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Veluponnar D, de Boer LL, Dashtbozorg B, Jong LJS, Geldof F, Guimaraes MDS, Sterenborg HJCM, Vrancken-Peeters MJTFD, van Duijnhoven F, Ruers T. Margin assessment during breast conserving surgery using diffuse reflectance spectroscopy. J Biomed Opt 2024; 29:045006. [PMID: 38665316 PMCID: PMC11045169 DOI: 10.1117/1.jbo.29.4.045006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/03/2024] [Revised: 04/03/2024] [Accepted: 04/08/2024] [Indexed: 04/28/2024]
Abstract
Significance During breast-conserving surgeries, it is essential to evaluate the resection margins (edges of breast specimen) to determine whether the tumor has been removed completely. In current surgical practice, there are no methods available to aid in accurate real-time margin evaluation. Aim In this study, we investigated the diagnostic accuracy of diffuse reflectance spectroscopy (DRS) combined with tissue classification models in discriminating tumorous tissue from healthy tissue up to 2 mm in depth on the actual resection margin of in vivo breast tissue. Approach We collected an extensive dataset of DRS measurements on ex vivo breast tissue and in vivo breast tissue, which we used to develop different classification models for tissue classification. Next, these models were used in vivo to evaluate the performance of DRS for tissue discrimination during breast conserving surgery. We investigated which training strategy yielded optimum results for the classification model with the highest performance. Results We achieved a Matthews correlation coefficient of 0.76, a sensitivity of 96.7% (95% CI 95.6% to 98.2%), a specificity of 90.6% (95% CI 86.3% to 97.9%) and an area under the curve of 0.98 by training the optimum model on a combination of ex vivo and in vivo DRS data. Conclusions DRS allows real-time margin assessment with a high sensitivity and specificity during breast-conserving surgeries.
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Affiliation(s)
- Dinusha Veluponnar
- Netherlands Cancer Institute, Antoni van Leeuwenhoek, Department of Surgery, Image-Guided Surgery, Amsterdam, The Netherlands
- University of Twente, Department of Nanobiophysics, Faculty of Science and Technology, Enschede, The Netherlands
| | - Lisanne L. de Boer
- Netherlands Cancer Institute, Antoni van Leeuwenhoek, Department of Surgery, Image-Guided Surgery, Amsterdam, The Netherlands
| | - Behdad Dashtbozorg
- Netherlands Cancer Institute, Antoni van Leeuwenhoek, Department of Surgery, Image-Guided Surgery, Amsterdam, The Netherlands
| | - Lynn-Jade S. Jong
- Netherlands Cancer Institute, Antoni van Leeuwenhoek, Department of Surgery, Image-Guided Surgery, Amsterdam, The Netherlands
- University of Twente, Department of Nanobiophysics, Faculty of Science and Technology, Enschede, The Netherlands
| | - Freija Geldof
- Netherlands Cancer Institute, Antoni van Leeuwenhoek, Department of Surgery, Image-Guided Surgery, Amsterdam, The Netherlands
- University of Twente, Department of Nanobiophysics, Faculty of Science and Technology, Enschede, The Netherlands
| | - Marcos Da Silva Guimaraes
- Netherlands Cancer Institute, Antoni van Leeuwenhoek, Department of Pathology, Amsterdam, The Netherlands
| | - Henricus J. C. M. Sterenborg
- Amsterdam University Medical Center, Department of Biomedical Engineering and Physics, Amsterdam, The Netherlands
| | | | - Frederieke van Duijnhoven
- Netherlands Cancer Institute, Antoni van Leeuwenhoek, Department of Surgery, Image-Guided Surgery, Amsterdam, The Netherlands
| | - Theo Ruers
- Netherlands Cancer Institute, Antoni van Leeuwenhoek, Department of Surgery, Image-Guided Surgery, Amsterdam, The Netherlands
- University of Twente, Department of Nanobiophysics, Faculty of Science and Technology, Enschede, The Netherlands
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Jong LJS, Appelman JGC, Sterenborg HJCM, Ruers TJM, Dashtbozorg B. Spatial and Spectral Reconstruction of Breast Lumpectomy Hyperspectral Images. Sensors (Basel) 2024; 24:1567. [PMID: 38475103 DOI: 10.3390/s24051567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 02/23/2024] [Accepted: 02/27/2024] [Indexed: 03/14/2024]
Abstract
(1) Background: Hyperspectral imaging has emerged as a promising margin assessment technique for breast-conserving surgery. However, to be implicated intraoperatively, it should be both fast and capable of yielding high-quality images to provide accurate guidance and decision-making throughout the surgery. As there exists a trade-off between image quality and data acquisition time, higher resolution images come at the cost of longer acquisition times and vice versa. (2) Methods: Therefore, in this study, we introduce a deep learning spatial-spectral reconstruction framework to obtain a high-resolution hyperspectral image from a low-resolution hyperspectral image combined with a high-resolution RGB image as input. (3) Results: Using the framework, we demonstrate the ability to perform a fast data acquisition during surgery while maintaining a high image quality, even in complex scenarios where challenges arise, such as blur due to motion artifacts, dead pixels on the camera sensor, noise from the sensor's reduced sensitivity at spectral extremities, and specular reflections caused by smooth surface areas of the tissue. (4) Conclusion: This gives the opportunity to facilitate an accurate margin assessment through intraoperative hyperspectral imaging.
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Affiliation(s)
- Lynn-Jade S Jong
- Image-Guided Surgery, Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
- Department of Nanobiophysics, Faculty of Science and Technology, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands
| | - Jelmer G C Appelman
- Image-Guided Surgery, Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
- Faculty of Science, Vrije Universiteit Amsterdam, De Boelelaan 1111, 1081 HV Amsterdam, The Netherlands
| | - Henricus J C M Sterenborg
- Image-Guided Surgery, Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Theo J M Ruers
- Image-Guided Surgery, Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
- Department of Nanobiophysics, Faculty of Science and Technology, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands
| | - Behdad Dashtbozorg
- Image-Guided Surgery, Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
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Jong LJS, Post AL, Veluponnar D, Geldof F, Sterenborg HJCM, Ruers TJM, Dashtbozorg B. Tissue Classification of Breast Cancer by Hyperspectral Unmixing. Cancers (Basel) 2023; 15:2679. [PMID: 37345015 DOI: 10.3390/cancers15102679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 04/26/2023] [Accepted: 05/04/2023] [Indexed: 06/23/2023] Open
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 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.
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Affiliation(s)
- Lynn-Jade S Jong
- Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
- Department of Nanobiophysics, Faculty of Science and Technology, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands
| | - Anouk L Post
- Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Centers, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
| | - Dinusha Veluponnar
- Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
- Department of Nanobiophysics, Faculty of Science and Technology, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands
| | - Freija Geldof
- Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
- Department of Nanobiophysics, Faculty of Science and Technology, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands
| | - Henricus J C M Sterenborg
- Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Centers, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
| | - Theo J M Ruers
- Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
- Department of Nanobiophysics, Faculty of Science and Technology, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands
| | - Behdad Dashtbozorg
- Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
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Kho E, de Boer LL, Post AL, Van de Vijver KK, Jóźwiak K, Sterenborg HJCM, Ruers TJM. Imaging depth variations in hyperspectral imaging: Development of a method to detect tumor up to the required tumor-free margin width. J Biophotonics 2019; 12:e201900086. [PMID: 31290280 DOI: 10.1002/jbio.201900086] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Revised: 05/20/2019] [Accepted: 07/09/2019] [Indexed: 06/09/2023]
Abstract
Hyperspectral imaging is a promising technique for resection margin assessment during cancer surgery. Thereby, only a specific amount of the tissue below the resection surface, the clinically defined margin width, should be assessed. Since the imaging depth of hyperspectral imaging varies with wavelength and tissue composition, this can have consequences for the clinical use of hyperspectral imaging as margin assessment technique. In this study, a method was developed that allows for hyperspectral analysis of resection margins in breast cancer. This method uses the spectral slope of the diffuse reflectance spectrum at wavelength regions where the imaging depth in tumor and healthy tissue is equal. Thereby, tumor can be discriminated from healthy breast tissue while imaging up to a similar depth as the required tumor-free margin width of 2 mm. Applying this method to hyperspectral images acquired during surgery would allow for robust margin assessment of resected specimens. In this paper, we focused on breast cancer, but the same approach can be applied to develop a method for other types of cancer.
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Affiliation(s)
- Esther Kho
- Department of Surgery, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Lisanne L de Boer
- Department of Surgery, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Anouk L Post
- Department of Surgery, Netherlands Cancer Institute, Amsterdam, the Netherlands
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, Amsterdam, Netherlands
| | - Koen K Van de Vijver
- Department of Pathology, the Netherlands Cancer Institute, Amsterdam, Netherlands
- Department of Pathology, Ghent University Hospital, Ghent, Belgium
| | - Katarzyna Jóźwiak
- Department of Epidemiology and Biostatistics, the Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Henricus J C M Sterenborg
- Department of Surgery, Netherlands Cancer Institute, Amsterdam, the Netherlands
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, Amsterdam, Netherlands
| | - Theo J M Ruers
- Department of Surgery, Netherlands Cancer Institute, Amsterdam, the Netherlands
- Faculty of Science and Technology, University of Twente, Enschede, Netherlands
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Brouwer de Koning SG, Baltussen EJM, Karakullukcu MB, Dashtbozorg B, Smit LA, Dirven R, Hendriks BHW, Sterenborg HJCM, Ruers TJM. Toward complete oral cavity cancer resection using a handheld diffuse reflectance spectroscopy probe. J Biomed Opt 2018; 23:1-8. [PMID: 30341837 DOI: 10.1117/1.jbo.23.12.121611] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2018] [Accepted: 09/26/2018] [Indexed: 05/15/2023]
Abstract
This ex-vivo study evaluates the feasibility of diffuse reflectance spectroscopy (DRS) for discriminating tumor from healthy tissue, with the aim to develop a technology that can assess resection margins for the presence of tumor cells during oral cavity cancer surgery. Diffuse reflectance spectra were acquired on fresh surgical specimens from 28 patients with oral cavity squamous cell carcinoma. The spectra (400 to 1600 nm) were detected after illuminating tissue with a source fiber at 0.3-, 0.7-, 1.0-, and 2.0-mm distances from a detection fiber, obtaining spectral information from different sampling depths. The spectra were correlated with histopathology. A total of 76 spectra were obtained from tumor tissue and 110 spectra from healthy muscle tissue. The first- and second-order derivatives of the spectra were calculated and a classification algorithm was developed using fivefold cross validation with a linear support vector machine. The best results were obtained by the reflectance measured with a 1-mm source-detector distance (sensitivity, specificity, and accuracy are 89%, 82%, and 86%, respectively). DRS can accurately discriminate tumor from healthy tissue in an ex-vivo setting using a 1-mm source-detector distance. Accurate validation methods are warranted for larger sampling depths to allow for guidance during oral cavity cancer excision.
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Affiliation(s)
- Susan G Brouwer de Koning
- Netherlands Cancer Institute, Antoni van Leeuwenhoek, Department of Surgery, Amsterdam, The Netherlands
| | - Elisabeth J M Baltussen
- Netherlands Cancer Institute, Antoni van Leeuwenhoek, Department of Surgery, Amsterdam, The Netherlands
| | - M Baris Karakullukcu
- Netherlands Cancer Institute, Antoni van Leeuwenhoek, Department of Surgery, Amsterdam, The Netherlands
| | - Behdad Dashtbozorg
- Netherlands Cancer Institute, Antoni van Leeuwenhoek, Department of Surgery, Amsterdam, The Netherlands
| | - Laura A Smit
- Netherlands Cancer Institute, Antoni van Leeuwenhoek, Department of Pathology, Amsterdam, The Netherlands
| | - Richard Dirven
- Netherlands Cancer Institute, Antoni van Leeuwenhoek, Department of Surgery, Amsterdam, The Netherlands
| | - Benno H W Hendriks
- Philips Research, Department of In-body Systems, Eindhoven, The Netherlands
- Delft University of Technology, Department of Biomechanical Engineering, Delft, The Netherlands
| | - Henricus J C M Sterenborg
- Netherlands Cancer Institute, Antoni van Leeuwenhoek, Department of Surgery, Amsterdam, The Netherlands
- Academic Medical Centre, Department of Biomedical Engineering and Physics, Amsterdam, The Netherlands
| | - Theo J M Ruers
- Netherlands Cancer Institute, Antoni van Leeuwenhoek, Department of Surgery, Amsterdam, The Netherlands
- University of Twente, MIRA Institute, Enschede, The Netherlands
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