{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T00:03:43Z","timestamp":1758845023839,"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_25","type":"book-chapter","created":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T02:29:49Z","timestamp":1758767389000},"page":"248-257","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Can We Teach AI to\u00a0Understand Breast Tumour Behaviour? Our MAMA-MIA Challenge Journey"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-4054-4546","authenticated-orcid":false,"given":"Hadeel","family":"Awwad","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2148-6751","authenticated-orcid":false,"given":"Joan C.","family":"Vilanova","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8080-2710","authenticated-orcid":false,"given":"Robert","family":"Mart\u00ed","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,21]]},"reference":[{"key":"25_CR1","doi-asserted-by":"publisher","unstructured":"Chen, T., Guestrin, C.: Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785\u2013794 (2016). https:\/\/doi.org\/10.1145\/2939672.2939785","DOI":"10.1145\/2939672.2939785"},{"issue":"4","key":"25_CR2","doi-asserted-by":"publisher","first-page":"1068","DOI":"10.1002\/jmri.28111","volume":"56","author":"S Eskreis-Winkler","year":"2022","unstructured":"Eskreis-Winkler, S., et al.: Breast mri background parenchymal enhancement categorization using deep learning: outperforming the radiologist. J. Magn. Reson. Imaging 56(4), 1068\u20131076 (2022). https:\/\/doi.org\/10.1002\/jmri.28111. epub 2022 Feb 15 Oct","journal-title":"J. Magn. Reson. Imaging"},{"key":"25_CR3","doi-asserted-by":"publisher","unstructured":"Garrucho, L., et al.: Advancing generalizability and fairness in breast mri tumour segmentation and treatment response prediction (mama-mia), March 2025. https:\/\/doi.org\/10.5281\/zenodo.15052678","DOI":"10.5281\/zenodo.15052678"},{"issue":"1","key":"25_CR4","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). https:\/\/doi.org\/10.1038\/s41597-025-04707-4","journal-title":"Sci. Data"},{"issue":"21","key":"25_CR5","doi-asserted-by":"publisher","first-page":"e104","DOI":"10.1158\/0008-5472.CAN-17-0339","volume":"77","author":"JJM Griethuysen","year":"2017","unstructured":"Griethuysen, J.J.M., et al.: Computational radiomics system to decode the radiographic phenotype. Can. Res. 77(21), e104\u2013e107 (2017). https:\/\/doi.org\/10.1158\/0008-5472.CAN-17-0339. Nov","journal-title":"Can. Res."},{"key":"25_CR6","doi-asserted-by":"publisher","unstructured":"Hirsch, L., et al.: Radiologist-level performance by using deep learning for segmentation of breast cancers on mri scans. Radiol. Artif. Intell. 4(1), e200231 (2022). https:\/\/doi.org\/10.1148\/ryai.200231","DOI":"10.1148\/ryai.200231"},{"key":"25_CR7","doi-asserted-by":"crossref","unstructured":"Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203\u2013211 (2021)","DOI":"10.1038\/s41592-020-01008-z"},{"key":"25_CR8","doi-asserted-by":"crossref","unstructured":"Isensee, F., et al.: nnU-Net revisited: a call for rigorous validation in 3d medical image segmentation (2024)","DOI":"10.1007\/978-3-031-72114-4_47"},{"key":"25_CR9","doi-asserted-by":"publisher","unstructured":"Janse, M.H.A., et al.: Deep learning-based segmentation of locally advanced breast cancer on mri in relation to residual cancer burden: a multi-institutional cohort study. J. Magn. Reson. Imaging 58(6), 1739\u20131749 (2023). https:\/\/doi.org\/10.1002\/jmri.28679","DOI":"10.1002\/jmri.28679"},{"key":"25_CR10","doi-asserted-by":"publisher","unstructured":"Li, W., et al.: I-SPY 2 breast dynamic contrast enhanced MRI trial (ISPY2) (Version 1) (2022). https:\/\/doi.org\/10.7937\/TCIA.D8Z0-9T85, dataset published on The Cancer Imaging Archive (TCIA)","DOI":"10.7937\/TCIA.D8Z0-9T85"},{"key":"25_CR11","doi-asserted-by":"publisher","unstructured":"Newitt, D., Hylton, N.: Single site breast dce-mri data and segmentations from patients undergoing neoadjuvant chemotherapy (version 3) (2016). https:\/\/doi.org\/10.7937\/K9\/TCIA.2016.QHsyhJKy","DOI":"10.7937\/K9\/TCIA.2016.QHsyhJKy"},{"key":"25_CR12","doi-asserted-by":"publisher","unstructured":"Newitt, D., Hylton, N., the I-SPY 1\u00a0Network, Team, A..T.: Multi-center breast dce-mri data and segmentations from patients in the i-spy 1\/acrin 6657 trials (2016). https:\/\/doi.org\/10.7937\/K9\/TCIA.2016.HdHpgJLK","DOI":"10.7937\/K9\/TCIA.2016.HdHpgJLK"},{"key":"25_CR13","doi-asserted-by":"crossref","unstructured":"Ny\u00fal, L.G., Udupa, J.K.: On standardizing the MR image intensity scale. Magn. Reson. Med. 42(6), 1072\u20131081 (1999). https:\/\/doi.org\/10.1002\/(sici)1522-2594(199912)42:6<1072::aid-mrm11>3.0.co;2-m","DOI":"10.1002\/(SICI)1522-2594(199912)42:6<1072::AID-MRM11>3.3.CO;2-D"},{"key":"25_CR14","doi-asserted-by":"publisher","first-page":"846775","DOI":"10.3389\/fonc.2022.846775","volume":"12","author":"Y Peng","year":"2022","unstructured":"Peng, Y., et al.: Pretreatment dce-mri-based deep learning outperforms radiomics analysis in predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer. Front. Oncol. 12, 846775 (2022). https:\/\/doi.org\/10.3389\/fonc.2022.846775","journal-title":"Front. Oncol."},{"key":"25_CR15","doi-asserted-by":"publisher","first-page":"4165","DOI":"10.1038\/s41598-018-22437-z","volume":"8","author":"D Ribli","year":"2018","unstructured":"Ribli, D., Horv\u00e1th, A., Unger, Z., Pollner, P., Csabai, I.: Detecting and classifying lesions in mammograms with deep learning. Sci. Rep. 8, 4165 (2018). https:\/\/doi.org\/10.1038\/s41598-018-22437-z","journal-title":"Sci. Rep."},{"key":"25_CR16","doi-asserted-by":"publisher","unstructured":"Saha, A., et al.: Dynamic contrast-enhanced magnetic resonance images of breast cancer patients with tumor locations (2021). https:\/\/doi.org\/10.7937\/TCIA.e3sv-re93","DOI":"10.7937\/TCIA.e3sv-re93"},{"key":"25_CR17","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1007\/s10278-017-0037-8","volume":"31","author":"Z Yaniv","year":"2018","unstructured":"Yaniv, Z., Lowekamp, B.C., Johnson, H.J., Beare, R.: Simpleitk image-analysis notebooks: a collaborative environment for education and reproducible research. J. Digit. Imaging 31, 290\u2013303 (2018). https:\/\/doi.org\/10.1007\/s10278-017-0037-8","journal-title":"J. Digit. Imaging"}],"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_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T02:29:54Z","timestamp":1758767394000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-05559-0_25"}},"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_25","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"}}]}}