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Cancers
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Hyperspectral and Multispectral Imaging Applications in Cancer Diagnosis and Detection
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Hyperspectral and Multispectral Imaging Applications in Cancer Diagnosis and Detection

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Causes, Screening and Diagnosis".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 8054

Special Issue Editor

Dr. Sylvain Gioux

E-Mail Website
Guest Editor
1. ICube Laboratory, University of Strasbourg, 67400 Strasbourg, France
2. Intuitive Surgical, 1170 Aubonne, Switzerland
Interests: multispectral imaging; hyperspectral imaging; optical diagnosis; tissue characterization; cancer detection

Special Issue Information

Dear Colleagues,

Optical detection methods applied to cancer diagnosis have witnessed a very rapid growth over the last decade. Among those methods, hyperspectral imaging (HSI) and multispectral imaging (MSI) take advantage of the spectral nature of the interactions between photons and living tissues to extract physiologically and structurally relevant information that could help healthcare professionals in detecting and diagnosing cancer. Such methods have been translated to humans and hold strong promise for being able to visualize cancer with high specificity and high sensitivity. This Special Issue aims to gather the latest and most advanced contributions in the field of HSI and MSI applied to cancer diagnosis and detection.

Dr. Sylvain Gioux
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cancers is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • multispectral imaging
  • hyperspectral imaging
  • optical diagnosis
  • tissue characterization
  • cancer detection

Published Papers (4 papers)

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Research

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17 pages, 2284 KiB  
Article
Tissue Classification of Breast Cancer by Hyperspectral Unmixing
by Lynn-Jade S. Jong, Anouk L. Post, Dinusha Veluponnar, Freija Geldof, Henricus J. C. M. Sterenborg, Theo J. M. Ruers and Behdad Dashtbozorg
Cancers 2023, 15(10), 2679; https://doi.org/10.3390/cancers15102679 - 9 May 2023
Cited by 1 | Viewed by 2100
Abstract
(1) Background: Assessing the resection margins during breast-conserving surgery is an important clinical need to minimize the risk of recurrent breast cancer. However, currently there is no technique that can provide real-time feedback to aid surgeons in the margin assessment. Hyperspectral imaging has [...] Read more.
(1) Background: Assessing the resection margins during breast-conserving surgery is an important clinical need to minimize the risk of recurrent breast cancer. However, currently there is no technique that can provide real-time feedback to aid surgeons in the margin assessment. Hyperspectral imaging has the potential to overcome this problem. To classify resection margins with this technique, a tissue discrimination model should be developed, which requires a dataset with accurate ground-truth labels. However, establishing such a dataset for resection specimens is difficult. (2) Methods: In this study, we therefore propose a novel approach based on hyperspectral unmixing to determine which pixels within hyperspectral images should be assigned to the ground-truth labels from histopathology. Subsequently, we use this hyperspectral-unmixing-based approach to develop a tissue discrimination model on the presence of tumor tissue within the resection margins of ex vivo breast lumpectomy specimens. (3) Results: In total, 372 measured locations were included on the lumpectomy resection surface of 189 patients. We achieved a sensitivity of 0.94, specificity of 0.85, accuracy of 0.87, Matthew’s correlation coefficient of 0.71, and area under the curve of 0.92. (4) Conclusion: Using this hyperspectral-unmixing-based approach, we demonstrated that the measured locations with hyperspectral imaging on the resection surface of lumpectomy specimens could be classified with excellent performance. Full article
(This article belongs to the Special Issue Hyperspectral and Multispectral Imaging Applications in Cancer Diagnosis and Detection)
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22 pages, 8310 KiB  
Article
Impact of Pre- and Post-Processing Steps for Supervised Classification of Colorectal Cancer in Hyperspectral Images
by Mariia Tkachenko, Claire Chalopin, Boris Jansen-Winkeln, Thomas Neumuth, Ines Gockel and Marianne Maktabi
Cancers 2023, 15(7), 2157; https://doi.org/10.3390/cancers15072157 - 5 Apr 2023
Viewed by 1697
Abstract
Background: Recent studies have shown that hyperspectral imaging (HSI) combined with neural networks can detect colorectal cancer. Usually, different pre-processing techniques (e.g., wavelength selection and scaling, smoothing, denoising) are analyzed in detail to achieve a well-trained network. The impact of post-processing was studied [...] Read more.
Background: Recent studies have shown that hyperspectral imaging (HSI) combined with neural networks can detect colorectal cancer. Usually, different pre-processing techniques (e.g., wavelength selection and scaling, smoothing, denoising) are analyzed in detail to achieve a well-trained network. The impact of post-processing was studied less. Methods: We tested the following methods: (1) Two pre-processing techniques (Standardization and Normalization), with (2) Two 3D-CNN models: Inception-based and RemoteSensing (RS)-based, with (3) Two post-processing algorithms based on median filter: one applies a median filter to a raw predictions map, the other applies the filter to the predictions map after adopting a discrimination threshold. These approaches were evaluated on a dataset that contains ex vivo hyperspectral (HS) colorectal cancer records of 56 patients. Results: (1) Inception-based models perform better than RS-based, with the best results being 92% sensitivity and 94% specificity; (2) Inception-based models perform better with Normalization, RS-based with Standardization; (3) Our outcomes show that the post-processing step improves sensitivity and specificity by 6.6% in total. It was also found that both post-processing algorithms have the same effect, and this behavior was explained. Conclusion: HSI combined with tissue classification algorithms is a promising diagnostic approach whose performance can be additionally improved by the application of the right combination of pre- and post-processing. Full article
(This article belongs to the Special Issue Hyperspectral and Multispectral Imaging Applications in Cancer Diagnosis and Detection)
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13 pages, 2684 KiB  
Article
Hyperspectral Imaging in Major Hepatectomies: Preliminary Results from the Ex-Machyna Trial
by Emanuele Felli, Lorenzo Cinelli, Elisa Bannone, Fabio Giannone, Edoardo Maria Muttillo, Manuel Barberio, Deborah Susan Keller, María Rita Rodríguez-Luna, Nariaki Okamoto, Toby Collins, Alexandre Hostettler, Catherine Schuster, Didier Mutter, Patrick Pessaux, Jacques Marescaux, Sylvain Gioux, Eric Felli and Michele Diana
Cancers 2022, 14(22), 5591; https://doi.org/10.3390/cancers14225591 - 14 Nov 2022
Cited by 5 | Viewed by 1647
Abstract
Ischemia-reperfusion injury during major hepatic resections is associated with high rates of post-operative complications and liver failure. Real-time intra-operative detection of liver dysfunction could provide great insight into clinical outcomes. In the present study, we demonstrate the intra-operative application of a novel optical [...] Read more.
Ischemia-reperfusion injury during major hepatic resections is associated with high rates of post-operative complications and liver failure. Real-time intra-operative detection of liver dysfunction could provide great insight into clinical outcomes. In the present study, we demonstrate the intra-operative application of a novel optical technology, hyperspectral imaging (HSI), to predict short-term post-operative outcomes after major hepatectomy. We considered fifteen consecutive patients undergoing major hepatic resection for malignant liver lesions from January 2020 to June 2021. HSI measures included tissue water index (TWI), organ hemoglobin index (OHI), tissue oxygenation (StO2%), and near infrared (NIR). Pre-operative, intra-operative, and post-operative serum and clinical outcomes were collected. NIR values were higher in unhealthy liver tissue (p = 0.003). StO2% negatively correlated with post-operative serum ALT values (r = −0.602), while ΔStO2% positively correlated with ALP (r = 0.594). TWI significantly correlated with post-operative reintervention and OHI with post-operative sepsis and liver failure. In conclusion, the HSI imaging system is accurate and precise in translating from pre-clinical to human studies in this first clinical trial. HSI indices are related to serum and outcome metrics. Further experimental and clinical studies are necessary to determine clinical value of this technology. Full article
(This article belongs to the Special Issue Hyperspectral and Multispectral Imaging Applications in Cancer Diagnosis and Detection)
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Review

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18 pages, 3491 KiB  
Review
A Comparison of Spectroscopy and Imaging Techniques Utilizing Spectrally Resolved Diffusely Reflected Light for Intraoperative Margin Assessment in Breast-Conserving Surgery: A Systematic Review and Meta-Analysis
by Dhurka Shanthakumar, Maria Leiloglou, Colm Kelliher, Ara Darzi, Daniel S. Elson and Daniel R. Leff
Cancers 2023, 15(11), 2884; https://doi.org/10.3390/cancers15112884 - 23 May 2023
Cited by 2 | Viewed by 1616
Abstract
Up to 19% of patients require re-excision surgery due to positive margins in breast-conserving surgery (BCS). Intraoperative margin assessment tools (IMAs) that incorporate tissue optical measurements could help reduce re-excision rates. This review focuses on methods that use and assess spectrally resolved diffusely [...] Read more.
Up to 19% of patients require re-excision surgery due to positive margins in breast-conserving surgery (BCS). Intraoperative margin assessment tools (IMAs) that incorporate tissue optical measurements could help reduce re-excision rates. This review focuses on methods that use and assess spectrally resolved diffusely reflected light for breast cancer detection in the intraoperative setting. Following PROSPERO registration (CRD42022356216), an electronic search was performed. The modalities searched for were diffuse reflectance spectroscopy (DRS), multispectral imaging (MSI), hyperspectral imaging (HSI), and spatial frequency domain imaging (SFDI). The inclusion criteria encompassed studies of human in vivo or ex vivo breast tissues, which presented data on accuracy. The exclusion criteria were contrast use, frozen samples, and other imaging adjuncts. 19 studies were selected following PRISMA guidelines. Studies were divided into point-based (spectroscopy) or whole field-of-view (imaging) techniques. A fixed-or random-effects model analysis generated pooled sensitivity/specificity for the different modalities, following heterogeneity calculations using the Q statistic. Overall, imaging-based techniques had better pooled sensitivity/specificity (0.90 (CI 0.76–1.03)/0.92 (CI 0.78–1.06)) compared with probe-based techniques (0.84 (CI 0.78–0.89)/0.85 (CI 0.79–0.91)). The use of spectrally resolved diffusely reflected light is a rapid, non-contact technique that confers accuracy in discriminating between normal and malignant breast tissue, and it constitutes a potential IMA tool. Full article
(This article belongs to the Special Issue Hyperspectral and Multispectral Imaging Applications in Cancer Diagnosis and Detection)
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