[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128270-en":3,"doc-seo-128270-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128270,2336475104957,"Seraphina","https://ap-avatar.wpscdn.com/avatar/22000c4c6bd8a5076e1?x-image-process=image/resize,m_fixed,w_180,h_180&k=1787554080175789136",8,"Research & Report","Digital image analysis and machine learning-assisted prediction of neoadjuvant chemotherapy response in triple-negative breast cancer","Pathological complete response (pCR) is linked to better outcomes in triple-negative breast cancer (TNBC), yet only 30–40% of patients receiving neoadjuvant chemotherapy (NAC) achieve pCR, with 60–70% showing residual disease (RD). The study addresses uncertainty about how the tumor microenvironment relates to NAC response. A two-step machine learning pipeline is proposed to classify histological components on H&E whole-slide images and extract spatial features to predict patient-level NAC response.","Fisher et al. Breast Cancer Research (2024) 26:12 [https://doi.org/10.1186/s13058-023-01752-y](https://doi.org/10.1186/s13058-023-01752-y)  \nBreast Cancer Research  \n RESEARCH Open Access  \nDigital image analysis and machine   learning-assisted prediction of neoadjuvant chemotherapy response in triple-negative breast cancer  \nTimothy B. Fisher 1, Geetanjali Saini2, T. S. Rekha3, Jayashree Krishnamurthy3, Shristi Bhattarai2, Grace Callagy4, Mark Webber4, Emiel A. M. Janssen5,7, Jun Kong6* and Ritu Aneja1,2*  \nAbstract  \nBackground Pathological complete response (pCR) is associated with favorable prognosis in patients with triplenegative breast cancer (TNBC) . However, only 30–40% ofTNBC patients treated with neoadjuvant chemotherapy (NAC) show pCR, while the remaining 60–70% show residual disease (RD) . The role of the tumor microenvironment in NAC response in patients with TNBC remains unclear. In this study, we developed a machine learning-based twostep pipeline to distinguish between various histological components in hematoxylin and eosin (H&E)-stained whole slide images (WSIs) ofTNBC tissue biopsies and to identify histological features that can predict NAC response. Methods H&E-stained WSIs of treatment-naïve biopsies from 85 patients (51 with pCR and 34 with RD) of the model development cohort and 79 patients (41 with pCR and 38 with RD) of the validation cohort were separated through a stratified eightfold cross-validation strategy for the first step and leave-one-out cross-validation strategy for the second step. A tile-level histology label prediction pipeline and four machine-learning classifiers were used to analyze 468,043 tiles ofWSIs. The best-trained classifier used 55 texture features from each tile to produce a probability profile during testing. The predicted histology classes were used to generate a histology classification map of the spatial distributions of different tissue regions. A patient-level NAC response prediction pipeline was trained with features derived from paired histology classification maps. The top graph-based features capturing the relevant spatial information across the different histological classes were provided to the radial basis function kernel support vector machine (rbfSVM) classifier for NAC treatment response prediction.  \nResults The tile-level prediction pipeline achieved 86 . 72% accuracy for histology class classification,  \nwhile the patient-level pipeline achieved 83 . 53% NAC response (pCR vs. RD) prediction accuracy of the model development cohort. The model was validated with an independent cohort with tile histology validation accuracy of 83 . 59% and NAC prediction accuracy of 81 . 01% . The histological class pairs with the strongest NAC response predictive ability were tumor and tumor tumor-infiltrating lymphocytes for pCR and microvessel density and polyploid giant cancer cells for RD.  \n*Correspondence: Jun Kong [jkong@gsu.edu](jkong@gsu.edu)[ ](jkong@gsu.edu)Ritu Aneja[raneja@uab.edu](raneja@uab.edu)  \nFull list of author information is available at the end of the article  \n© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, 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 changes were made. 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 exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licens","cbCaijOeNOlFFvGN","https://ap.wps.com/l/cbCaijOeNOlFFvGN","pdf",1960512,3,1,13,"English","en",105,"# Abstract\n## Background\n## Methods\n## Results\n## Conclusion\n# Introduction","[{\"question\":\"What clinical problem does the study target in TNBC patients receiving NAC?\",\"answer\":\"The study targets the limited proportion of TNBC patients who achieve pathological complete response (pCR) after NAC and the need for better prediction of treatment response among those who instead develop residual disease (RD).\"},{\"question\":\"How does the proposed pipeline analyze whole-slide images?\",\"answer\":\"It uses a two-step machine learning approach: a tile-level histology classification pipeline for H\\u0026E whole-slide images, followed by a patient-level response prediction pipeline that leverages features derived from paired histology classification maps and spatial information across tissue regions.\"},{\"question\":\"What were the main performance outcomes and the most predictive histological class pairs?\",\"answer\":\"The tile-level histology classification accuracy reached 86.72% in model development, while patient-level NAC response prediction achieved 83.53% accuracy. The strongest predictive class pairs were tumor and tumor-infiltrating lymphocytes for pCR, and microvessel density with polyploid giant cancer cells for RD.\"}]","Digital image analysis and machine learning-assisted prediction of neoadjuvant chemotherapy response in triple-negative breast cancer | PDF",1785946357,33,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"digital-image-analysis-and-machine-learning-assisted-prediction-of-neoadjuvant-chemotherapy-response-in-triple-negative-breast-cancer","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/digital-image-analysis-and-machine-learning-assisted-prediction-of-neoadjuvant-chemotherapy-response-in-triple-negative-breast-cancer/128270/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-27","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What clinical problem does the study target in TNBC patients receiving NAC?","Question",{"text":76,"@type":77},"The study targets the limited proportion of TNBC patients who achieve pathological complete response (pCR) after NAC and the need for better prediction of treatment response among those who instead develop residual disease (RD).","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the proposed pipeline analyze whole-slide images?",{"text":81,"@type":77},"It uses a two-step machine learning approach: a tile-level histology classification pipeline for H&E whole-slide images, followed by a patient-level response prediction pipeline that leverages features derived from paired histology classification maps and spatial information across tissue regions.",{"name":83,"@type":74,"acceptedAnswer":84},"What were the main performance outcomes and the most predictive histological class pairs?",{"text":85,"@type":77},"The tile-level histology classification accuracy reached 86.72% in model development, while patient-level NAC response prediction achieved 83.53% accuracy. The strongest predictive class pairs were tumor and tumor-infiltrating lymphocytes for pCR, and microvessel density with polyploid giant cancer cells for RD.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]