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Existing standard clinical oral examinations often cannot reliably separate early lesions from benign findings, so early diagnosis occurs in only about 30% of patients. This study evaluates automated, noninvasive discrimination using multispectral autofluorescence imaging endoscopy with histopathology-guided classification.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/computer-assisted-discrimination-of-cancerous-and-precancerous-from-benign-oral-lesions-based-on-multispectral-autofluorescence-imaging-endoscopy/331518/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/computer-assisted-discrimination-of-cancerous-and-precancerous-from-benign-oral-lesions-based-on-multispectral-autofluorescence-imaging-endoscopy/331518.png","ImageObject",300,407,{"name":92,"@type":93},"Quinn","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-14","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",11,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"Why is early diagnosis of oral cancer challenging with standard clinical examination?","Question",{"text":113,"@type":114},"Precancerous and cancerous oral lesions are often difficult to distinguish from benign oral lesions during routine examination, leading to early diagnosis in only about 30% of patients.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How does the study perform automated discrimination among lesion types?",{"text":118,"@type":114},"Multispectral autofluorescence endoscopic images are acquired in vivo from patients, lesions are classified using histopathology, and multispectral autofluorescence feature maps train and optimize SVM models to separate cancerous/precancerous from benign cases.",{"name":120,"@type":111,"acceptedAnswer":121},"What performance results are reported for the optimized model?",{"text":122,"@type":114},"Using leave-one-patient-out cross-validation, the optimized SVM model with four multispectral autofluorescence features achieves sensitivity of 85%, specificity of 71%, and overall accuracy of 78%.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},331518,1790016180,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":46,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":31,"preview_page_count":4},962075114765,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","Author Manuscript Author Manuscript Author Manuscript Author Manuscript  \n\n| | HHS Public Access\u003Cbr>Author manuscript\u003Cbr>Biophotonics Discov. Author manuscript; available in PMC 2024 November 14. |\n| --- | --- |\n\nPublished in final edited form as:  \nBiophotonics Discov. 2024 July ; 1(2): . doi:10.1117/1.bios.1.2.025001 .  \nComputer-assisted discrimination of cancerous and precancerous from benign oral lesions based on multispectral autofluorescence imaging endoscopy  \nElvis de Jesus Duran Sierraa, Shuna Chenga, Rodrigo Cuencab, Beena Ahmedc, Jim Jid, Vladislav V. Yakovleva, Mathias Martineze, Moustafa Al-Khalile, Hussain Al-Enazif, Carlos Bussog, Javier A. Job,*  \naTexas A&M University, Department of Biomedical Engineering, College Station, Texas, United States  \nb University of Oklahoma, School of Electrical and Computer Engineering, Norman, Oklahoma, United States  \nc University of New South Wales, School of Electrical Engineering and Telecommunications, Sydney, New South Wales, Australia  \ndTexas A&M University at Qatar, Department of Electrical and Computer Engineering, Doha, Qatar  \ne Hamad Medical Corporation, Department of Cranio-Maxillofacial Surgery, Doha, Qatar fHamad Medical Corporation, Department of Otorhinolaryngology Head and Neck Surgery, Doha, Qatar  \ngThe University of Texas at Dallas, School of Electrical and Computer Engineering, Dallas, Texas, United States  \nAbstract  \nSignificance: Diagnosis of cancerous and pre-cancerous oral lesions at early stages is critical for the improvement of patient care, to increase survival rates and minimize the invasiveness of tumor resection surgery. Unfortunately, oral precancerous and early-stage cancerous lesions are often difficult to distinguish from oral benign lesions with the existing diagnostic tools used during standard clinical oral examination. In consequence, early diagnosis of oral cancer can be achieved in only about 30% of patients. Therefore, clinical diagnostic technologies for fast, minimally invasive, and accurate oral cancer screening are urgently needed.  \nPublished by SPIE under a Creative Commons Attribution 4.0 International License.  \n*Address all correspondence to Javier A. Jo, [javierjo@ou.edu](javierjo@ou.edu).  \nDisclosures  \nThe authors declare no conflicts of interest.  \nInstitutional Review Board Statement  \nThe study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board at Hamad Medical Corporation (study 16332/16, approved on 09/13/2017) .  \nCode and Data Availability  \nThe code associated to this article is publicly available at: [https://github.com/eduransierra/Cancer-vs.-Benign-Oral-Lesion](https://github.com/eduransierra/Cancer-vs.-Benign-Oral-Lesion)Classification  \nAuthor Manuscript Author Manuscript Author Manuscript Author Manuscript  \nde Jesus Duran Sierra et al. Page 2  \nAim: This study investigated the use of multispectral autofluorescence imaging endoscopy for the automated and noninvasive discrimination of cancerous and precancerous from benign oral epithelial lesions.  \nApproach: In vivo multispectral autofluorescence endoscopic images of clinically suspicious oral lesions were acquired from 67 patients undergoing tissue biopsy examination. The imaged lesions were classified as precancerous (n = 4), cancerous (n = 29), and benign (n = 34) lesions based on histopathology diagnosis. Multispectral autofluorescence intensity feature maps were generated for each oral lesion and used to train and optimize support vector machine (SVM)  \nmodels for automated discrimination of cancerous and precancerous from benign oral lesions.  \nResults: After a leave-one-patient-out cross-validation strategy, an optimized SVM model developed with four multispectral autofluorescence features yielded levels of sensitivity and specificity of 85% and 71%, respectively and overall accuracy of 78% in the discrimination of cancerous/precancerous versus benign oral lesions.  \nConclusion: Th","cbCaiqb31vDKDELb","https://ap.wps.com/l/cbCaiqb31vDKDELb","pdf",980070,"English","# Abstract\n# Significance\n# Aim\n# Approach\n## Data acquisition and labeling\n## Model training and optimization\n# Results\n# Conclusion\n# Keywords\n# 1 Introduction\n## Need for early detection\n## Limitations of existing diagnostic adjuncts\n## Related optical imaging approaches","[{\"question\":\"Why is early diagnosis of oral cancer challenging with standard clinical examination?\",\"answer\":\"Precancerous and cancerous oral lesions are often difficult to distinguish from benign oral lesions during routine examination, leading to early diagnosis in only about 30% of patients.\"},{\"question\":\"How does the study perform automated discrimination among lesion types?\",\"answer\":\"Multispectral autofluorescence endoscopic images are acquired in vivo from patients, lesions are classified using histopathology, and multispectral autofluorescence feature maps train and optimize SVM models to separate cancerous/precancerous from benign cases.\"},{\"question\":\"What performance results are reported for the optimized model?\",\"answer\":\"Using leave-one-patient-out cross-validation, the optimized SVM model with four multispectral autofluorescence features achieves sensitivity of 85%, specificity of 71%, and overall accuracy of 78%.\"}]","Computer-assisted discrimination of cancerous and precancerous from benign oral lesions based on multispectral autofluorescence imaging endoscopy | PDF",1789993692]