[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-352775-105":59,"doc-detail-352775-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","intelligent-stain-free-histology-on-structural-colorimetric-nanocavities","Intelligent Stain-Free Histology on Structural Colorimetric Nanocavities","","Optical microscopic imaging supports modern pathology, yet conventional hematoxylin-and-eosin (H&E) staining is inconsistent, expensive, time-consuming, and labor-intensive. This study presents planar nanocavities-on-silicon (NOS) for nonstained histological imaging, removing traditional staining and lateral nanopatterning. A ~270 nm silicon nitride layer on silicon yields structural color contrast sensitive to tissue refractive index and thickness. NOS visualization simplifies morphological assessment, reduces per-test costs, and shows strong agreement with H&E for key structures. A deep-learning model trained on 2700+ NOS images achieves >95% accuracy distinguishing cancerous from healthy colorectal tissue.",{"@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":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/intelligent-stain-free-histology-on-structural-colorimetric-nanocavities/352775/",{"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/intelligent-stain-free-histology-on-structural-colorimetric-nanocavities/352775.png","ImageObject",300,407,{"name":92,"@type":93},"Quinn Holloway","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-23","2026-09-22",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",12,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"Why is conventional H&E staining a problem in pathology workflows?","Question",{"text":113,"@type":114},"H&E staining can be inconsistent and is costly, time-consuming, and labor-intensive. Its quality also depends on reagent uniformity and variability, and diagnosis may rely heavily on individual expertise.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How does the proposed NOS approach enable stain-free histological imaging?",{"text":118,"@type":114},"The method uses planar nanocavities-on-silicon with an approximately 270 nm silicon nitride layer on a standard silicon wafer. Structural colors arise from optical interference sensitive to the tissue’s local refractive index and thickness.",{"name":120,"@type":111,"acceptedAnswer":121},"How accurate is the deep-learning model trained on NOS images?",{"text":122,"@type":114},"The deep-learning model trained on over 2700 NOS images achieves greater than 95% accuracy in distinguishing cancerous from healthy colorectal tissues.","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},352775,1790470531,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":34,"category_name":35,"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":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":145,"read_time":146},2336474466712,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","Advanced Science    \n[www.advancedscience.com](www.advancedscience.com)  \n RESEARCH ARTICLE   \nIntelligent Stain-Free Histology on Structural Colorimetric Nanocavities  \nQizhe Chen1   Yifei Ren2  Lijie Hu3 Jiajun Li1  Qiutao He2  Yingfeng Zhu2 Di Wang3  Zunguo Du2  Xiaodong Gu6  \n Yanyan Li4  Wenyue Liang1  Haifeng Hu5  Qiwen Zhan5  Qiaoqiang Gan1, 8   \n Jin Wang1  Han Gao1  Xinhai Wang3  Imed Gallouzi4, 7  Jasmeen Merzaban4, 7  \n1Material Science and Engineering, Physical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia  2 Department of Pathology, Huashan Hospital, Fudan University, Shanghai, China  3 Computer Science, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia  4 Bioscience Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia  5 School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China  6 Department of General Surgery, Huashan Hospital, Fudan University, Shanghai, China  7 KAUST Center of Excellence for Smart Health (KCSH), King Abdullah University of Science and Technology, Thuwal, Saudi Arabia   \n8 Environmental Science and Engineering, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia  \nCorrespondence: Zunguo Du ([duzunguo@fudan.edu.cn](duzunguo@fudan.edu.cn))  Xiaodong Gu (guxiaodong@fudan.edu.cn)  Qiaoqiang Gan (qiaoqiang.gan@kaust.edu.sa)  \nReceived: 29 July 2025  Revised: 2 February 2026  Accepted: 4 February 2026  \nKeywords: AI-assisted diagnosis | on-chip cancer diagnosis | nanocavities-on-silicon (NOS) | stain-free histology | structural color contrast  \nABSTRACT  \nOptical microscopic imaging and analysis are indispensable in modern pathology for providing conclusive evidence in disease diagnosis. However, the current gold standard histological staining techniques, hematoxylin and eosin (H&E), can be inconsistent, costly, time-consuming, and laborious. Although nanophotonic chips offer a promising stain-free approach to revealing the optical features of healthy and cancerous tissue, their clinical adoption is hindered by the significant costs associated with top-down lithography. This study introduces planar nanocavities-on-silicon (NOS) for nonstained histological imaging, eliminating the need for traditional staining and lateral nanopatterns. By depositing an approximately 270-nm-thick silicon nitride layer onto a standard silicon wafer, NOS slides produce vibrant structural colors via optical interference that is sensitive to local refractive index and thickness of tissue sections. This method simplifies the visualization of morphological features using conventional microscopesand reduces raw material and processing costs per test. Comparative imaging analyses between NOS and H&E-stained slides show good agreement in key histological structures. Moreover, a deep-learning model trained on over 2700 NOS images achieves >95% accuracy in distinguishing cancerous from healthy colorectal tissues. These results suggest NOS as a faster, inexpensive, chemicalfree, stain-free platform that enables future AI-assisted screening of colorectal cancer for improved pathological diagnostics.  \n1  Main  \nPathologists have traditionally relied on meticulously examining biological tissue stained with hematoxylin and eosin (H&E) to  \nassess pathological features via morphological characteristics and color distributions [1] . While foundational, the efficacy of this method is significantly influenced by the consistency of the staining process and the expertise of the pathologist. The  \n\n| Qizhe Chen, Yifei Ren, Lijie Hu, and Yanyan Li contributed equally to this work.\u003Cbr>|\n| --- |\n| This is an open access article under the terms of the Cr","cbCaii29PtfeBQjL","https://ap.wps.com/l/cbCaii29PtfeBQjL","pdf",7636961,13,"English","# Abstract\n## Background: limitations of H&E staining\n## Proposed method: planar NOS stain-free histology\n## Imaging mechanism and practical workflow\n## Comparative results vs. H&E\n## AI model for colorectal cancer classification","[{\"question\":\"Why is conventional H\\u0026E staining a problem in pathology workflows?\",\"answer\":\"H\\u0026E staining can be inconsistent and is costly, time-consuming, and labor-intensive. Its quality also depends on reagent uniformity and variability, and diagnosis may rely heavily on individual expertise.\"},{\"question\":\"How does the proposed NOS approach enable stain-free histological imaging?\",\"answer\":\"The method uses planar nanocavities-on-silicon with an approximately 270 nm silicon nitride layer on a standard silicon wafer. Structural colors arise from optical interference sensitive to the tissue’s local refractive index and thickness.\"},{\"question\":\"How accurate is the deep-learning model trained on NOS images?\",\"answer\":\"The deep-learning model trained on over 2700 NOS images achieves greater than 95% accuracy in distinguishing cancerous from healthy colorectal tissues.\"}]","Intelligent Stain-Free Histology on Structural Colorimetric Nanocavities | PDF",1790101344,33]