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The peritumoral vasculature on the maximum intensity projection (MIP) from pretreatment DCE-MRI was segmented by a Hessian matrix-based filter and then edited by a radiologist. Radiomics features were extracted from the tumor and peritumoral vasculature of the MIP images. The LASSO method was used for feature selection, and the k-nearest neighbor (k-NN) classifier was trained and validated to build a predictive model. The diagnostic performance was assessed using the ROC analysis.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>One hundred of the 282 patient (35.5%) with TNBC achieved pCRs after NAC. In predicting pCRs, the combined peritumoral vascular and intratumoral model (fusion model) yields a maximum AUC of 0.82 (95% confidence interval [CI]: 0.75, 0.88) in the primary cohort, a maximum AUC of 0.67 (95% CI: 0.57, 0.76) in the internal validation cohort, and a maximum AUC of 0.65 (95% CI: 0.52, 0.78) in TCIA cohort. The fusion model showed improved performance over the intratumoral model and the peritumoral vascular model, but not significantly (<jats:italic>p<\/jats:italic>\u2009&gt;\u20090.05).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>This study suggested that combined peritumoral vascular and intratumoral radiomics model could provide a non-invasive tool to enable prediction of pCR in TNBC patients treated with NAC.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12880-024-01311-7","type":"journal-article","created":{"date-parts":[[2024,6,6]],"date-time":"2024-06-06T09:02:33Z","timestamp":1717664553000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Development and validation of peritumoral vascular and intratumoral radiomics to predict pathologic complete responses to neoadjuvant chemotherapy in patients with triple-negative breast cancer"],"prefix":"10.1186","volume":"24","author":[{"given":"Tianwen","family":"Xie","sequence":"first","affiliation":[]},{"given":"Jing","family":"Gong","sequence":"additional","affiliation":[]},{"given":"Qiufeng","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Chengyue","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Siyu","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Weijun","family":"Peng","sequence":"additional","affiliation":[]},{"given":"Yajia","family":"Gu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,6,6]]},"reference":[{"key":"1311_CR1","doi-asserted-by":"publisher","first-page":"4429","DOI":"10.1158\/1078-0432.CCR-06-3045","volume":"13","author":"R Dent","year":"2007","unstructured":"Dent R, Trudeau M, Pritchard KI, et al. 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