{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T17:58:50Z","timestamp":1781632730187,"version":"3.54.5"},"reference-count":22,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T00:00:00Z","timestamp":1769990400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T00:00:00Z","timestamp":1772409600000},"content-version":"vor","delay-in-days":28,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["82430061"],"award-info":[{"award-number":["82430061"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["82271957"],"award-info":[{"award-number":["82271957"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Science and Technology Development Fund of Shanghai Pudong New Area","award":["PKJ2024-Y55"],"award-info":[{"award-number":["PKJ2024-Y55"]}]},{"name":"Shanghai Municipal Health Commission General Project","award":["202240241"],"award-info":[{"award-number":["202240241"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cancer Imaging"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Luminal breast cancer shows limited sensitivity to neoadjuvant chemotherapy (NAC) and substantial risk of late recurrence among non-pCR patients. Accurate tools to predict both NAC response and long-term prognosis are urgently needed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>We retrospectively analyzed 850 patients from three cohorts (FUSCC, YNCC,tures were used for pCR prediction with XGBoost, whereas pre-, post-, and delta (\u0394) features informed prognostic modeling with Cox-XGBoost. Performance was evaluated by AUC and C-index with internal and external validation. Independent value of the radiomics-derived RadScore was tested by I-SPY2). A subregion-aware, multitemporal radiomics model was built from DCE- and DWI-MRI, complemented by conventional MRI descriptors (e.g., breast edema, shrinkage pattern) and clinicopathologic variables. Pre-NAC feamultivariable regression. Radiogenomic analyses explored biological underpinnings.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      For response prediction, 253 patients from FUSCC (pCR 38\/253) and 222 from YNCC (pCR 34\/222) were included. Seven radiomics features were retained, mainly from high-perfusion subregions (4\/7). The combined model achieved the best performance across cohorts, surpassing radiomics, traditional MRI, and clinical models, with AUCs of 0.83 [0.78-0.88], 0.78 [0.73-0.83], 0.63 [0.57-0.69] and 0.61 [0.55-0.67] in the validation cohort. RadScore derived from the radiomics remained an independent predictor of pCR after adjusting for clinical and MRI variables (OR = 2.06 [1.28\u20132.15];\n                      <jats:italic>P<\/jats:italic>\n                      = 0.001). For prognosis, 318 non-pCR patients with \u2265\u20095 years follow-up were analyzed (FUSCC\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009160, 44 events; YNCC\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009158, 48 events). Nine radiomics features were retained, dominated by delta (\u0394) features (5\/9) and moderate-perfusion subregions (4\/9). The combined model showed the highest prognostic discrimination outperforming radiomics, traditional MRI and clinical models in the validation cohort (0.84 [0.73-0.90] vs 0.81 [0.61, 0.84], 0.60 [0.73-0.90], 0.58 [0.47, 0.69]). Prognostic RadScore remained independentlnedy associated with recurrence (HR 4.78, 95% CI 2.54\u20135.89;\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.001), along with post-NAC Ki-67, diffuse edema, and non-concentric shrinkage. Radiogenomic validation confirmed that high-perfusion features driving response were enriched in drug-metabolism, PI3K-Akt, and estrogen signaling pathways, whereas moderate-perfusion delta (\u0394) features driving prognosis aligned with hypoxia and immune-evasion programs associated with recurrence.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Subregion-aware, multitemporal radiomics accurately and interpretabily predicts NAC response and long-term prognosis in luminal breast cancer, supporting individualized treatment selection and risk stratification.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s40644-026-00994-1","type":"journal-article","created":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T11:04:22Z","timestamp":1770030262000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multicenter study provides radiomic and biological insights into neoadjuvant chemotherapy response and prognosis in luminal breast cancer"],"prefix":"10.1186","volume":"26","author":[{"given":"Shiyun","family":"Sun","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yansong","family":"Bai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingnan","family":"Bai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingying","family":"Ding","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinlong","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiayin","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tingting","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yajia","family":"Gu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhuolin","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"You","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,2,2]]},"reference":[{"key":"994_CR1","doi-asserted-by":"publisher","unstructured":"Giaquinto AN, Sung H, Newman LA, Freedman RA, Smith RA, Star J, Jemal A, Siegel RL. 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