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Two Ki-67 cut-offs (14% and 30%) were applied. Radiomics models were constructed using five machine learning classifiers, and deep learning models using four three-dimensional architectures (ResNet50, ResNet101, DenseNet121, and ShuffleNet). The best-performing models from each category were integrated with the clinical model using logistic regression to establish combined models. Prognostic value was evaluated using disease-free survival (DFS) in the prognostic cohort.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Among the radiomics models, those based on ExtraTrees and eXtreme Gradient Boosting performed best for the 14% and 30% cut-offs, respectively, with areas under the receiver operating characteristic curves (AUCs) of 0.885 and 0.831 in the external test cohort. Among the deep learning architectures, ShuffleNet performed best, with corresponding AUCs of 0.630 and 0.825. Combined models achieved the highest AUCs of 0.895 and 0.869 for the 14% and 30% cut-offs, respectively, in the external test cohort. However, they did not significantly outperform the corresponding radiomics models. In survival analysis, the radiomics score for predicting Ki-67\u2009\u2265\u200930% was an independent prognostic factor for DFS (hazard ratio: 1.661; 95% confidence interval: 1.142\u20132.417;\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.008).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>CE-CBBCT-based radiomics models demonstrated utility as non-invasive tools for the preoperative prediction of high Ki-67 expression defined at both the 14% and 30% cut-offs. The prognostic value of the radiomics score for predicting Ki-67\u2009\u2265\u200930% was preliminarily demonstrated.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s40644-026-01082-0","type":"journal-article","created":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T16:34:37Z","timestamp":1782491677000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Preoperative prediction of Ki-67 expression and prognosis in breast cancer based on contrast-enhanced cone-beam breast CT: a two-centre study"],"prefix":"10.1186","volume":"26","author":[{"given":"Wenxiu","family":"Guo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Binglin","family":"Lyu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yafei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keyi","family":"Bian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaqi","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaopan","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yueqiang","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaoxiang","family":"Ye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,26]]},"reference":[{"issue":"3","key":"1082_CR1","first-page":"229","volume":"74","author":"F Bray","year":"2024","unstructured":"Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. 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