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This study evaluates an artificial intelligence approach that predicts preoperative chemotherapy effects from hematoxylin and eosin (H&E) images of needle biopsy tissues. Unlike single-model pipelines, the method combines three independent models targeting distinct atypia characteristics, achieving 95.15% accuracy on 103 unseen test cases.",{"@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/development-of-multiple-ai-pipelines-that-predict-neoadjuvant-chemotherapy-response-of-breast-cancer-using-he-stained-tissues/377717/",{"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-of-multiple-ai-pipelines-that-predict-neoadjuvant-chemotherapy-response-of-breast-cancer-using-he-stained-tissues/377717.png","ImageObject",300,407,{"name":92,"@type":93},"Emma Wilson","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-03","2026-09-24",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},"What clinical problem does the study address?","Question",{"text":113,"@type":114},"The study targets the lack of clear factors predicting a patient’s sensitivity to neoadjuvant chemotherapy (NAC) in breast cancer beyond tumor subtype.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What data and images are used to make the predictions?",{"text":118,"@type":114},"Predictions are derived from hematoxylin and eosin (H&E) images of pathological tissue obtained from needle biopsies prior to chemotherapy.",{"name":120,"@type":111,"acceptedAnswer":121},"How does the proposed AI pipeline differ from typical approaches?",{"text":122,"@type":114},"Instead of relying on a single model, the system uses three independent models that focus on different characteristics of cancer atypia, including CNN-based structural atypia and SVM/random forest-based nuclear atypia features.","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},377717,1790579427,{"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},3848291630094,"https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45","The Journal of Pathology: Clinical Research  \nJ Pathol Clin Res May 2023; 9: 182–194  \nPublished online 10 March 2023 in Wiley Online Library (wi[leyonlinelibrary.com](leyonlinelibrary.com). DOI: 10.1002/cjp2.314)[).](leyonlinelibrary.com). DOI: 10.1002/cjp2.314)[ DOI: 10.1002/cjp2.314](leyonlinelibrary.com). DOI: 10.1002/cjp2.314)  \nORIGINAL ARTICLE  \nDevelopment of multiple AI pipelines that predict neoadjuvant chemotherapy response of breast cancer using H&E-stained tissues  \nBin Shen 1,2 , Akira Saito 1,2, Ai Ueda3, Koji Fujita 1 , Yui Nagamatsu 1 , Mikihiro Hashimoto4, Masaharu Kobayashi4, Aashiq H Mirza5, Hans Peter Graf6, Eric Cosatto6, Shoichi Hazama7, Hiroaki Nagano8, Eiichi Sato9, Jun Matsubayashi 10, Toshitaka Nagao 10, Esther Cheng 11 , Syed AF Hoda 11 , Takashi Ishikawa3 and  \nMasahiko Kuroda 1,2*   \n1 Department of Molecular Pathology, Tokyo Medical University, Shinjuku-ku, Tokyo, Japan  \n2Department of AI Applied Quantitative Clinical Science, Tokyo Medical University, Shinjuku-ku, Tokyo, Japan 3Department of Breast Oncology and Surgery, Tokyo Medical University Hospital, Shinjuku-ku, Tokyo, Japan 4Research and Development Division, Chi Corporation, Shinjuku-ku, Tokyo, Japan  \n5Department of Pharmacology, Weill Cornell Medicine, New York, NY, USA 6Department of Machine Learning, NEC Labs America Inc., Princeton, NJ, USA  \n7Department of Translational Research and Development Therapeutics against Cancer, School of Medicine, Yamaguchi University, Ube, Yamaguchi, Japan  \n8Department of Gastroenterological, Breast and Endocrine Surgery, Graduate School of Medicine, Yamaguchi University, Ube, Yamaguchi, Japan 9Department of Pathology, Institute of Medical Science, Tokyo Medical University, Shinjuku-ku, Tokyo, Japan  \n10Department of Anatomic Pathology, Tokyo Medical University, Shinjuku-ku, Tokyo, Japan  \n11 Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York Presbyterian Hospital, New York, NY, USA  \n*Correspondence to: Masahiko Kuroda, Department of Molecular Pathology, Tokyo Medical University, 6-1-1 Shinjuku, Shinjuku-ku, Tokyo  \n160-8402, Japan. E-mail: [kuroda@tokyo-med.ac.jp](kuroda@tokyo-med.ac.jp)  \nAbstract  \nIn recent years, the treatment of breast cancer has advanced dramatically and neoadjuvant chemotherapy (NAC) has become a common treatment method, especially for locally advanced breast cancer. However, other than the subtype of breast cancer, no clear factor indicating sensitivity to NAC has been identified. In this study, we attempted to use artificial intelligence (AI) to predict the effect of preoperative chemotherapy from hematoxylin and eosin images of pathological tissue obtained from needle biopsies prior to chemotherapy. Application of AI to pathological images typically uses a single machine-learning model such as support vector machines (SVMs) or deep convolutional neural networks (CNNs). However, cancer tissues are extremely diverse and learning with a realistic number of cases limits the prediction accuracy of a single model. In this study, we propose a novel pipeline system that uses three independent models each focusing on different characteristics of cancer atypia. Our system uses a CNN model to learn structural atypia from image patches and SVM and random forest models to learn nuclear atypia from fine-grained nuclear features extracted by image analysis methods. It was able to predict the NAC response with 95.15% accuracy on a test set of 103 unseen cases. We believe that this AI pipeline system will contribute to the adoption of personalized medicine in NAC therapy for breast cancer.  \nKeywords: artificial intelligence; breast cancer; digital pathology; neoadjuvant chemotherapy  \nReceived 25 September 2022; Revised 23 January 2023; Accepted 10 February 2023 No conflicts of interest were declared.  \nIntroduction  \nNeoadjuvant chemotherapy (NAC) is used for locally advanced breast cancer, breast cancer with poor  \nprognosis (triple negative and HER2 positive, ","cbCaimn3R2g9TQZP","https://ap.wps.com/l/cbCaimn3R2g9TQZP","pdf",2429488,13,"English","# Abstract\n## Introduction","[{\"question\":\"What clinical problem does the study address?\",\"answer\":\"The study targets the lack of clear factors predicting a patient’s sensitivity to neoadjuvant chemotherapy (NAC) in breast cancer beyond tumor subtype.\"},{\"question\":\"What data and images are used to make the predictions?\",\"answer\":\"Predictions are derived from hematoxylin and eosin (H\\u0026E) images of pathological tissue obtained from needle biopsies prior to chemotherapy.\"},{\"question\":\"How does the proposed AI pipeline differ from typical approaches?\",\"answer\":\"Instead of relying on a single model, the system uses three independent models that focus on different characteristics of cancer atypia, including CNN-based structural atypia and SVM/random forest-based nuclear atypia features.\"}]","Development of multiple AI pipelines that predict neoadjuvant chemotherapy response of breast cancer using H&E-stained tissues | PDF",1790226828,33]