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The model then classified 456 NHG2 tumors into DRG2\u2212 (NHG1-like) and DRG2+ (NHG3-like) subgroups. Recurrence-free survival (RFS) was assessed with Kaplan\u2013Meier and Cox models adjusting for age, lymph node invasion, tumor stage, and molecular subtype.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      DRG achieved an AUC of 0.84 [95% CI: 0.83\u20130.86] in training, 0.82 [0.71\u20130.91] in testing, and 0.84 [0.69\u20130.96] in external validation. Among NHG2 tumors, 315 were classified as DRG2\u2212 and 131 as DRG2+. Patients with DRG2+ tumors had significantly worse RFS compared to DRG2\u2212 (adjusted hazard ratio\u2009=\u20092.39 [95% CI: 1.29\u20134.45],\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.0059), independent of standard prognostic factors. Incorporating DRG classification improved the Cox model\u2019s\n                      <jats:italic>C<\/jats:italic>\n                      -index from 0.68 to 0.73 (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.040).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>A deep learning model applied to routine DCE MRI effectively stratified NHG2 breast tumors into clinically meaningful subgroups with distinct recurrence risk. This approach offers a cost-effective tool for individualized risk stratification and could help tailor treatment to minimize over- and under-treatment in intermediate-grade breast cancer.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Points<\/jats:title>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Question<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>Difficulty in deciding between treatment options (neoadjuvant chemotherapy or primary surgery) in patients with intermediate risk breast cancer (Nottingham Grade 2).<\/jats:italic>\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Findings<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>Deep learning model reclassified grade 2 tumors into grade 1- and 3-like based on MRI. Patients with grade 3-like tumors had worse RFS: adjusted hazard ratio\u2009=\u20092.39 [95% CI: 1.29\u20134.45], p\u2009=\u20090.0059)<\/jats:italic>\n                      .\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Clinical relevance<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>Risk stratification could inform treatment choice to minimize over- and under-treatment in patients with intermediate-risk breast cancer<\/jats:italic>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Graphical Abstract<\/jats:title>\n                  <\/jats:sec>","DOI":"10.1007\/s00330-025-12208-6","type":"journal-article","created":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T13:48:28Z","timestamp":1765979308000},"page":"3335-3345","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A deep learning framework to stratify Nottingham histologic grade 2 breast tumors based on dynamic contrast-enhanced MRI"],"prefix":"10.1007","volume":"36","author":[{"given":"Roham","family":"Hadidchi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anchita","family":"Agrawal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael Z.","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takouhie","family":"Maldijan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yihui","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hien Quang","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinyu","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Della","family":"Makower","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Susan","family":"Fineberg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6403-2827","authenticated-orcid":false,"given":"Tim Q.","family":"Duong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,12,17]]},"reference":[{"key":"12208_CR1","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1111\/j.1365-2559.1991.tb00229.x","volume":"19","author":"CW Elston","year":"1991","unstructured":"Elston CW, Ellis IO (1991) Pathological prognostic factors in breast cancer. 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