[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-457555-105":3,"detail-sidebar-cat-0-en-105":81,"doc-detail-457555-en":130},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","tumor-shrinkage-patterns-and-optimal-timing-of-response-assessment-during-neoadjuvant-therapy-for-breast-cancer-a-study-based-on-multiparametric-mri","Tumor shrinkage patterns and optimal timing of response assessment during neoadjuvant therapy for breast cancer: a study based on multiparametric MRI","","Objective: Determine tumor shrinkage规律 and identify the optimal time window for predicting rapid tumor shrinkage efficacy during neoadjuvant systemic therapy (NST) in breast cancer using multiparametric MRI (mp-MRI). Methods: Retrospective analysis of 215 patients (221 breasts) with mp-MRI at baseline and after 2, 4, and 6 cycles; tumors were grouped into four shrinkage patterns via latent class trajectory modeling, then predictors were found using multivariate logistic regression and evaluated with ROC/AUC and DeLong tests. Conclusion: The fourth cycle is the critical window for accurate prediction.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/healthcare/","Healthcare",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/tumor-shrinkage-patterns-and-optimal-timing-of-response-assessment-during-neoadjuvant-therapy-for-breast-cancer-a-study-based-on-multiparametric-mri/457555/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/tumor-shrinkage-patterns-and-optimal-timing-of-response-assessment-during-neoadjuvant-therapy-for-breast-cancer-a-study-based-on-multiparametric-mri/457555.png","ImageObject",300,407,{"name":42,"@type":43},"Blitz","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-11-08","2026-09-30",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",8,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What imaging method and time points were used to evaluate tumor shrinkage during NST?","Question",{"text":63,"@type":64},"The study used multiparametric MRI (mp-MRI), acquiring images at baseline and after 2, 4, and 6 treatment cycles.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How were tumors grouped into different shrinkage patterns?",{"text":68,"@type":64},"Tumors were categorized into four shrinkage patterns using latent class trajectory modeling (LCTM) based on changes in maximum tumor diameter on dynamic contrast-enhanced MRI (DCE-MRI).",{"name":70,"@type":61,"acceptedAnswer":71},"Which time window was identified as best for predicting rapid tumor shrinkage?",{"text":72,"@type":64},"The fourth cycle was identified as the critical time window for accurate prediction of rapid shrinkage using multiparametric MRI.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},457555,1791266014,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,110,114,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":108,"slug":109},7,40,"healthcare",{"id":55,"doc_module":4,"doc_module_name":25,"category_name":111,"show_sort_weight":112,"slug":113},"Research & Report",30,"research-report",{"id":115,"doc_module":4,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":97,"slug":129},19,"General","general",{"code":4,"msg":82,"data":131},{"doc_id":79,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":107,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":139,"language":140,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":12,"update_tm":144,"read_time":145,"preview_page_count":4},24464137899374,"https://us-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2","Wang et al. Cancer Imaging (2026) 26:2 [https://doi.org/10.1186/s40644-025-00971-0](https://doi.org/10.1186/s40644-025-00971-0)  \nCancer Imaging  \nRESEARCH Open Access  \nTumor shrinkage patterns and optimal timing  of response assessment during neoadjuvant therapy for breast cancer: a study based on multiparametric MRI  \nJingbo Wang 1,2,3,4†, Yacong Liu5†, Tianhui Liu 1,2,3,4, Yanbo Li 1,2,3,4, Xiaoxu Ma 1,2,3,4, Yishan Zhao 1,2,3,4 and Hong Lu 1,2,3,4*  \nAbstract  \nObjective To investigate the laws of tumor shrinkage and to determine the optimal time window for predicting the treatment efficacy (defined as rapid tumor shrinkage) during neoadjuvant systemic therapy (NST) in breast cancer using multiparametric MRI (mp-MRI) .  \nMethods This retrospective study included 215 breast cancer patients (221 breasts) who underwent complete NSTand received mp-MRI at baseline and after 2, 4, and 6 treatment cycles. Tumors were categorized into four shrinkage patterns using latent class trajectory modeling (LCTM) based on changes in maximum tumor diameter on dynamic contrast-enhanced MRI (DCE-MRI) . Clinical, pathological, and imaging features were analyzed to identify independent predictors of shrinkage rate using multivariate logistic regression. A predictive model was then constructed by combining the independent predictors. Receiver operating characteristic (ROC) curves were plotted to evaluate the model’s performance, and DeLong’s test was used to compare the area under the curve (AUC) between different models to assess predictive efficacy.  \nResults Using LCTM, based on the change patterns of maximum lesion diameter on the first phase of DCE-MRI, all 221 breast cancer cases were classified into four distinct shrinkage types. Multivariate logistic regression analysis identified HER2 positivity, baseline peritumoral edema, DWI isointense signal at NST cycles 2/4/6, a lower early enhancement rate at cycle 6, and imaging features assessed at the fourth cycle—including non-mass enhancement, concentric shrinkage, and higher ADC values—as independent predictors of rapid tumor shrinkage. A combined model incorporating clinicopathological features and MRI characteristics at NST cycle 4 achieved the highest predictive performance (AUC = 0 . 814), which was comparable to that of the model based on cycle 6 imaging features (AUC = 0 . 759, P = 0 . 243) . To determine the best-performing model, predictive models combining clinicopathological and baseline features with NST cycle 4 or cycle 6 imaging features were constructed. The AUCs were 0.835 and 0. 796, respectively, with no significant difference (P = 0 . 319) .  \n†Jingbo Wang andYacong Liu are the primary authors, and they contributed equally to this work.  \n*Correspondence: Hong Lu [honglu@tmu.edu.cn](honglu@tmu.edu.cn)  \nFull list of author information is available at the end of the article  \n© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, 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 you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. 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://creati](http://creati)[vecommons.org/l](vecommons.org/l)icenses/by-nc-nd/4.0/.  \nWang et al. Cancer Imaging (2026) 26","cbCaik3iG8uTXkHQ","https://ap.wps.com/l/cbCaik3iG8uTXkHQ","pdf",2604962,13,"English","# Abstract\n# Introduction\n# Methods\n## Imaging and shrinkage pattern modeling\n## Predictive modeling and evaluation\n# Results\n# Conclusion","[{\"question\":\"What imaging method and time points were used to evaluate tumor shrinkage during NST?\",\"answer\":\"The study used multiparametric MRI (mp-MRI), acquiring images at baseline and after 2, 4, and 6 treatment cycles.\"},{\"question\":\"How were tumors grouped into different shrinkage patterns?\",\"answer\":\"Tumors were categorized into four shrinkage patterns using latent class trajectory modeling (LCTM) based on changes in maximum tumor diameter on dynamic contrast-enhanced MRI (DCE-MRI).\"},{\"question\":\"Which time window was identified as best for predicting rapid tumor shrinkage?\",\"answer\":\"The fourth cycle was identified as the critical time window for accurate prediction of rapid shrinkage using multiparametric MRI.\"}]","Tumor shrinkage patterns and optimal timing of response assessment during neoadjuvant therapy for breast cancer: a study based on multiparametric MRI | PDF",1790749716,33]