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Training used archival focused microscopic view images (2015–2018) with annotated invasive cancer areas, followed by temporal validation on a later cohort (2019–2021). Tile-based classification models achieved strong detection performance, with basal cell carcinoma showing high median average precision and area under the receiver operating curve at multiple magnifications.",{"@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/development-and-validation-of-a-deep-learning-model-for-improving-detection-of-nonmelanoma-skin-cancers-in-mohs-frozen-sections-treated-with-mohs-micrographic-surgery-key-findings/378502/",{"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/development-and-validation-of-a-deep-learning-model-for-improving-detection-of-nonmelanoma-skin-cancers-in-mohs-frozen-sections-treated-with-mohs-micrographic-surgery-key-findings/378502.png","ImageObject",300,407,{"name":92,"@type":93},"Fans","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-09-24",true,{"@type":102,"interactionType":103,"userInteractionCount":24},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What clinical problem does the study address in Mohs surgery?","Question",{"text":112,"@type":113},"The study targets the need for reliable, low-cost techniques to corroborate cancerous regions of interest and surgical margin proximity during real-time review of frozen sections.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were the deep learning models developed and validated?",{"text":117,"@type":113},"Models were trained on archival focused microscopic view images (2015–2018) with annotated invasive nonmelanoma skin cancer regions, then validated on prospectively collected images in a temporal cohort (2019–2021).",{"name":119,"@type":110,"acceptedAnswer":120},"What performance results were reported for basal cell carcinoma and invasive squamous cell carcinoma?",{"text":121,"@type":113},"For basal cell carcinoma, the best models achieved a median average precision of 0.966 and median area under the receiver operating curve of 0.889 at 100x magnification. For invasive squamous cell carcinomas, high median average precision of 0.904 was reported at 100x magnification.","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},378502,1790840535,{"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":24,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":44,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":144},5909892330395,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","ORIGINAL ARTICLE  \nDevelopment and validation of a deep learning model for improving detection of nonmelanoma skin cancers treated  \nwith Mohs micrographic surgery  \nEugene Tan, MBChB,a ,b ,c Sophie Lim, MBBS,c Duncan Lamont, MMed,d Richard Epstein, MBBS, PhD,e  \nDavid Lim, MBChB,b and Frank P. Y. Lin, MBChB, PhDe ,f,g  \nBackground: Real-time review of frozen sections underpins the quality of Mohs surgery. There is an unmet need for low-cost techniques that can improve Mohs surgery by reliably corroborating cancerous regions of interest and surgical margin proximity.  \nObjective: To test that deep learning models can identify nonmelanoma skin cancer regions in Mohs frozen section specimens.  \nMethods: Deep learning models were developed on archival images of focused microscopic views (FMVs) containing regions of annotated, invasive nonmelanoma skin cancer between 2015 and 2018, then validated on prospectively collected images in a temporal cohort (2019-2021) .  \nResults: The tile-based classification models were derived using 1423 focused microscopic view images from 154 patients and tested on 374 images from 66 patients. The best models detected basal cell carcinomas with a median average precision of 0.966 and median area under the receiver operating curve of 0 .889 at 100x magnification (0 .943 and 0 .922 at 40x magnification) . For invasive squamous cell carcinomas, high median average precision of 0 .904 was achieved at 100x magnification.  \nLimitations: Single institution study with limited cases of squamous cell carcinoma and rare nonmelanoma skin cancer.  \nConclusion: Deep learning appears highly accurate for detecting skin cancers in Mohs frozen sections, supporting its potential for enhancing surgical margin control and increasing operational efficiency. ( JAAD Int 2024;14:39-47.)  \nKey words: artificial intelligence; basal cell carcinoma; deep learning; digital pathology; Mohs micrographic surgery; squamous cell carcinoma.  \nFrom the Western Skin Institute, Melbourne, Australiaa; Skintel, Auckland, New Zealandb; Alfred Health, Melbourne, Australiac; Department of Pathology, Waikato Hospital, Hamilton, New Zealandd; School of Medicine, University of New South Wales, Sydney, Australiae; Kinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, Sydney, Australiaf; and NHMRC Clinical Trials Centre, University of Sydney, Camperdown, NSW, Australia.g  \nFunding sources: Dr Lin is supported by an Australian Medical Research Future Fund as part of the Rapid Applied Research Translation initiative (RARUR000125), a Capacity Building Grant funded by Cancer Institute NSW (2021/CBG003), and generous support from the philanthropic funds from the Wolf family (St. Vincent’s Curran Foundation) .  \nPrior presentation: Abstracts of this work have previously been presented at the Annual Meetings of the Australian College of Dermatologists in 2022 and 2023, the 2022 annual meeting of  \nthe New Zealand Dermatological Society Inc, the 25th World Congress of Dermatology held in Singapore in 2023, and the New South Wales Cancer Conference in Sydney in 2022 .  \nPatient consent: Not applicable.  \nIRB approval status: This study for diagnostic model development has approval from the St. Vincent’s Hospital Human Research Ethics Committee (2021/ETH00647) .  \nAccepted for publication October 29, 2023 .  \nCorrespondence to: Eugene Tan, MBChB, Department of Dermatology, Skintel, 11 Apollo Dr, Rosedale, Auckland, 0632, New Zealand. E-mail: [eugene@skintel.co.nz](eugene@skintel.co.nz).  \n2666-3287  \n􀀁 2023 by the American Academy of Dermatology, Inc. Published by Elsevier Inc. This is an open access article under the CC BY license ([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)).  \n[https://doi.org/10.1016/j.jdin.2023.10.007](https://doi.org/10.1016/j.jdin.2023.10.007)  \n40 Tan et al JAAD INT MARCH 2024  \nINTRODUCTION  \nMohs micrographic surgery (MMS) is the recommended procedure for local high ","cbCaimL74XJBqECG","https://ap.wps.com/l/cbCaimL74XJBqECG","pdf",1866179,"English","# Background\n# Objective\n# Methods\n# Results\n# Limitations\n# Conclusion\n# Key words","[{\"question\":\"What clinical problem does the study address in Mohs surgery?\",\"answer\":\"The study targets the need for reliable, low-cost techniques to corroborate cancerous regions of interest and surgical margin proximity during real-time review of frozen sections.\"},{\"question\":\"How were the deep learning models developed and validated?\",\"answer\":\"Models were trained on archival focused microscopic view images (2015–2018) with annotated invasive nonmelanoma skin cancer regions, then validated on prospectively collected images in a temporal cohort (2019–2021).\"},{\"question\":\"What performance results were reported for basal cell carcinoma and invasive squamous cell carcinoma?\",\"answer\":\"For basal cell carcinoma, the best models achieved a median average precision of 0.966 and median area under the receiver operating curve of 0.889 at 100x magnification. For invasive squamous cell carcinomas, high median average precision of 0.904 was reported at 100x magnification.\"}]","Development and validation of a deep learning model for improving detection of nonmelanoma skin cancers in Mohs frozen sections treated with Mohs micrographic surgery - Key findings | PDF",1790230350,23]