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The study analyzes clinical data, OCT images, and immune markers in patients undergoing radical oral cancer resection, first deriving an immune-indicator-guided deep learning feature representation and then building a multi-view prognostic radiomics model using feature selection and logistic modeling. Quantified OCT features evaluate prognostic contributions of each indicator.",{"@graph":69,"@context":122},[70,84,105],{"@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/deep-radiomics-based-prognostic-prediction-of-oral-cancer-using-optical-coherence-tomography/332489/",{"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/deep-radiomics-based-prognostic-prediction-of-oral-cancer-using-optical-coherence-tomography/332489.png","ImageObject",300,407,{"name":92,"@type":93},"Levi","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-31","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":44},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the main goal of the study?","Question",{"text":112,"@type":113},"To predict oral cancer prognosis by integrating optical coherence tomography (OCT) with peripheral blood immune indicators using artificial intelligence.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were OCT and immune indicators modeled?",{"text":117,"@type":113},"The work builds an immune-indicator-guided deep learning feature representation for OCT images, then integrates a multi-view prognostic radiomics model using feature selection and logistic modeling.",{"name":119,"@type":110,"acceptedAnswer":120},"What key performance results were reported?",{"text":121,"@type":113},"The prognostic model achieved an AUC of 0.886, and the deep learning model classified SII risk with 85.26% accuracy and 0.86 AUC.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},332489,1790031922,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":44,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"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":145,"preview_page_count":4},7971461740909,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","Yuan et al. BMCOral Health (2024) 24:1117 BMC Oral Health  \n[https://doi.org/10.1186/s12903-024-04849-8](https://doi.org/10.1186/s12903-024-04849-8)  \nRESEARCH Open Access  \nDeep radiomics-based prognostic prediction  of oral cancer using optical coherence tomography  \nWei Yuan 1, Jiayi Rao 1, Yanbin Liu2, Sen Li3, Lizheng Qin 1* and Xin Huang 1*  \nAbstract  \nBackground This study aims to evaluate the integration of optical coherence tomography (OCT) and peripheral blood immune indicators for predicting oral cancer prognosis by artificial intelligence.  \nMethods In this study, we examined patients undergoing radical oral cancer resection and explored inherent relationships among clinical data, OCT images, and peripheral immune indicators for oral cancer prognosis. We firstly built a peripheral blood immune indicator-guided deep learning feature representation method for OCT images, and further integrated a multi-view prognostic radiomics model incorporating feature selection and logistic modeling. Thus, we can assess the prognostic impact of each indicator on oral cancer by quantifying OCT features.  \nResults We collected 289 oral mucosal samples from 68 patients, yielding 1,445 OCT images. Using our deep radiomics-based prognosis model, it achieved excellent discrimination for oral cancer prognosis with the area under the receiver operating characteristic curve (AUC) of 0 . 886, identifying systemic immune-inflammation index (SII) asthe most informative feature for prognosis prediction. Additionally, the deep learning model also performed excellent results with 85 . 26% accuracy and 0.86 AUC in classifying the SII risk.  \nConclusions Our study effectively merged OCT imaging with peripheral blood immune indicators to create a deep learning-based model for inflammatory risk prediction in oral cancer. Additionally, we constructed a comprehensive multi-view radiomics model that utilizes deep learning features for accurate prognosis prediction. The study highlighted the significance of the SII as a crucial indicator for evaluating patient outcomes, corroborating our clinical statistical analyses. This integration underscores the potential of combining imaging and blood indicators in clinical decision-making.  \nTrial registration The clinical trial associated with this study was prospectively registered in the Chinese Clinical Trial Registry with the trial registration number (TRN) ChiCTR2200064861 . The registration was completed on 2021. Keywords Optical coherence tomography, Oral cancer, Prognostic prediction, Deep learning, Peripheral blood immune indicators  \n*Correspondence: Lizheng Qin [qinlizheng@ccmu.edu.cn](qinlizheng@ccmu.edu.cn)[ ](qinlizheng@ccmu.edu.cn)Xin Huang [huangxin@ccmu.edu.cn](huangxin@ccmu.edu.cn)  \n1Department of Oral and Maxillofacial & Head and Neck Oncology, Beijing Stomatological Hospital, Capital Medical University, Beijing 100050, China 2Department of Dental Implant Center, Beijing Stomatological Hospital, Capital Medical University, Beijing 100050, China  \n3School of Science, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, Guangdong, China  \n© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceed","cbCaig9j4DUcmFHY","https://ap.wps.com/l/cbCaig9j4DUcmFHY","pdf",2451569,14,"English","# Abstract\n## Background\n## Methods\n## Results\n## Conclusions\n## Trial registration\n## Keywords\n# Background\n## Overview of oral cancer\n## Inflammation and immune indicators in cancer prognosis","[{\"question\":\"What is the main goal of the study?\",\"answer\":\"To predict oral cancer prognosis by integrating optical coherence tomography (OCT) with peripheral blood immune indicators using artificial intelligence.\"},{\"question\":\"How were OCT and immune indicators modeled?\",\"answer\":\"The work builds an immune-indicator-guided deep learning feature representation for OCT images, then integrates a multi-view prognostic radiomics model using feature selection and logistic modeling.\"},{\"question\":\"What key performance results were reported?\",\"answer\":\"The prognostic model achieved an AUC of 0.886, and the deep learning model classified SII risk with 85.26% accuracy and 0.86 AUC.\"}]","Deep radiomics-based prognostic prediction of oral cancer using optical coherence tomography | PDF",1789998446,35]