{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,9]],"date-time":"2026-08-09T19:43:40Z","timestamp":1786304620519,"version":"build-2736575974"},"reference-count":23,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T00:00:00Z","timestamp":1784678400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004772","name":"Ningxia Hui Autonomous Region Natural Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004772","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100016692","name":"Key Research and Development Program of Ningxia","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100016692","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["academicradiology.org","clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Academic Radiology"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.acra.2026.07.038","type":"journal-article","created":{"date-parts":[[2026,8,8]],"date-time":"2026-08-08T19:27:12Z","timestamp":1786217232000},"update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"title":["Cross-Spatial-Scale Radiomics for Predicting Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer: A Multicenter, Interpretable MRI Study"],"prefix":"10.1016","author":[{"given":"Pei","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen-xi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming","family":"Jie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xueqiang","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huairong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Yin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fan","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"9938","key":"10.1016\/j.acra.2026.07.038_bib1","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/S0140-6736(13)62422-8","article-title":"Pathological complete response and long-term clinical benefit in breast cancer: the CTNeoBC pooled analysis","volume":"384","author":"Cortazar","year":"2014","journal-title":"Lancet"},{"issue":"12","key":"10.1016\/j.acra.2026.07.038_bib2","doi-asserted-by":"crossref","first-page":"2838","DOI":"10.1158\/1078-0432.CCR-19-3492","article-title":"Pathologic complete response after neoadjuvant chemotherapy and impact on breast cancer recurrence and survival: a comprehensive meta-analysis","volume":"26","author":"Spring","year":"2020","journal-title":"Clin Cancer Res"},{"issue":"9","key":"10.1016\/j.acra.2026.07.038_bib3","doi-asserted-by":"crossref","first-page":"1355","DOI":"10.1001\/jamaoncol.2020.2535","article-title":"Association of event-free and distant recurrence-free survival with individual-level pathologic complete response in neoadjuvant treatment of stages 2 and 3 breast cancer: three-year follow-up analysis for the I-SPY2 adaptively randomized clinical trial","volume":"6","author":"I-SPY2 Trial Consortium","year":"2020","journal-title":"JAMA Oncol"},{"key":"10.1016\/j.acra.2026.07.038_bib4","first-page":"229","article-title":"Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"74","author":"Bray","year":"2024","journal-title":"CA Cancer J Clin"},{"key":"10.1016\/j.acra.2026.07.038_bib5","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1016\/j.annonc.2024.02.002","article-title":"Event-free survival by residual cancer burden with pembrolizumab in early-stage TNBC: exploratory analysis from KEYNOTE-522","volume":"35","author":"Pusztai","year":"2024","journal-title":"Ann Oncol"},{"issue":"9","key":"10.1016\/j.acra.2026.07.038_bib6","article-title":"Radiomics in breast cancer: current advances and future directions","volume":"5","author":"Qi","year":"2024","journal-title":"Cell Rep Med"},{"key":"10.1016\/j.acra.2026.07.038_bib7","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1148\/radiol.2020191145","article-title":"The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping","volume":"295","author":"Zwanenburg","year":"2020","journal-title":"Radiology"},{"key":"10.1016\/j.acra.2026.07.038_bib8","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1002\/jmri.28273","article-title":"Four-dimensional machine learning radiomics for the pretreatment assessment of breast cancer pathologic complete response to neoadjuvant chemotherapy in dynamic contrast-enhanced MRI","volume":"57","author":"Caballo","year":"2023","journal-title":"J Magn Reson Imaging"},{"key":"10.1016\/j.acra.2026.07.038_bib9","doi-asserted-by":"crossref","first-page":"1723","DOI":"10.1002\/ijc.35514","article-title":"MR imaging of breast cancer: Interpretable radiomics analysis to assess treatment response and survival prognosis after neoadjuvant therapy","volume":"157","author":"Hou","year":"2025","journal-title":"Int J Cancer"},{"issue":"6","key":"10.1016\/j.acra.2026.07.038_bib10","doi-asserted-by":"crossref","DOI":"10.1148\/ryai.2020190168","article-title":"Tumor habitat-derived radiomic features at pretreatment MRI that are prognostic for progression-free survival in glioblastoma are associated with key morphologic attributes at histopathologic examination: a feasibility study","volume":"2","author":"Verma","year":"2020","journal-title":"Radiol Artif Intell"},{"key":"10.1016\/j.acra.2026.07.038_bib11","article-title":"MRI-based quantification of intratumoral heterogeneity for predicting treatment response to neoadjuvant chemotherapy in breast cancer","volume":"308","author":"Shi","year":"2023","journal-title":"Radiology"},{"issue":"1","key":"10.1016\/j.acra.2026.07.038_bib12","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1186\/s13058-024-01921-7","article-title":"Quantification of intratumoral heterogeneity using habitat-based MRI radiomics to identify HER2-positive, -low and -zero breast cancers: a multicenter study","volume":"26","author":"Chen","year":"2024","journal-title":"Breast Cancer Res"},{"issue":"1","key":"10.1016\/j.acra.2026.07.038_bib13","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1186\/s13244-025-01934-6","article-title":"Intratumoral and peritumoral ultrasound-based radiomics for preoperative prediction of HER2-low breast cancer: a multicenter retrospective study","volume":"16","author":"Luo","year":"2025","journal-title":"Insights Imaging"},{"issue":"1","key":"10.1016\/j.acra.2026.07.038_bib14","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1186\/s13058-017-0846-1","article-title":"Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI","volume":"19","author":"Braman","year":"2017","journal-title":"Breast Cancer Res"},{"issue":"1","key":"10.1016\/j.acra.2026.07.038_bib15","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-021-99701-2","article-title":"Robust imaging habitat computation using voxel-wise radiomics features","volume":"11","author":"Bernatowicz","year":"2021","journal-title":"Sci Rep"},{"key":"10.1016\/j.acra.2026.07.038_bib16","first-page":"4765","article-title":"A unified approach to interpreting model predictions","volume":"30","author":"Lundberg","year":"2017","journal-title":"Adv\u00a0Neural Informat\u00a0Process\u00a0Syst"},{"issue":"6","key":"10.1016\/j.acra.2026.07.038_bib17","doi-asserted-by":"crossref","first-page":"556","DOI":"10.3390\/bioengineering11060556","article-title":"Explainable precision medicine in breast MRI: a combined radiomics and deep learning approach for the classification of contrast agent uptake","volume":"11","author":"Nowakowska","year":"2024","journal-title":"Bioengineering (Basel)"},{"issue":"11","key":"10.1016\/j.acra.2026.07.038_bib18","doi-asserted-by":"crossref","DOI":"10.1002\/acm2.70347","article-title":"Integrating multiparametric MRI radiomics and clinical models to assess sensitivity to neoadjuvant chemotherapy in breast cancer: a multicenter study","volume":"26","author":"Zeng","year":"2025","journal-title":"J Appl Clin Med Phys"},{"issue":"7","key":"10.1016\/j.acra.2026.07.038_bib19","doi-asserted-by":"crossref","first-page":"3801","DOI":"10.1016\/j.acra.2025.03.010","article-title":"Multiparametric MRI-based interpretable machine learning radiomics model for distinguishing between luminal and non-luminal tumors in breast cancer: a multicenter study","volume":"32","author":"Zhou","year":"2025","journal-title":"Acad Radiol"},{"key":"10.1016\/j.acra.2026.07.038_bib20","article-title":"Value of intra- and peritumoral ultrasound radiomics for predicting axillary lymph node burden in breast cancer","volume":"15","author":"Hao","year":"2025","journal-title":"Front Oncol"},{"issue":"3","key":"10.1016\/j.acra.2026.07.038_bib21","doi-asserted-by":"crossref","first-page":"968","DOI":"10.3390\/biomedicines11030968","article-title":"Updates on lymphovascular invasion in breast cancer","volume":"11","author":"Kuhn","year":"2023","journal-title":"Biomedicines"},{"issue":"7","key":"10.1016\/j.acra.2026.07.038_bib22","doi-asserted-by":"crossref","first-page":"1727","DOI":"10.3390\/cancers14071727","article-title":"Multiparametric 18F-FDG PET\/MRI-based radiomics for prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer","volume":"14","author":"Umutlu","year":"2022","journal-title":"Cancers (Basel)"},{"issue":"11","key":"10.1016\/j.acra.2026.07.038_bib23","doi-asserted-by":"crossref","first-page":"1654","DOI":"10.1001\/jamaoncol.2021.3690","article-title":"Assessment of residual cancer burden and event-free survival in neoadjuvant treatment for high-risk breast cancer: an analysis of data from the I-SPY2 randomized clinical trial","volume":"7","author":"Symmans","year":"2021","journal-title":"JAMA Oncol"}],"container-title":["Academic Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1076633226005465?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1076633226005465?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,8,9]],"date-time":"2026-08-09T19:39:10Z","timestamp":1786304350000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1076633226005465"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":23,"alternative-id":["S1076633226005465"],"URL":"https:\/\/doi.org\/10.1016\/j.acra.2026.07.038","relation":{},"ISSN":["1076-6332"],"issn-type":[{"value":"1076-6332","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Cross-Spatial-Scale Radiomics for Predicting Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer: A Multicenter, Interpretable MRI Study","name":"articletitle","label":"Article Title"},{"value":"Academic Radiology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.acra.2026.07.038","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Association of Academic Radiology. Published by Elsevier Inc.","name":"copyright","label":"Copyright"}]}}