[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84771-en":3,"doc-seo-84771-105":30,"detail-sidebar-cat-0-en-105":91},{"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":13,"seo_description":14,"update_tm":28,"read_time":29},84771,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Graph Representation Learning of Longitudinal Medical Imaging Trajectories for Treatment Response Prediction","In breast cancer, pathological complete response (pCR) serves as a clinically meaningful surrogate for long-term outcomes, yet neoadjuvant chemotherapy response varies widely across patients, making individualized prediction difficult. A imaging-based 3D spatio-temporal framework is introduced that combines a state-of-the-art graph neural network with relational temporal interaction modeling across DCE-MRI timepoints. Three novel complementary self-supervised objectives learn treatment trajectory representations. Experiments on 585 ISPY-2 patients show strong gains over vision and self-supervised baselines and provide ablations, including effects of available timepoints and inter-scan time differences.","arXiv :2607 .049 12v 1 [ cs .CV] 6 Jul 2026  \nGraph Representation Learning of Longitudinal Medical Imaging Trajectories for Treatment Response Prediction  \nJohannes Kiechle 1 ,2 ,3 ,7 , Richard Osuala2 ,4 , Daniel M. Lang3 , Stefan M. Fischer 1 ,2 ,3 ,7 , Ivana Janíčková5 , Karim Lekadir4 , Julia A. Schnabel 1 ,3 ,6 ,7 ,†, and Jan C. Peeken2 ,†  \n1 Technical University of Munich, 2 TUM University Hospital Rechts der Isar, 3 Helmholtz Munich, 4 Universitat de Barcelona, 5 Medical University of Vienna,  \n6 King’s College London, 7 Munich Center for Machine Learning  \nAbstract. In patients with breast cancer, pathological complete response (pCR) has been established as a clinically meaningful surrogate marker for long-term outcomes. While commonly treated with neoadjuvant chemotherapy (NACT), effective treatment decision-making remains challenging, as therapeutic response can vary substantially across patients, calling for predictive models capable of accurately estimating individualized treatment response. To address this, we propose an imaging-based 3D spatio-temporal framework for treatment response prediction that integrates a state-of-the-art graph neural network with relational modeling of temporal interactions across timepoints alongside three novel complementary self-supervised treatment trajectory representation learning objectives. Experiments across a cohort of 585 patients from the public ISPY-2 dataset demonstrate that our method substantially outperforms both vision and self-supervised learning baselines across several classification metrics. Alongside establishing a breast cancer pCR prediction benchmark, we include a principled ablation of our method and further introduce and empirically assess the impact of the available number of DCE-MRI timepoints per patient trajectory and the inclusion of inter-scan time-differences. Overall, our study substantiates the utility of clinically meaningful longitudinal medical imagaging modeling for predicting NACT-induced pCR. We will publicly share our code repository and a user-friendly PyPI library for dataset curation upon publication, effectively promoting reproducible open-source research.  \nKeywords: GNNs · Longitudinal · Self-Supervised · Breast Cancer  \n1 Introduction  \nWith an estimated 2.3 million women diagnosed with breast cancer annually [1], optimizing treatment remains a clinical priority where neoadjuvant chemother-† Shared senior authorship.  \n2 Kiechle et al.  \nFig. 1. Method overview. Longitudinal image-derived latent features are represented as a directed acyclic graph and aggregated through a graph neural network (GNN) projection head to obtain a compact patient-level embedding. Representation learning is guided by an asymmetric response-aware objective that combines (a) population-level alignment,(b) patient-level representation decorrelation, and (c) temporal consistency losses. The overall objective encourages discriminative separation between respondersand non-responders while preserving the temporal structure of disease evolution.  \napy (NACT) crucially enables early response assessment to guide therapy adaptation and surgical planning [2] . Pathologic complete response (pCR), i.e. the absence of residual invasive disease in the breast and axillary lymph nodes at surgery following NACT [2], is commonly used to estimate NACT efficacy while being associated with improved long-term outcomes and event-free survival [3] .  \nReliable early prediction of pCR holds the promise of mitigating treatmentrelated toxicity and crucially supports informed clinical decision-making, including therapeutic escalation or de-escalation strategies. Moreover, early identification of non-responders may enable timely treatment modification and optimization of surgical intervention. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays an essential role in screening and assessment of response to NACT [4] . Accordingly, DCE-MRI–based methods for pCR prediction hav","cbCaiss35lY33yP9","https://ap.wps.com/l/cbCaiss35lY33yP9","pdf",821615,2,1,11,"English","en",105,"# Introduction\n## Problem motivation and clinical need\n## Limitations of existing pCR prediction approaches\n## Related longitudinal modeling work","[{\"question\":\"What clinical problem does the document address?\",\"answer\":\"It targets reliable early prediction of pathological complete response (pCR) after neoadjuvant chemotherapy to support individualized treatment decisions.\"},{\"question\":\"How does the proposed method model longitudinal medical imaging?\",\"answer\":\"It represents longitudinal, image-derived latent features as a directed acyclic graph and aggregates them with a graph neural network, using relational modeling of temporal interactions across timepoints.\"},{\"question\":\"What evidence is used to evaluate performance?\",\"answer\":\"Experiments are conducted on a cohort of 585 patients from the public ISPY-2 dataset, showing improvements over vision and self-supervised learning baselines across multiple classification metrics.\"}]",1784198129,28,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"graph-representation-learning-of-longitudinal-medical-imaging-trajectories-for-treatment-response-prediction","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,47,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":20},"https://docshare.wps.com/document/","Document",{"item":48,"name":12,"@type":43,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/graph-representation-learning-of-longitudinal-medical-imaging-trajectories-for-treatment-response-prediction/84771/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-22","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What clinical problem does the document address?","Question",{"text":75,"@type":76},"It targets reliable early prediction of pathological complete response (pCR) after neoadjuvant chemotherapy to support individualized treatment decisions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed method model longitudinal medical imaging?",{"text":80,"@type":76},"It represents longitudinal, image-derived latent features as a directed acyclic graph and aggregates them with a graph neural network, using relational modeling of temporal interactions across timepoints.",{"name":82,"@type":73,"acceptedAnswer":83},"What evidence is used to evaluate performance?",{"text":84,"@type":76},"Experiments are conducted on a cohort of 585 patients from the public ISPY-2 dataset, showing improvements over vision and self-supervised learning baselines across multiple classification metrics.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]