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D'Annunzio Chieti Pescara","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100005874","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Oncotype Dx Recurrence Score (RS) has been validated in patients with ER\u2009+\u2009\/HER2\u2009\u2212\u2009invasive breast carcinoma to estimate patient risk of recurrence and guide the use of adjuvant chemotherapy. We investigated the role of MRI-based radiomics features extracted from the tumor and the peritumoral tissues to predict the risk of tumor recurrence. A total of 62 patients with biopsy-proved ER\u2009+\u2009\/HER2\u2009\u2212\u2009breast cancer who underwent pre-treatment MRI and Oncotype Dx were included. An RS\u2009&gt;\u200925 was considered discriminant between low-intermediate and high risk of tumor recurrence. Two readers segmented each tumor. Radiomics features were extracted from the tumor and the peritumoral tissues. Partial least square (PLS) regression was used as the multivariate machine learning algorithm. PLS \u03b2-weights of radiomics features included the 5% features with the largest \u03b2-weights in magnitude (top 5%). Leave-one-out nested cross-validation (nCV) was used to achieve hyperparameter optimization and evaluate the generalizable performance of the procedure. The diagnostic performance of the radiomics model was assessed through receiver operating characteristic (ROC) analysis. A null hypothesis probability threshold of 5% was chosen (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.05). The exploratory analysis for the complete dataset revealed an average absolute correlation among features of 0.51. The nCV framework delivered an AUC of 0.76 (<jats:italic>p<\/jats:italic>\u2009=\u20091.1\u221910<jats:sup>\u22123<\/jats:sup>). When combining \u201cearly\u201d and \u201cpeak\u201d DCE images of only T or TST, a tendency toward statistical significance was obtained for TST with an AUC of 0.61 (<jats:italic>p<\/jats:italic>\u2009=\u20090.05). The 47 features included in the top 5% were balanced between T and TST (23 and 24, respectively). Moreover, 33\/47 (70%) were texture-related, and 25\/47 (53%) were derived from high-resolution images (1\u00a0mm). A radiomics-based machine learning approach shows the potential to accurately predict the recurrence risk in early ER\u2009+\u2009\/HER2\u2009\u2212\u2009breast cancer patients.<\/jats:p>","DOI":"10.1007\/s10278-023-00781-5","type":"journal-article","created":{"date-parts":[[2023,1,25]],"date-time":"2023-01-25T19:02:44Z","timestamp":1674673364000},"page":"1071-1080","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["MRI-Based Radiomics Approach Predicts Tumor Recurrence in ER\u2009+\u2009\/HER2\u2009\u2212\u2009Early Breast Cancer Patients"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1089-9809","authenticated-orcid":false,"given":"Piero","family":"Chiacchiaretta","sequence":"first","affiliation":[]},{"given":"Domenico","family":"Mastrodicasa","sequence":"additional","affiliation":[]},{"given":"Antonio Maria","family":"Chiarelli","sequence":"additional","affiliation":[]},{"given":"Riccardo","family":"Luberti","sequence":"additional","affiliation":[]},{"given":"Pierpaolo","family":"Croce","sequence":"additional","affiliation":[]},{"given":"Mario","family":"Sguera","sequence":"additional","affiliation":[]},{"given":"Concetta","family":"Torrione","sequence":"additional","affiliation":[]},{"given":"Camilla","family":"Marinelli","sequence":"additional","affiliation":[]},{"given":"Chiara","family":"Marchetti","sequence":"additional","affiliation":[]},{"given":"Angelucci","family":"Domenico","sequence":"additional","affiliation":[]},{"given":"Giulio","family":"Cocco","sequence":"additional","affiliation":[]},{"given":"Angela","family":"Di Credico","sequence":"additional","affiliation":[]},{"given":"Alessandro","family":"Russo","sequence":"additional","affiliation":[]},{"given":"Claudia","family":"D\u2019Eramo","sequence":"additional","affiliation":[]},{"given":"Antonio","family":"Corvino","sequence":"additional","affiliation":[]},{"given":"Marco","family":"Colasurdo","sequence":"additional","affiliation":[]},{"given":"Stefano L.","family":"Sensi","sequence":"additional","affiliation":[]},{"given":"Marzia","family":"Muzi","sequence":"additional","affiliation":[]},{"given":"Massimo","family":"Caulo","sequence":"additional","affiliation":[]},{"given":"Andrea","family":"Delli Pizzi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,1,25]]},"reference":[{"issue":"1","key":"781_CR1","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1016\/j.molonc.2010.11.003","volume":"5","author":"A Prat","year":"2011","unstructured":"Prat A, Perou CM. 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The other authors declare no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}]}}