical margin delineation. In this current study, we collated and analyzed various intraoperative imaging modalities utilized in head and neck cancer to evaluate their use in discriminating malignant from healthy tissues. The authors conducted a systematic database search through PubMed/Medline, Web of Science, and EBSCOhost (CINAHL). Study screening and data extraction were performed and verified by the authors, and more studies were added through handsearching. Here, intraoperative imaging modalities are described, including optical coherence tomography, narrow band imaging, autofluorescence, and fluorescent-tagged probe techniques. Available sensitivities and specificities in delineating cancerous from healthy tissues ranged from 83.0% to 100.0% and 79.2% to 100.0%, respectively, across the different imaging modalities. Many of these initial studies are in small sample sizes, with methodological differences that preclude more extensive quantitative comparison. Thus, there is impetus for future larger studies examining and comparing the efficacy of these intraoperative imaging technologies.

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Section: Resultsmentioning

confidence: 99%

“…Hyperspectral imaging [5,[42][43][44][45][46] Makes use of extended spectral information from tissues, outside the limited range of RGB wavelengths. This allows for the generation of a 2D image with a corresponding 3D dataset on wavelengths (hyperspectral cube).…”

Section: Similar Weaknesses As Nbimentioning

confidence: 99%

Intraoperative In Vivo Imaging Modalities in Head and Neck Cancer Surgical Margin Delineation: A Systematic Review

Young

Kejriwal

et al. 2022

Cancers

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“…Halicek et al [8] developed a CNN classifier to diagnose head and neck cancer using pre-processed HSI with 78% AUC across a spectral range of 450 nm to 900 nm. But in contrast to [5] and our investigation, the tumor categorization was done ex-vivo. Furthermore, none of these past works used HS cube at a low spatial resolution as they are realistic in the clinical application and used in our study, nor a U-Net.…”

Section: Introductionmentioning

confidence: 89%

“…Although HSI thus is promising, there are relatively few works of Deep Learning in HSI. Eggert et al [5] were able to predict in-vivo laryngeal cancer with a 2D CNN, and normal tissue within a spectral range of 380 nm to 680 nm in the visible spectrum and obtain an average AUC of 79%. However, longer wavelengths are favorable according to [6,7].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Wavelength Analysis with U-Net for Larynx Cancer Detection

Meyer-Veit¹,

Rayyes²,

Gerstner³

et al. 2022

ESANN 2022 Proceedings

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Early detection of laryngeal tumors is critical for their successful therapy. In this paper, we investigate how hyperspectral (HS) imaging can contribute to this aim based on an in-vivo data set of 13 HS image cubes recorded in clinical practice. We perform semantic segmentation with a tailored U-Net trained on labels provided by the clinicians. We specifically investigate the influence of exposure time during image acquisition, the suitable wavelengths to determine the most informative image channels, and present quantitative results on accuracy and the AUC measure.* The data set collection was funded by the German Cancer Aid within the project framework "Early detection of laryngeal cancer by means of Hyperspectral Imaging (109825110275)".

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“…Researchers have demonstrated the ability of HSI to detect a wide range of diseases, such as oximetry of the retinal ( Gao et al, 2012 ; Hadoux et al, 2019 ; Lim et al, 2021 ), intestinal ischemia identification ( Barberio et al, 2020 ; Mehdorn et al, 2020 ), histopathological tissue analysis ( Khouj et al, 2018 ), detecting cancer metastases in lung and lymph node tissue ( Zhang et al, 2021 ), blood vessel visualization enhancement ( Bjorgan et al, 2015 ; Fouad Aref et al, 2021 ), identifying skin tumors ( Leon et al, 2020 ; Courtenay et al, 2021 ), evaluating the cholesterol levels ( Milanic et al, 2015 ), diabetic foot, etc. In the field of oncology, HSI technology has been successfully applied to detect head and neck cancer ( Halicek et al, 2017 ; Eggert et al, 2022 ), thyroid and salivary glands ( Halicek et al, 2020 ), gastric cancer ( Li et al, 2019 ; Liu et al, 2020a ), oral cancer ( Jeyaraj et al, 2020 ), colon cancer ( Baltussen et al, 2019 ; Manni, 2020 ; Maktabi, 2021 ) as well as breast cancer ( Kho et al, 2019 ; Aboughaleb et al, 2020 ). Previously, other authors have published comprehensive overviews concerning the application of HSI in gastroenterology ( Ortega et al, 2019 ), wound care ( Saiko et al, 2020 ) or breast cancer therapy and diagnosis ( Aref et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Review on the Application of Hyperspectral Imaging Technology of the Exposed Cortex in Cerebral Surgery

Yang

et al. 2022

Front. Bioeng. Biotechnol.

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The study of brain science is vital to human health. The application of hyperspectral imaging in biomedical fields has grown dramatically in recent years due to their unique optical imaging method and multidimensional information acquisition. Hyperspectral imaging technology can acquire two-dimensional spatial information and one-dimensional spectral information of biological samples simultaneously, covering the ultraviolet, visible and infrared spectral ranges with high spectral resolution, which can provide diagnostic information about the physiological, morphological and biochemical components of tissues and organs. This technology also presents finer spectral features for brain imaging studies, and further provides more auxiliary information for cerebral disease research. This paper reviews the recent advance of hyperspectral imaging in cerebral diagnosis. Firstly, the experimental setup, image acquisition and pre-processing, and analysis methods of hyperspectral technology were introduced. Secondly, the latest research progress and applications of hyperspectral imaging in brain tissue metabolism, hemodynamics, and brain cancer diagnosis in recent years were summarized briefly. Finally, the limitations of the application of hyperspectral imaging in cerebral disease diagnosis field were analyzed, and the future development direction was proposed.

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In vivo detection of head and neck tumors by hyperspectral imaging combined with deep learning methods

Cited by 24 publications

References 32 publications

Intraoperative In Vivo Imaging Modalities in Head and Neck Cancer Surgical Margin Delineation: A Systematic Review

Intraoperative In Vivo Imaging Modalities in Head and Neck Cancer Surgical Margin Delineation: A Systematic Review

Hyperspectral Wavelength Analysis with U-Net for Larynx Cancer Detection

Review on the Application of Hyperspectral Imaging Technology of the Exposed Cortex in Cerebral Surgery

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