{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T00:03:44Z","timestamp":1758845024097,"version":"3.44.0"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032055583","type":"print"},{"value":"9783032055590","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,21]],"date-time":"2025-09-21T00:00:00Z","timestamp":1758412800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,21]],"date-time":"2025-09-21T00:00:00Z","timestamp":1758412800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-05559-0_12","type":"book-chapter","created":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T02:33:06Z","timestamp":1758767586000},"page":"113-122","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Towards Breast Cancer Recurrence Prediction Using Transformer-Based Learning from\u00a0Global\u2013Local Radiomics and\u00a0Clinical Data"],"prefix":"10.1007","author":[{"given":"Adnan","family":"Khalid","sequence":"first","affiliation":[]},{"given":"Muhammad","family":"Mursil","sequence":"additional","affiliation":[]},{"given":"Fabrice","family":"Meriaudeau","sequence":"additional","affiliation":[]},{"given":"Ibrahima","family":"Faye","sequence":"additional","affiliation":[]},{"given":"Alain","family":"Lalande","sequence":"additional","affiliation":[]},{"given":"Domenec","family":"Puig","sequence":"additional","affiliation":[]},{"given":"Hatem A.","family":"Rashwan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,21]]},"reference":[{"key":"12_CR1","doi-asserted-by":"crossref","unstructured":"Arik, S.\u00d6., Pfister, T.: TabNet: attentive interpretable tabular learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a035, pp. 6679\u20136687 (2021)","DOI":"10.1609\/aaai.v35i8.16826"},{"issue":"11","key":"12_CR2","doi-asserted-by":"publisher","first-page":"674","DOI":"10.1038\/nrclinonc.2016.66","volume":"13","author":"G Bianchini","year":"2016","unstructured":"Bianchini, G., Balko, J.M., Mayer, I.A., Sanders, M.E., Gianni, L.: Triple-negative breast cancer: challenges and opportunities of a heterogeneous disease. Nat. Rev. Clin. Oncol. 13(11), 674\u2013690 (2016)","journal-title":"Nat. Rev. Clin. Oncol."},{"issue":"15","key":"12_CR3","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., et al.: Triple-negative breast cancer: clinical features and patterns of recurrence. Clin. Cancer Res. 13(15), 4429\u20134434 (2007)","journal-title":"Clin. Cancer Res."},{"issue":"11","key":"12_CR4","doi-asserted-by":"publisher","first-page":"1829","DOI":"10.1200\/JCO.2009.24.4798","volume":"28","author":"M Dowsett","year":"2010","unstructured":"Dowsett, M., et al.: Prediction of risk of distant recurrence using the 21-gene recurrence score in node-negative and node-positive postmenopausal patients with breast cancer treated with anastrozole or tamoxifen: a transatac study. J. Clin. Oncol. 28(11), 1829\u20131834 (2010)","journal-title":"J. Clin. Oncol."},{"key":"12_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40644-018-0145-9","volume":"18","author":"K Drukker","year":"2018","unstructured":"Drukker, K., Li, H., Antropova, N., Edwards, A., Papaioannou, J., Giger, M.L.: Most-enhancing tumor volume by MRI radiomics predicts recurrence-free survival \u201cearly on\u2019\u2019 in neoadjuvant treatment of breast cancer. Cancer Imaging 18, 1\u20139 (2018)","journal-title":"Cancer Imaging"},{"issue":"1","key":"12_CR6","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1038\/s41597-025-04707-4","volume":"12","author":"L Garrucho","year":"2025","unstructured":"Garrucho, L., et al.: A large-scale multicenter breast cancer DCE-MRI benchmark dataset with expert segmentations. Sci. Data 12(1), 453 (2025)","journal-title":"Sci. Data"},{"issue":"8","key":"12_CR7","doi-asserted-by":"publisher","first-page":"700","DOI":"10.1016\/j.crad.2017.10.021","volume":"73","author":"D Leithner","year":"2018","unstructured":"Leithner, D., et al.: Clinical role of breast MRI now and going forward. Clin. Radiol. 73(8), 700\u2013714 (2018)","journal-title":"Clin. Radiol."},{"issue":"2","key":"12_CR8","doi-asserted-by":"publisher","first-page":"382","DOI":"10.1148\/radiol.2016152110","volume":"281","author":"H Li","year":"2016","unstructured":"Li, H., et al.: MR imaging radiomics signatures for predicting the risk of breast cancer recurrence as given by research versions of Mammaprint, oncotype DX, and PAM50 gene assays. Radiology 281(2), 382\u2013391 (2016)","journal-title":"Radiology"},{"issue":"6","key":"12_CR9","doi-asserted-by":"publisher","first-page":"1719","DOI":"10.1148\/rg.266065025","volume":"26","author":"KJ Macura","year":"2006","unstructured":"Macura, K.J., Ouwerkerk, R., Jacobs, M.A., Bluemke, D.A.: Patterns of enhancement on breast MR images: interpretation and imaging pitfalls. Radiographics 26(6), 1719\u20131734 (2006)","journal-title":"Radiographics"},{"issue":"23","key":"12_CR10","doi-asserted-by":"publisher","DOI":"10.1097\/MD.0000000000015871","volume":"98","author":"KJ Nam","year":"2019","unstructured":"Nam, K.J., et al.: Radiomics signature on 3T dynamic contrast-enhanced magnetic resonance imaging for estrogen receptor-positive invasive breast cancers: Preliminary results for correlation with oncotype dx recurrence scores. Medicine 98(23), e15871 (2019)","journal-title":"Medicine"},{"issue":"S3","key":"12_CR11","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1634\/theoncologist.10-90003-20","volume":"10","author":"J O\u2019Shaughnessy","year":"2005","unstructured":"O\u2019Shaughnessy, J.: Extending survival with chemotherapy in metastatic breast cancer. Oncologist 10(S3), 20\u201329 (2005)","journal-title":"Oncologist"},{"issue":"6","key":"12_CR12","doi-asserted-by":"publisher","first-page":"1840","DOI":"10.3390\/cancers15061840","volume":"15","author":"V Romeo","year":"2023","unstructured":"Romeo, V., et al.: MRI radiomics and machine learning for the prediction of oncotype DX recurrence score in invasive breast cancer. Cancers 15(6), 1840 (2023)","journal-title":"Cancers"},{"issue":"4","key":"12_CR13","doi-asserted-by":"publisher","first-page":"508","DOI":"10.1038\/s41416-018-0185-8","volume":"119","author":"A Saha","year":"2018","unstructured":"Saha, A., et al.: A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features. Br. J. Cancer 119(4), 508\u2013516 (2018)","journal-title":"Br. J. Cancer"},{"issue":"2","key":"12_CR14","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1056\/NEJMoa1804710","volume":"379","author":"JA Sparano","year":"2018","unstructured":"Sparano, J.A., et al.: Adjuvant chemotherapy guided by a 21-gene expression assay in breast cancer. N. Engl. J. Med. 379(2), 111\u2013121 (2018)","journal-title":"N. Engl. J. Med."},{"key":"12_CR15","doi-asserted-by":"crossref","unstructured":"Sung, H., et al.: Global cancer statistics 2020: globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: Cancer J. Clinic. 71(3), 209\u2013249 (2021)","DOI":"10.3322\/caac.21660"},{"key":"12_CR16","doi-asserted-by":"crossref","unstructured":"Zhang, J., Saha, A., Zhu, Z., Mazurowski, M.A.: Breast tumor segmentation in DCE-MRI using fully convolutional networks with an application in radiogenomics. In: Medical Imaging 2018: Computer-Aided Diagnosis, vol. 10575, pp. 192\u2013196. SPIE (2018)","DOI":"10.1117\/12.2295436"},{"issue":"Suppl 1","key":"12_CR17","doi-asserted-by":"publisher","first-page":"857","DOI":"10.1007\/s10462-023-10543-y","volume":"56","author":"T Zhang","year":"2023","unstructured":"Zhang, T., et al.: Radiomics and artificial intelligence in breast imaging: a survey. Artif. Intell. Rev. 56(Suppl 1), 857\u2013892 (2023)","journal-title":"Artif. Intell. Rev."}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence and Imaging for Diagnostic and Treatment Challenges in Breast Care"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-05559-0_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T02:33:10Z","timestamp":1758767590000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-05559-0_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,21]]},"ISBN":["9783032055583","9783032055590"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-05559-0_12","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,21]]},"assertion":[{"value":"21 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"Deep-Breath","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Deep Breast Workshop on AI and Imaging for Diagnostic and Treatment Challenges in Breast Care","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"deep-breath2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/deep-breath-miccai.github.io\/deepbreath-2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}