{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T03:36:44Z","timestamp":1758771404879,"version":"3.44.0"},"publisher-location":"Cham","reference-count":22,"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:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,9,21]],"date-time":"2025-09-21T00:00:00Z","timestamp":1758412800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Accurate segmentation of small lesions in Breast Dynamic Contrast-Enhanced MRI (DCE-MRI) is critical for early cancer detection, especially in high-risk patients. While recent deep learning methods have advanced lesion segmentation, they primarily target large lesions and neglect valuable longitudinal and clinical information routinely used by radiologists. In real-world screening, detecting subtle or emerging lesions requires radiologists to compare across timepoints and consider previous radiology assessments, such as the BI-RADS score. We propose <jats:italic>LesiOnTime<\/jats:italic>, a novel 3D segmentation approach that mimics clinical diagnostic workflows by jointly leveraging longitudinal imaging and BI-RADS scores. The key components are: (1) a <jats:italic>Temporal Prior Attention (TPA)<\/jats:italic> block that dynamically integrates information from previous and current scans; and (2) a <jats:italic>BI-RADS Consistency Regularization (BCR)<\/jats:italic> loss that enforces latent space alignment for scans with similar radiological assessments, thus embedding domain knowledge into the training process. Evaluated on a curated in-house longitudinal dataset of high-risk patients with DCE-MRI, our approach outperforms state-of-the-art single-timepoint and longitudinal baselines by 5% in terms of Dice. Ablation studies demonstrate that both TPA and BCR contribute complementary performance gains. These results highlight the importance of incorporating temporal and clinical context for reliable early lesion segmentation in real-world breast cancer screening. Our code is publicly available at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/cirmuw\/LesiOnTime\" ext-link-type=\"uri\">https:\/\/github.com\/cirmuw\/LesiOnTime<\/jats:ext-link>.<\/jats:p>","DOI":"10.1007\/978-3-032-05559-0_33","type":"book-chapter","created":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T02:29:09Z","timestamp":1758767349000},"page":"329-339","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["LesiOnTime - Joint Temporal and\u00a0Clinical Modeling for\u00a0Small Breast Lesion Segmentation in\u00a0Longitudinal DCE-MRI"],"prefix":"10.1007","author":[{"given":"Mohammed","family":"Kamran","sequence":"first","affiliation":[]},{"given":"Maria","family":"Bernathova","sequence":"additional","affiliation":[]},{"given":"Raoul","family":"Varga","sequence":"additional","affiliation":[]},{"given":"Christian F.","family":"Singer","sequence":"additional","affiliation":[]},{"given":"Zsuzsanna","family":"Bago-Horvath","sequence":"additional","affiliation":[]},{"given":"Thomas","family":"Helbich","sequence":"additional","affiliation":[]},{"given":"Georg","family":"Langs","sequence":"additional","affiliation":[]},{"given":"Philipp","family":"Seeb\u00f6ck","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,21]]},"reference":[{"key":"33_CR1","unstructured":"American cancer society: breast cancer. website, American cancer society (2025), https:\/\/www.cancer.org\/cancer\/types\/breast-cancer.html, Accessed December 2025"},{"issue":"1","key":"33_CR2","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1186\/s41747-023-00343-y","volume":"7","author":"B Burger","year":"2023","unstructured":"Burger, B., Bernathova, M., Seeb\u00f6ck, P., Singer, C.F., Helbich, T.H., Langs, G.: Deep learning for predicting future lesion emergence in high-risk breast mri screening: a feasibility study. 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