[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123766-en":3,"doc-seo-123766-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123766,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Enhancing intraoperative tumor delineation with multispectral short-wave infrared fluorescence imaging and machine learning","Fluorescence-guided surgery (FGS) enables real-time visualization of tumors, yet fluorescence intensity measurements can be error-prone. Multispectral short-wave infrared (SWIR) imaging can improve tumor delineation by supporting machine-learning classification of pixels from their spectral signatures. A multispectral SWIR imaging device with six spectral filters was built and used with DinutuximabIRDye800 in neuroblastoma xenografts. Image cubes spanning ~850–1450 nm were classified using multiple learning methods, yielding robust pixel-level discrimination.","Enhancing intraoperative tumor delineation with multispectral short-wave infrared fluorescence imaging and machine learning  \nDale J. Waterhouse ,a,* Laura Privitera,a,b John Anderson,b  \nDanail Stoyanov ,a and Stefano Giuliania,b,c  \naUniversity College London, Wellcome, EPSRC Centre for Interventional and Surgical Sciences,  \nLondon, United Kingdom  \nbUCL Great Ormond Street Institute of Child Health, Cancer Section,  \nDevelopmental Biology and Cancer Programme, London, United Kingdom cGreat Ormond Street Hospital for Children NHS Trust, Department of Specialist Neonatal and  \nPaediatric Surgery, London, United Kingdom  \nAbstract  \nSignificance: Fluorescence-guided surgery (FGS) provides specific real-time visualization of tumors, but intensity-based measurement of fluorescence is prone to errors. Multispectral imaging (MSI) in the short-wave infrared (SWIR) has the potential to improve tumor delineation by enabling machine-learning classification of pixels based on their spectral characteristics.  \nAim: Determine whether MSI can be applied to FGS and combined with machine learning to provide a robust method for tumor visualization.  \nApproach: A multispectral SWIR fluorescence imaging device capable of collecting data from six spectral filters was constructed and deployed on neuroblastoma (NB) subcutaneous xenografts (n ¼ 6) after the injection of a NB-specific NIR-I fluorescent probe (DinutuximabIRDye800) . We constructed image cubes representing fluorescence collected from ∼850 to 1450 nm and compared the performance of seven learning-based methods for pixel-by-pixel classification, including linear discriminant analysis, k-nearest neighbor classification, anda neural network.  \nResults: The spectra of tumor and non-tumor tissue were subtly different and conserved between individuals. In classification, a combine principal component analysis and k-nearest-neighbor approach with area under curve normalization performed best, achieving 97.5% per-pixel classification accuracy (97.1%, 93.5%, and 99.2% for tumor, non-tumor tissue and background, respectively) .  \nConclusions: The development of dozens of new imaging agents provides a timely opportunity for multispectral SWIR imaging to revolutionize next-generation FGS.  \n© The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. [DOI: 10.1117/1.JBO.28.9.094804]  \nKeywords: short-wave infrared; fluorescence-guided surgery; multispectral; machine-learning; cancer; neuroblastoma.  \nPaper 220227SSR received Oct. 6, 2022; accepted for publication Mar. 6, 2023; published online Mar. 27, 2023 .  \n1 Introduction  \nDespite significant improvements in diagnosis and treatment, cancer remains the second leading cause of death worldwide (9 .6 million∕year) .1 Surgical removal of the tumor is used in 45% of cancer treatments.2 Fluorescence-guided surgery (FGS) provides real-time visualization of tumors with molecular specificity by targeting tumor-associated molecules with fluorescently  \n*Address all correspondence to Dale J. Waterhouse, [d.waterhouse@ucl.ac.uk](d.waterhouse@ucl.ac.uk)  \nJournal of Biomedical Optics 094804-1 September 2023 • Vol. 28(9)  \nDownloaded From: [https://www.spiedigitallibrary.org/journals/Journal-of-Biomedical-Optics on](https://www.spiedigitallibrary.org/journals/Journal-of-Biomedical-Optics on) 04 Apr 2023  \nTerms of Use: [https://www.spiedigitallibrary.org/terms-of-use](https://www.spiedigitallibrary.org/terms-of-use)  \nWaterhouse et al.: Enhancing intraoperative tumor delineation with multispectral short-wave infrared . . .  \nFig. 1 Rationale for the present study. (a) Schematic of the main external factors that can result ina multiplicative change of the measured fluorescence signal (exposure factors). (b) Thresholds are often applied to define tumor versus non-tumor. Visual representat","cbCait9vrA9vjK7n","https://ap.wps.com/l/cbCait9vrA9vjK7n","pdf",6995784,1,14,"English","en",105,"# Abstract\n## Significance\n## Aim\n## Approach\n## Results\n## Conclusions\n# Keywords\n# Introduction\n## Fluorescence-guided surgery and current limitations\n## Biological windows and rationale for SWIR","[{\"question\":\"Why can intensity-based fluorescence measurements lead to errors in FGS?\",\"answer\":\"Fluorescence intensity alone is sensitive to exposure and measurement factors, which can distort thresholds used to separate tumor from non-tumor tissue.\"},{\"question\":\"How does multispectral SWIR imaging help tumor delineation?\",\"answer\":\"It enables machine-learning classification of pixels based on spectral characteristics, leveraging subtle but conserved spectral differences between tumor and non-tumor tissue.\"},{\"question\":\"What device and probe were used in the study?\",\"answer\":\"A multispectral SWIR imaging device collecting data from six spectral filters was used with DinutuximabIRDye800, a neuroblastoma-specific NIR-I fluorescent probe.\"}]","Enhancing intraoperative tumor delineation with multispectral short-wave infrared fluorescence imaging and machine learning | 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can intensity-based fluorescence measurements lead to errors in FGS?","Question",{"text":75,"@type":76},"Fluorescence intensity alone is sensitive to exposure and measurement factors, which can distort thresholds used to separate tumor from non-tumor tissue.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does multispectral SWIR imaging help tumor delineation?",{"text":80,"@type":76},"It enables machine-learning classification of pixels based on spectral characteristics, leveraging subtle but conserved spectral differences between tumor and non-tumor tissue.",{"name":82,"@type":73,"acceptedAnswer":83},"What device and probe were used in the study?",{"text":84,"@type":76},"A multispectral SWIR imaging device collecting data from six spectral filters was used with DinutuximabIRDye800, a neuroblastoma-specific NIR-I fluorescent 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