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Geldof F, Schrage YM, van Houdt WJ, Sterenborg HJCM, Dashtbozorg B, Ruers TJM. Toward the use of diffuse reflection spectroscopy for intra-operative tissue discrimination during sarcoma surgery. JOURNAL OF BIOMEDICAL OPTICS 2024; 29:027001. [PMID: 38361507 PMCID: PMC10869119 DOI: 10.1117/1.jbo.29.2.027001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 01/23/2024] [Accepted: 01/24/2024] [Indexed: 02/17/2024]
Abstract
Significance Accurately distinguishing tumor tissue from normal tissue is crucial to achieve complete resections during soft tissue sarcoma (STS) surgery while preserving critical structures. Incomplete tumor resections are associated with an increased risk of local recurrence and worse patient prognosis. Aim We evaluate the performance of diffuse reflectance spectroscopy (DRS) to distinguish tumor tissue from healthy tissue in STSs. Approach DRS spectra were acquired from different tissue types on multiple locations in 20 freshly excised sarcoma specimens. A k -nearest neighbors classification model was trained to predict the tissue types of the measured locations, using binary and multiclass approaches. Results Tumor tissue could be distinguished from healthy tissue with a classification accuracy of 0.90, sensitivity of 0.88, and specificity of 0.93 when well-differentiated liposarcomas were included. Excluding this subtype, the classification performance increased to an accuracy of 0.93, sensitivity of 0.94, and specificity of 0.93. The developed model showed a consistent performance over different histological subtypes and tumor locations. Conclusions Automatic tissue discrimination using DRS enables real-time intra-operative guidance, contributing to more accurate STS resections.
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Affiliation(s)
- Freija Geldof
- Netherlands Cancer Institute, Image-Guided Surgery, Department of Surgery, Amsterdam, The Netherlands
- University of Twente, Faculty of Science and Technology, Enschede, The Netherlands
| | - Yvonne M. Schrage
- Netherlands Cancer Institute, Image-Guided Surgery, Department of Surgery, Amsterdam, The Netherlands
| | - Winan J. van Houdt
- Netherlands Cancer Institute, Image-Guided Surgery, Department of Surgery, Amsterdam, The Netherlands
| | | | - Behdad Dashtbozorg
- Netherlands Cancer Institute, Image-Guided Surgery, Department of Surgery, Amsterdam, The Netherlands
| | - Theo J. M. Ruers
- Netherlands Cancer Institute, Image-Guided Surgery, Department of Surgery, Amsterdam, The Netherlands
- University of Twente, Faculty of Science and Technology, Enschede, The Netherlands
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Nazarian S, Gkouzionis I, Murphy J, Darzi A, Patel N, Peters CJ, Elson DS. Real-time classification of tumour and non-tumour tissue in colorectal cancer using diffuse reflectance spectroscopy and neural networks to aid margin assessment. Int J Surg 2024; 110:01279778-990000000-01004. [PMID: 38241421 PMCID: PMC11020003 DOI: 10.1097/js9.0000000000001102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 01/08/2024] [Indexed: 01/21/2024]
Abstract
BACKGROUND Colorectal cancer is the third most commonly diagnosed malignancy and the second leading cause of mortality worldwide. A positive resection margin following surgery for colorectal cancer is linked with higher rates of local recurrence and poorer survival. We investigated diffuse reflectance spectroscopy (DRS) to distinguish tumour and non-tumour tissue in ex vivo colorectal specimens, to aid margin assessment and provide augmented visual maps to the surgeon in real-time. METHODS Patients undergoing elective colorectal cancer resection surgery at a London-based hospital were prospectively recruited. A hand-held DRS probe was used on the surface of freshly resected ex vivo colorectal tissue. Spectral data was acquired for tumour and non-tumour tissue. Binary classification was achieved using conventional machine learning classifiers and a convolutional neural network (CNN), which were evaluated in terms of sensitivity, specificity, accuracy and the area under the curve. RESULTS A total of 7692 mean spectra were obtained for tumour and non-tumour colorectal tissue. The CNN-based classifier was the best performing machine learning algorithm, when compared to contrastive approaches, for differentiating tumour and non-tumour colorectal tissue, with an overall diagnostic accuracy of 90.8% and area under the curve of 96.8%. Live on-screen classification of tissue type was achieved using a graduated colourmap. CONCLUSION A high diagnostic accuracy for a DRS probe and tracking system to differentiate ex vivo tumour and non-tumour colorectal tissue in real-time with on-screen visual feedback was highlighted by this study. Further in vivo studies are needed to ensure integration into a surgical workflow.
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Affiliation(s)
| | - Ioannis Gkouzionis
- Department of Surgery and Cancer
- Hamlyn Centre for Robotics Surgery, Imperial College London, London, UK
| | | | - Ara Darzi
- Department of Surgery and Cancer
- Hamlyn Centre for Robotics Surgery, Imperial College London, London, UK
| | | | | | - Daniel S. Elson
- Department of Surgery and Cancer
- Hamlyn Centre for Robotics Surgery, Imperial College London, London, UK
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3
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Saito Nogueira M, Maryam S, Amissah M, Killeen S, O'Riordain M, Andersson-Engels S. Diffuse reflectance spectroscopy for colorectal cancer surgical guidance: towards real-time tissue characterization and new biomarkers. Analyst 2023; 149:88-99. [PMID: 37994161 DOI: 10.1039/d3an00261f] [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: 11/24/2023]
Abstract
Colorectal cancer (CRC) is the third most common and second most deadly type of cancer worldwide, representing 11.3% of the diagnosed cancer cases and resulting in 10.2% (0.88 million) of the cancer related deaths in 2020. CRCs are typically detected at the late stage, which leads to high mortality and morbidity. Mortality and poor prognosis are partially caused by cancer recurrence and postoperative complications. Patient survival could be increased by improving precision in surgical resection using accurate surgical guidance tools based on diffuse reflectance spectroscopy (DRS). DRS enables real-time tissue identification for potential cancer margin delineation through determination of the circumferential resection margin (CRM), while also supporting non-invasive and label-free approaches for laparoscopic surgery to avoid short-term complications of open surgery as suitable. In this study, we have estimated the scattering properties and chromophore concentrations based on 2949 DRS measurements of freshly excised ex vivo specimens of 47 patients, and used this estimation to classify normal colorectal wall (CW), fat and tumor tissues. DRS measurements were performed with fiber-optic probes of 630 μm source-detector distance (SDD; probe 1) and 2500 μm SDD (probe 2) to measure tissue layers ∼0.5-1 mm and ∼0.5-2 mm deep, respectively. By using the 5-fold cross-validation of machine learning models generated with the classification and regression tree (CART) algorithm, we achieved 95.9 ± 0.7% sensitivity, 98.9 ± 0.3% specificity, 90.2 ± 0.4% accuracy, and 95.5 ± 0.3% AUC for probe 1. Similarly, we achieved 96.9 ± 0.8% sensitivity, 98.9 ± 0.2% specificity, 94.0 ± 0.4% accuracy, and 96.7 ± 0.4% AUC for probe 2.
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Affiliation(s)
- Marcelo Saito Nogueira
- Tyndall National Institute, Lee Maltings, Dyke Parade, Cork, T12 R5CP, Ireland.
- Department of Physics, University College Cork, College Road, Cork, T12 K8AF, Ireland
| | - Siddra Maryam
- Tyndall National Institute, Lee Maltings, Dyke Parade, Cork, T12 R5CP, Ireland.
- Department of Physics, University College Cork, College Road, Cork, T12 K8AF, Ireland
| | - Michael Amissah
- Tyndall National Institute, Lee Maltings, Dyke Parade, Cork, T12 R5CP, Ireland.
- Department of Physics, University College Cork, College Road, Cork, T12 K8AF, Ireland
| | - Shane Killeen
- Department of Surgery, Mercy University Hospital, Cork, T12 WE28, Ireland
| | - Micheal O'Riordain
- Department of Surgery, Mercy University Hospital, Cork, T12 WE28, Ireland
| | - Stefan Andersson-Engels
- Tyndall National Institute, Lee Maltings, Dyke Parade, Cork, T12 R5CP, Ireland.
- Department of Physics, University College Cork, College Road, Cork, T12 K8AF, Ireland
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4
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Spinelli A, Carrano FM, Laino ME, Andreozzi M, Koleth G, Hassan C, Repici A, Chand M, Savevski V, Pellino G. Artificial intelligence in colorectal surgery: an AI-powered systematic review. Tech Coloproctol 2023; 27:615-629. [PMID: 36805890 DOI: 10.1007/s10151-023-02772-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 02/07/2023] [Indexed: 02/23/2023]
Abstract
Artificial intelligence (AI) has the potential to revolutionize surgery in the coming years. Still, it is essential to clarify what the meaningful current applications are and what can be reasonably expected. This AI-powered review assessed the role of AI in colorectal surgery. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-compliant systematic search of PubMed, Embase, Scopus, Cochrane Library databases, and gray literature was conducted on all available articles on AI in colorectal surgery (from January 1 1997 to March 1 2021), aiming to define the perioperative applications of AI. Potentially eligible studies were identified using novel software powered by natural language processing (NLP) and machine learning (ML) technologies dedicated to systematic reviews. Out of 1238 articles identified, 115 were included in the final analysis. Available articles addressed the role of AI in several areas of interest. In the preoperative phase, AI can be used to define tailored treatment algorithms, support clinical decision-making, assess the risk of complications, and predict surgical outcomes and survival. Intraoperatively, AI-enhanced surgery and integration of AI in robotic platforms have been suggested. After surgery, AI can be implemented in the Enhanced Recovery after Surgery (ERAS) pathway. Additional areas of applications included the assessment of patient-reported outcomes, automated pathology assessment, and research. Available data on these aspects are limited, and AI in colorectal surgery is still in its infancy. However, the rapid evolution of technologies makes it likely that it will increasingly be incorporated into everyday practice.
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Affiliation(s)
- A Spinelli
- IRCCS Humanitas Research Hospital, via Manzoni 56, 20089, Rozzano, MI, Italy.
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090, Pieve Emanuele, MI, Italy.
| | - F M Carrano
- IRCCS Humanitas Research Hospital, via Manzoni 56, 20089, Rozzano, MI, Italy
| | - M E Laino
- Artificial Intelligence Center, Humanitas Clinical and Research Center-IRCCS, Via A. Manzoni 56, 20089, Rozzano, MI, Italy
| | - M Andreozzi
- Department of Clinical Medicine and Surgery, University "Federico II" of Naples, Naples, Italy
| | - G Koleth
- Department of Gastroenterology and Hepatology, Hospital Selayang, Selangor, Malaysia
| | - C Hassan
- IRCCS Humanitas Research Hospital, via Manzoni 56, 20089, Rozzano, MI, Italy
| | - A Repici
- IRCCS Humanitas Research Hospital, via Manzoni 56, 20089, Rozzano, MI, Italy
| | - M Chand
- Wellcome EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, London, UK
| | - V Savevski
- Artificial Intelligence Center, Humanitas Clinical and Research Center-IRCCS, Via A. Manzoni 56, 20089, Rozzano, MI, Italy
| | - G Pellino
- Department of Advanced Medical and Surgical Sciences, Università degli Studi della Campania "Luigi Vanvitelli", Naples, Italy
- Colorectal Surgery, Vall d'Hebron University Hospital, Universitat Autonoma de Barcelona UAB, Barcelona, Spain
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5
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Geldof F, Witteveen M, Sterenborg HJCM, Ruers TJM, Dashtbozorg B. Diffuse reflection spectroscopy at the fingertip: design and performance of a compact side-firing probe for tissue discrimination during colorectal cancer surgery. BIOMEDICAL OPTICS EXPRESS 2023; 14:128-147. [PMID: 36698675 PMCID: PMC9841999 DOI: 10.1364/boe.476242] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 11/14/2022] [Accepted: 11/14/2022] [Indexed: 06/17/2023]
Abstract
Optical technologies are widely used for tissue sensing purposes. However, maneuvering conventional probe designs with flat-tipped fibers in narrow spaces can be challenging, for instance during pelvic colorectal cancer surgery. In this study, a compact side-firing fiber probe was developed for tissue discrimination during colorectal cancer surgery using diffuse reflectance spectroscopy. The optical behavior was compared to flat-tipped fibers using both Monte Carlo simulations and experimental phantom measurements. The tissue classification performance was examined using freshly excised colorectal cancer specimens. Using the developed probe and classification algorithm, an accuracy of 0.92 was achieved for discriminating tumor tissue from healthy tissue.
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Affiliation(s)
- Freija Geldof
- Department of Surgery, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Mark Witteveen
- Department of Surgery, Netherlands Cancer Institute, Amsterdam, The 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, The Netherlands
| | - Theo J. M. Ruers
- Department of Surgery, Netherlands Cancer Institute, Amsterdam, The Netherlands
- Faculty of Science and Technology, University of Twente, Enschede, The Netherlands
| | - Behdad Dashtbozorg
- Department of Surgery, Netherlands Cancer Institute, Amsterdam, The Netherlands
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6
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Insights into Biochemical Sources and Diffuse Reflectance Spectral Features for Colorectal Cancer Detection and Localization. Cancers (Basel) 2022; 14:cancers14225715. [PMID: 36428806 PMCID: PMC9688116 DOI: 10.3390/cancers14225715] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 11/07/2022] [Accepted: 11/09/2022] [Indexed: 11/23/2022] Open
Abstract
Colorectal cancer (CRC) is the third most common and second most deadly type of cancer worldwide. Early detection not only reduces mortality but also improves patient prognosis by allowing the use of minimally invasive techniques to remove cancer while avoiding major surgery. Expanding the use of microsurgical techniques requires accurate diagnosis and delineation of the tumor margins in order to allow complete excision of cancer. We have used diffuse reflectance spectroscopy (DRS) to identify the main optical CRC biomarkers and to optimize parameters for the integration of such technologies into medical devices. A total number of 2889 diffuse reflectance spectra were collected in ex vivo specimens from 47 patients. Short source-detector distance (SDD) and long-SDD fiber-optic probes were employed to measure tissue layers from 0.5 to 1 mm and from 0.5 to 1.9 mm deep, respectively. The most important biomolecules contributing to differentiating DRS between tissue types were oxy- and deoxy-hemoglobin (Hb and HbO2), followed by water and lipid. Accurate tissue classification and potential DRS device miniaturization using Hb, HbO2, lipid and water data were achieved particularly well within the wavelength ranges 350-590 nm and 600-1230 nm for the short-SDD probe, and 380-400 nm, 420-610 nm, and 650-950 nm for the long-SDD probe.
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7
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Reistad N, Sturesson C. Distinguishing tumor from healthy tissue in human liver ex vivo using machine learning and multivariate analysis of diffuse reflectance spectra. JOURNAL OF BIOPHOTONICS 2022; 15:e202200140. [PMID: 35860880 DOI: 10.1002/jbio.202200140] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 06/27/2022] [Accepted: 07/12/2022] [Indexed: 06/15/2023]
Abstract
The aim of this work was to evaluate the capability of diffuse reflectance spectroscopy to distinguish malignant liver tissues from surrounding tissues and to determine whether an extended wavelength range (450-1550 nm) offers any advantages over using the conventional wavelength range. Furthermore, multivariate analysis combined with a machine learning algorithm, either linear discriminant analysis or the more advanced support vector machine, was used to discriminate between and classify freshly excised human liver specimens from 18 patients. Tumors were distinguished from surrounding liver tissues with a sensitivity of 99%, specificity of 100%, classification rate of 100% and a Matthews correlation coefficient of 100% using the extended wavelength range and a combination of principal component analysis and support vector techniques. The results indicate that this technology may be useful in clinical applications for real-time tissue diagnostics of tumor margins where rapid classification is important.
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Affiliation(s)
- Nina Reistad
- Department of Physics, Lund University, Lund, Sweden
| | - Christian Sturesson
- Division of Surgery, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
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8
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Geldof F, Dashtbozorg B, Hendriks BHW, Sterenborg HJCM, Ruers TJM. Layer thickness prediction and tissue classification in two-layered tissue structures using diffuse reflectance spectroscopy. Sci Rep 2022; 12:1698. [PMID: 35105926 PMCID: PMC8807816 DOI: 10.1038/s41598-022-05751-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 01/12/2022] [Indexed: 11/26/2022] Open
Abstract
During oncological surgery, it can be challenging to identify the tumor and establish adequate resection margins. This study proposes a new two-layer approach in which diffuse reflectance spectroscopy (DRS) is used to predict the top layer thickness and classify the layers in two-layered phantom and animal tissue. Using wavelet-based and peak-based DRS spectral features, the proposed method could predict the top layer thickness with an accuracy of up to 0.35 mm. In addition, the tissue types of the first and second layers were classified with an accuracy of 0.95 and 0.99. Distinguishing multiple tissue layers during spectral analyses results in a better understanding of more complex tissue structures encountered in surgical practice.
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Affiliation(s)
- Freija Geldof
- Department of Surgery, Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands.
| | - Behdad Dashtbozorg
- Department of Surgery, Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands
| | - Benno H W Hendriks
- Department of IGT and US Devices & Systems, Philips Research Laboratories, 5656 AE, Eindhoven, The Netherlands
- Department of BioMechanical Engineering, 3mE, Delft University of Technology, 2628 CD, Delft, The Netherlands
| | - Henricus J C M Sterenborg
- Department of Surgery, Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, 1105 AZ, Amsterdam, The Netherlands
| | - Theo J M Ruers
- Department of Surgery, Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands
- Faculty of Science and Technology, University of Twente, 7522 NB, Enschede, The Netherlands
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9
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Reijers SJM, Heerink WJ, Van Veen R, Nijkamp J, Hoetjes NJ, Schrage Y, Van Akkooi A, Beets GL, Van Coevorden F, Ruers TJM, Groen HC, Van Houdt WJ. Surgical navigation for challenging recurrent or pretreated intra-abdominal and pelvic soft tissue sarcomas. J Surg Oncol 2021; 124:1173-1181. [PMID: 34320228 DOI: 10.1002/jso.26624] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 06/04/2021] [Accepted: 07/09/2021] [Indexed: 01/04/2023]
Abstract
BACKGROUND This study assessed whether electromagnetic navigation can be of added value during resection of recurrent or post-therapy intra-abdominal/pelvic soft tissue sarcomas (STS) in challenging locations. MATERIALS AND METHODS Patients were included in a prospective navigation study. A pre-operatively 3D roadmap was made and tracked using electromagnetic reference markers. During the operation, an electromagnetic pointer was used for the localization of the tumor/critical anatomical structures. The primary endpoint was feasibility, secondary outcomes were safety and usability. RESULTS Nine patients with a total of 12 tumors were included, 7 patients with locally recurrent sarcoma. Three patients received neoadjuvant radiotherapy and three other patients received neoadjuvant systemic treatment. The median tumor size was 4.6 cm (2.4-10.4). The majority of distances from tumor to critical anatomical structures was <0.5 cm. The tumors were localized using the navigation system without technical or safety issues. Despite the challenging nature of these resections, 89% were R0 resections, with a median blood loss of 100 ml (20-1050) and one incident of vascular damage. Based on the survey, surgeons stated navigation resulted in shorter surgery time and made the resections easier. CONCLUSION Electromagnetic navigation facilitates resections of challenging lower intra-abdominal/pelvic STS and might be of added value.
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Affiliation(s)
- Sophie J M Reijers
- Department of Surgical Oncology, The Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Wouter J Heerink
- Department of Surgical Oncology, The Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Ruben Van Veen
- Department of Surgical Oncology, The Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Jasper Nijkamp
- Department of Surgical Oncology, The Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Nikie J Hoetjes
- Department of Surgical Oncology, The Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Yvonne Schrage
- Department of Surgical Oncology, The Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Alexander Van Akkooi
- Department of Surgical Oncology, The Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Geerard L Beets
- Department of Surgical Oncology, The Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Frits Van Coevorden
- Department of Surgical Oncology, The Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Theo J M Ruers
- Department of Surgical Oncology, The Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands.,Faculty of Science and Technology (TNW), Nanobiophysics Group, Technical University of Twente, Enschede, The Netherlands
| | - Harald C Groen
- Department of Surgical Oncology, The Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Winan J Van Houdt
- Department of Surgical Oncology, The Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
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Dijkstra EA, Mul VEM, Hemmer PHJ, Havenga K, Hospers GAP, Kats-Ugurlu G, Beukema JC, Berveling MJ, El Moumni M, Muijs CT, van Etten B. Clinical selection strategy for and evaluation of intra-operative brachytherapy in patients with locally advanced and recurrent rectal cancer. Radiother Oncol 2021; 159:91-97. [PMID: 33741470 DOI: 10.1016/j.radonc.2021.03.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Revised: 03/05/2021] [Accepted: 03/08/2021] [Indexed: 01/01/2023]
Abstract
BACKGROUND AND PURPOSE A radical resection of locally advanced rectal cancer (LARC) or recurrent rectal cancer (RRC) can be challenging. In case of increased risk of an R1 resection, intra-operative brachytherapy (IOBT) can be applied. We evaluated the clinical selection strategy for IOBT. MATERIALS AND METHODS Between February 2007 and May 2018, 132 LARC/RRC patients who were scheduled for surgery with IOBT standby, were evaluated. By intra-operative inspection of the resection margin and MR imaging, it was determined whether a resection was presumed to be radical. Frozen sections were taken on indication. In case of a suspected R1 resection, IOBT (1 × 10 Gy) was applied. Histopathologic evaluation, treatment and toxicity data were collected from medical records. RESULTS Tumour was resected in 122 patients. IOBT was given in 42 patients of whom 54.8% (n = 23) had a histopathologically proven R1 resection. Of the 76 IOBT-omitted R0 resected patients, 17.1% (n = 13) had a histopathologically proven R1 resection. In 4 IOBT-omitted patients, a clinical R1/2 resection was seen. In total, correct clinical judgement occurred in 72.6% (n = 88) of patients. In LARC, 58.3% (n = 14) of patients were overtreated (R0, with IOBT) and 10.9% (n = 5) were undertreated (R1, without IOBT). In RRC, 26.5% (n = 9) of patients were undertreated. CONCLUSION In total, correct clinical judgement occurred in 72.6% (n = 88). However, in 26.5% (n = 9) RRC patients, IOBT was unjustifiedly omitted. IOBT is accompanied by comparable and acceptable toxicity. Therefore, we recommend IOBT to all RRC patients at risk of an R1 resection as their salvage treatment.
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Affiliation(s)
- Esmée A Dijkstra
- University of Groningen, University Medical Centre Groningen, Department of Medical Oncology, the Netherlands
| | - Véronique E M Mul
- University of Groningen, University Medical Centre Groningen, Department of Radiation Oncology, the Netherlands
| | - Patrick H J Hemmer
- University of Groningen, University Medical Centre Groningen, Department of Surgery, the Netherlands
| | - Klaas Havenga
- University of Groningen, University Medical Centre Groningen, Department of Surgery, the Netherlands
| | - Geke A P Hospers
- University of Groningen, University Medical Centre Groningen, Department of Medical Oncology, the Netherlands
| | - Gursah Kats-Ugurlu
- University of Groningen, University Medical Centre Groningen, Department of Pathology and Medical Biology, the Netherlands
| | - Jannet C Beukema
- University of Groningen, University Medical Centre Groningen, Department of Radiation Oncology, the Netherlands
| | - Maaike J Berveling
- University of Groningen, University Medical Centre Groningen, Department of Radiation Oncology, the Netherlands
| | - Mostafa El Moumni
- University of Groningen, University Medical Centre Groningen, Department of Surgery, the Netherlands
| | - Christina T Muijs
- University of Groningen, University Medical Centre Groningen, Department of Radiation Oncology, the Netherlands
| | - Boudewijn van Etten
- University of Groningen, University Medical Centre Groningen, Department of Surgery, the Netherlands.
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11
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Matthies L, Gebrekidan MT, Tegtmeyer JF, Oetter N, Rohde M, Vollkommer T, Smeets R, Wilczak W, Stelzle F, Gosau M, Braeuer AS, Knipfer C. Optical diagnosis of oral cavity lesions by label-free Raman spectroscopy. BIOMEDICAL OPTICS EXPRESS 2021; 12:836-851. [PMID: 33680545 PMCID: PMC7901324 DOI: 10.1364/boe.409456] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 11/21/2020] [Accepted: 12/08/2020] [Indexed: 06/12/2023]
Abstract
Oral squamous cell carcinoma (OSCC) is one of the most prevalent cancers and frequently preceded by non-malignant lesions. Using Shifted-Excitation Raman Difference Spectroscopy (SERDS), principal component and linear discriminant analysis in native tissue specimens, 9500 raw Raman spectra of OSCC, 4300 of non-malignant lesions and 4200 of physiological mucosa were evaluated. Non-malignant lesions were distinguished from physiological mucosa with a classification accuracy of 95.3% (95.4% sensitivity, 95.2% specificity, area under the curve (AUC) 0.99). Discriminating OSCC from non-malignant lesions showed an accuracy of 88.4% (93.7% sensitivity, 76.7% specificity, AUC 0.93). OSCC was identified against physiological mucosa with an accuracy of 89.8% (93.7% sensitivity, 81.0% specificity, AUC 0.90). These findings underline the potential of SERDS for the diagnosis of oral cavity lesions.
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Affiliation(s)
- Levi Matthies
- University Medical Center Hamburg-Eppendorf (UKE), Department of Oral and Maxillofacial Surgery, Martinistraße 52, D-20246 Hamburg, Germany
- These authors contributed equally
| | - Medhanie T. Gebrekidan
- Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen Graduate School in Advanced Optical Technologies (SAOT), Paul-Gordan-Straße 6, D-91054 Erlangen, Germany
- Technische Universität Bergakademie Freiberg (TUBAF), Institute of Thermal-, Environmental- and Resources‘ Process Engineering (ITUN), Leipziger Straße 28, D-09599 Freiberg, Germany
- These authors contributed equally
| | - Jasper F. Tegtmeyer
- University Medical Center Hamburg-Eppendorf (UKE), Department of Oral and Maxillofacial Surgery, Martinistraße 52, D-20246 Hamburg, Germany
| | - Nicolai Oetter
- Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen Graduate School in Advanced Optical Technologies (SAOT), Paul-Gordan-Straße 6, D-91054 Erlangen, Germany
- Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Department of Oral and Maxillofacial Surgery, Glückstraße 11, D-91054 Erlangen, Germany
| | - Maximilian Rohde
- Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Department of Oral and Maxillofacial Surgery, Glückstraße 11, D-91054 Erlangen, Germany
| | - Tobias Vollkommer
- University Medical Center Hamburg-Eppendorf (UKE), Department of Oral and Maxillofacial Surgery, Martinistraße 52, D-20246 Hamburg, Germany
| | - Ralf Smeets
- University Medical Center Hamburg-Eppendorf (UKE), Department of Oral and Maxillofacial Surgery, Martinistraße 52, D-20246 Hamburg, Germany
| | - Waldemar Wilczak
- University Medical Center Hamburg-Eppendorf (UKE), Institute of Pathology, Martinistraße 52, D-20246 Hamburg, Germany
| | - Florian Stelzle
- Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen Graduate School in Advanced Optical Technologies (SAOT), Paul-Gordan-Straße 6, D-91054 Erlangen, Germany
- Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Department of Oral and Maxillofacial Surgery, Glückstraße 11, D-91054 Erlangen, Germany
| | - Martin Gosau
- University Medical Center Hamburg-Eppendorf (UKE), Department of Oral and Maxillofacial Surgery, Martinistraße 52, D-20246 Hamburg, Germany
| | - Andreas S. Braeuer
- Technische Universität Bergakademie Freiberg (TUBAF), Institute of Thermal-, Environmental- and Resources‘ Process Engineering (ITUN), Leipziger Straße 28, D-09599 Freiberg, Germany
| | - Christian Knipfer
- University Medical Center Hamburg-Eppendorf (UKE), Department of Oral and Maxillofacial Surgery, Martinistraße 52, D-20246 Hamburg, Germany
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Nogueira MS, Maryam S, Amissah M, Lu H, Lynch N, Killeen S, O’Riordain M, Andersson-Engels S. Evaluation of wavelength ranges and tissue depth probed by diffuse reflectance spectroscopy for colorectal cancer detection. Sci Rep 2021; 11:798. [PMID: 33436684 PMCID: PMC7804163 DOI: 10.1038/s41598-020-79517-2] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Accepted: 12/04/2020] [Indexed: 01/29/2023] Open
Abstract
Colorectal cancer (CRC) is the third most common type of cancer worldwide and the second most deadly. Recent research efforts have focused on developing non-invasive techniques for CRC detection. In this study, we evaluated the diagnostic capabilities of diffuse reflectance spectroscopy (DRS) for CRC detection by building 6 classification models based on support vector machines (SVMs). Our dataset consists of 2889 diffuse reflectance spectra collected from freshly excised ex vivo tissues of 47 patients over wavelengths ranging from 350 and 1919 nm with source-detector distances of 630-µm and 2500-µm to probe different depths. Quadratic SVMs were used and performance was evaluated using twofold cross-validation on 10 iterations of randomized training and test sets. We achieved (93.5 ± 2.4)% sensitivity, (94.0 ± 1.7)% specificity AUC by probing the superficial colorectal tissue and (96.1 ± 1.8)% sensitivity, (95.7 ± 0.6)% specificity AUC by sampling deeper tissue layers. To the best of our knowledge, this is the first DRS study to investigate the potential of probing deeper tissue layers using larger SDD probes for CRC detection in the luminal wall. The data analysis showed that using a broader spectrum and longer near-infrared wavelengths can improve the diagnostic accuracy of CRC as well as probing deeper tissue layers.
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Affiliation(s)
- Marcelo Saito Nogueira
- grid.7872.a0000000123318773Tyndall National Institute, Lee Maltings, Dyke Parade, Cork, Ireland ,grid.7872.a0000000123318773Department of Physics, University College Cork, College Road, Cork, Ireland
| | - Siddra Maryam
- grid.7872.a0000000123318773Tyndall National Institute, Lee Maltings, Dyke Parade, Cork, Ireland ,grid.7872.a0000000123318773Department of Physics, University College Cork, College Road, Cork, Ireland
| | - Michael Amissah
- grid.7872.a0000000123318773Tyndall National Institute, Lee Maltings, Dyke Parade, Cork, Ireland ,grid.7872.a0000000123318773Department of Physics, University College Cork, College Road, Cork, Ireland
| | - Huihui Lu
- grid.7872.a0000000123318773Tyndall National Institute, Lee Maltings, Dyke Parade, Cork, Ireland
| | - Noel Lynch
- grid.411785.e0000 0004 0575 9497Department of Surgery, Mercy University Hospital, Cork, Ireland
| | - Shane Killeen
- grid.411785.e0000 0004 0575 9497Department of Surgery, Mercy University Hospital, Cork, Ireland
| | - Micheal O’Riordain
- grid.411785.e0000 0004 0575 9497Department of Surgery, Mercy University Hospital, Cork, Ireland
| | - Stefan Andersson-Engels
- grid.7872.a0000000123318773Tyndall National Institute, Lee Maltings, Dyke Parade, Cork, Ireland ,grid.7872.a0000000123318773Department of Physics, University College Cork, College Road, Cork, Ireland
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13
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Lindenberg M, Retèl V, van Til J, Kuhlmann K, Ruers T, van Harten W. Selecting Image-Guided Surgical Technologies in Oncology: A Surgeon's Perspective. J Surg Res 2020; 257:333-343. [PMID: 32892128 DOI: 10.1016/j.jss.2020.08.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Revised: 07/23/2020] [Accepted: 08/02/2020] [Indexed: 10/23/2022]
Abstract
BACKGROUND To improve surgical performance, image-guided (IG) technologies are increasingly introduced. Yet, it is unknown which oncological procedures yield most value from these technologies. This study aimed to select the most promising IG technology per oncologic indication. METHODS An Analytic Hierarchical Process was used to evaluate three IG technologies: navigation, optical imaging, and augmented reality, in five oncologic indications compared with usual care. Sixteen decision criteria were selected. The relative importance of the criteria and the expected performance of the technologies were evaluated among surgeons. The combination of these scores gives the expected value per technology. RESULTS On criteria level, sparing critical tissue (9%-18%) and reducing the risk of local recurrence (11%-27%) were most important. Navigation was preferred in three indications-removal of lymph nodes (42%), liver (47%), and rectal tumors (33%). In removing rectal tumors, optical imaging was equally preferred (34%). In removing breast and tongue tumors, no technology was clearly preferred. CONCLUSIONS In selecting IG technologies, especially optical and navigation technologies are expected to add value in addition to usual care. Further development of those technologies for the preferred indications seems valuable. Multi-attribute analysis showed to be useful in prioritization of conducting clinical studies and steer research and development initiatives.
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Affiliation(s)
- Melanie Lindenberg
- Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute-Antoni van Leeuwenhoek, Amsterdam, the Netherlands; Department of Health Technology and Services Research, University of Twente, Enschede, the Netherlands
| | - Valesca Retèl
- Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute-Antoni van Leeuwenhoek, Amsterdam, the Netherlands; Department of Health Technology and Services Research, University of Twente, Enschede, the Netherlands
| | - Janine van Til
- Department of Health Technology and Services Research, University of Twente, Enschede, the Netherlands
| | - Koert Kuhlmann
- Division of Surgical Oncology, The Netherlands Cancer Institute-Antoni van Leeuwenhoek, Amsterdam, the Netherlands
| | - Theo Ruers
- Division of Surgical Oncology, The Netherlands Cancer Institute-Antoni van Leeuwenhoek, Amsterdam, the Netherlands
| | - Wim van Harten
- Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute-Antoni van Leeuwenhoek, Amsterdam, the Netherlands; Department of Health Technology and Services Research, University of Twente, Enschede, the Netherlands.
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14
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Optical percutaneous needle biopsy of the liver: a pilot animal and clinical study. Sci Rep 2020; 10:14200. [PMID: 32848190 PMCID: PMC7449966 DOI: 10.1038/s41598-020-71089-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Accepted: 08/10/2020] [Indexed: 12/15/2022] Open
Abstract
This paper presents the results of the experiments which were performed using the optical biopsy system specially developed for in vivo tissue classification during the percutaneous needle biopsy (PNB) of the liver. The proposed system includes an optical probe of small diameter acceptable for use in the PNB of the liver. The results of the feasibility studies and actual tests on laboratory mice with inoculated hepatocellular carcinoma and in clinical conditions on patients with liver tumors are presented and discussed. Monte Carlo simulations were carried out to assess the diagnostic volume and to trace the sensing depth. Fluorescence and diffuse reflectance spectroscopy measurements were used to monitor metabolic and morphological changes in tissues. The tissue oxygen saturation was evaluated using a recently developed approach to neural network fitting of diffuse reflectance spectra. The Support Vector Machine Classification was applied to identify intact liver and tumor tissues. Analysis of the obtained results shows the high sensitivity and specificity of the proposed multimodal method. This approach allows to obtain information before the tissue sample is taken, which makes it possible to significantly reduce the number of false-negative biopsies.
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Nikbakth H, Karabiyik M, Akca BI. Ultrawide-bandwidth on-chip spectrometer design using band-pass filters. OPTICS EXPRESS 2020; 28:23003-23011. [PMID: 32752551 DOI: 10.1364/oe.399151] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Accepted: 07/14/2020] [Indexed: 06/11/2023]
Abstract
Here, we present the design and simulation of an ultrawide-bandwidth on-chip spectrometer that can be used in various applications, e.g. spectral tissue sensing. It covers 1200 nm wavelength range (400 nm-1600 nm) with 2 nm spectral resolution. The overall design size is only 3 × 3 cm2. The ultra-wide spectral range is made possible by using novel on-chip band-pass filters for the coarse wavelength division. The fine resolution is provided by the arrayed waveguide gratings. The band-pass filter is formed by using bend waveguides and adiabatic full-couplers. The additional loss caused by the band-pass filter is relatively small. The proposed spectrometer covers entire 400 nm-1600 nm range continuously with low crosstalk values. We envision that this design can be used in several different applications including food safety, agriculture, industrial inspection, optical imaging, and biomedical research.
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