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Chemical imaging such as stimulated Raman scattering (SRS) can directly capture molecular composition without extensive processing, while AI can reconstruct diagnostic-quality views. This work integrates SRS microscopy with a deep learning pipeline to rapidly generate virtual H&E-like images from minimally processed thick fresh-frozen prostate tissue, producing archival-quality outputs comparable to hours-to-days FFPE workflows, supporting pathologist decision-making and grade agreement.",{"@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":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/accelerating-cancer-histopathology-workflows-with-chemical-imaging-and-machine-learning/381678/",{"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/accelerating-cancer-histopathology-workflows-with-chemical-imaging-and-machine-learning/381678.png","ImageObject",300,407,{"name":92,"@type":93},"Terk","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-25","2026-09-24",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does this research aim to solve in cancer histopathology workflows?","Question",{"text":112,"@type":113},"The study targets the complexity and long turnaround time of traditional workflows that require fixation, thin sectioning, staining, and optical imaging before diagnosis.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the approach generate virtual H&E-like images without conventional thin sections?",{"text":117,"@type":113},"It integrates SRS chemical imaging to record data from intact thick fresh-frozen prostate tissue and uses a deep learning processing pipeline to synthesize virtual H&E images from multiple planes.",{"name":119,"@type":110,"acceptedAnswer":120},"What was evaluated to confirm diagnostic usefulness of the virtual images?",{"text":121,"@type":113},"Image quality was assessed from the perspective of enabling pathologists to make decisions, including evaluation of prostate cancer grade usefulness and interpathologist agreement.","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},381678,1790725068,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":34,"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},1099525198933,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","RESEARCH ARTICLE  [https://doi.org/10.1158/2767-9764.CRC-23-0226](https://doi.org/10.1158/2767-9764.CRC-23-0226)  OPEN ACCESS  \nAccelerating Cancer Histopathology Workflows with Chemical Imaging and Machine Learning  \nKianoush Falahkheirkhah1 , 2 ,, Sudipta S. Mukherjee1 ,, Sounak Gupta3 ,  \nLoren Herrera-Hernandez3 , Michael R. McCarthy3 , Rafael E. Jimenez3 , John C. Cheville3 , and Rohit Bhargava1 , 2 , 4 , 5 , 6 , 7  \nCheck for updates  \nABSTRACT  \nHistopathology has remained a cornerstone for biomedical tissue assessment for over a century, with a resource-intensive workflow involving biopsy or excision, gross examination, sampling, tissue processing to snap frozen or formalin-fixed paraffin-embedded blocks, sectioning, staining, optical imaging, and microscopic assessment. Emerging chemical imaging approaches, including stimulated Raman scattering (SRS) microscopy, can directly measure inherent molecular composition in tissue (thereby dispensing with the need for tissue processing, sectioning, and using dyes) and can use artificial intelligence (AI) algorithms to provide high-quality images. Here we show the integration of SRS microscopy in a pathology workflow to rapidly record chemical information from minimally processed fresh-frozen prostate tissue. Instead of using thin sections, we record data from intact thick tissues and use optical sectioning to generate images from multiple planes. We use a deep learning–based processing pipeline to generate virtual hematoxylin and eosin images. Next, we extend  \nthe computational method to generate archival-quality images in minutes, which are equivalent to those obtained from hours/days-long formalinfixed, paraffin-embedded processing. We assessed the quality of images from the perspective of enabling pathologists to make decisions, demonstrating that the virtual stained image quality was diagnostically useful and the interpathologist agreement on prostate cancer grade was not impacted. Finally, because this method does not wash away lipids and small molecules, we assessed the utility of lipid chemical composition in determining grade. Together, the combination of chemical imaging and AI provides novel capabilities for rapid assessments in pathology by reducing the complexity and burden of current workflows.  \nSignificance: Archival-quality (formalin-fixed paraffin-embedded), thinsection diagnostic images are obtained from thick-cut, fresh-frozen prostate tissues without dyes or stains to expedite cancer histopathology by combining SRS microscopy and machine learning.  \nIntroduction  \nThe identification and assessment of prostatic carcinoma is based on characteristic histomorphologic features observed by optical microscopy within  \n1 Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, Illinois. 2 Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, Illinois. 3 Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota. 4 Department of Chemical and Biomolecular Engineering, University of Illinois Urbana-Champaign, Urbana, Illinois. 5 Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, Illinois. 6 Mechanical Science and Engineering, University of Illinois Urbana-Champaign, Urbana, Illinois. 7 Cancer Center at Illinois, University of Illinois Urbana-Champaign, Urbana, Illinois.  \nK. Falahkheirkhah and S.S. Mukherjee contributed equally to this article.  \nCorresponding Author: Rohit Bhargava, University of Illinois at Urbana-Champaign, 405 N. Mathews Ave., Urbana, IL 61801 .  \nE-mail: [rxb@illinois.edu](rxb@illinois.edu)  \ndoi: 10.1158/2767-9764 .CRC-23-0226  \nThis open access article is distributed under the Creative Commons Attribution 4.0 International (CC BY 4 .0) license.  \n© 2023 The Authors; Published by the American Association for Cancer Research  \nprepared thin sections (∼5 μm, a single cell layer thick), cut onto glass slides prepared f","cbCaig1dDNZe5eqi","https://ap.wps.com/l/cbCaig1dDNZe5eqi","pdf",43049561,13,"English","# Abstract\n# Significance\n# Introduction","[{\"question\":\"What problem does this research aim to solve in cancer histopathology workflows?\",\"answer\":\"The study targets the complexity and long turnaround time of traditional workflows that require fixation, thin sectioning, staining, and optical imaging before diagnosis.\"},{\"question\":\"How does the approach generate virtual H\\u0026E-like images without conventional thin sections?\",\"answer\":\"It integrates SRS chemical imaging to record data from intact thick fresh-frozen prostate tissue and uses a deep learning processing pipeline to synthesize virtual H\\u0026E images from multiple planes.\"},{\"question\":\"What was evaluated to confirm diagnostic usefulness of the virtual images?\",\"answer\":\"Image quality was assessed from the perspective of enabling pathologists to make decisions, including evaluation of prostate cancer grade usefulness and interpathologist agreement.\"}]","Accelerating Cancer Histopathology Workflows with Chemical Imaging and Machine Learning | PDF",1790246468,33]