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We propose a fully automated, context\u2010informed personalization framework that integrates radiology report\u2010derived clinical context with imaging features to improve longitudinal liver tumor segmentation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This retrospective study included 171 patients for generalized model training and 41 longitudinal patients with follow\u2010up CT scans acquired 3\u201315 months after baseline for personalization and evaluation. We developed a U\u2010Net\u2010based vision\u2010language segmentation framework that automatically summarizes free\u2010text radiology reports, encodes them into language embeddings, and integrates them into intermediate image features to provide clinical context. A generalized model was trained using paired CT\u2010report data. For each longitudinal patient, personalization was performed at the 3\u2010month follow\u2010up using either image\u2010only input (vision\u2010only personalization) or paired image\u2013report input (context\u2010informed personalization). Models were then evaluated on subsequent 6\u201315\u2010month follow\u2010up scans without further adaptation. Patients were stratified into Stable and Dynamic cohorts based on relative GTV volume variability. Performance was assessed using the Dice similarity coefficient (DSC) and LogPenalty Score (LPS), with additional ablation studies examining language models and report components.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      In the Dynamic cohort, vision\u2010only personalization exhibited diminishing gains at later follow\u2010up, whereas context\u2010informed personalization maintained statistically significant improvements in \u0394LPS at 9\u201315 months (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.05). In the Stable cohort, both personalization strategies showed sustained performance with comparable segmentation accuracy, indicating that incorporating report\u2010derived clinical context did not degrade performance in cases with limited anatomical variability. Ablation studies demonstrated that domain\u2010specific encoder\u2010based language models (BioClinicalBERT) outperformed larger generative models and that effective context integration did not require complex architectural modifications.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Incorporating report\u2010derived clinical context improves the longitudinal robustness of personalized liver tumor segmentation, particularly in patients with dynamic tumor evolution. These findings suggest that clinically grounded language representations can stabilize personalization across extended follow\u2010up while enabling fully automated and scalable deployment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70624","type":"journal-article","created":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T03:10:13Z","timestamp":1789528213000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Context\u2010informed personalized segmentation from radiology reports: A fully automated framework for long\u2010term liver tumor follow\u2010up"],"prefix":"10.1002","volume":"53","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9695-6079","authenticated-orcid":false,"given":"Jaehee","family":"Chun","sequence":"first","affiliation":[{"name":"Department of Imaging Physics The University of Texas MD Anderson Cancer Center  Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0995-3695","authenticated-orcid":false,"given":"McKell","family":"Woodland","sequence":"additional","affiliation":[{"name":"Department of Imaging Physics The University of Texas MD Anderson Cancer Center  Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3015-3570","authenticated-orcid":false,"given":"Austin","family":"Castelo","sequence":"additional","affiliation":[{"name":"Department of Imaging Physics The University of Texas MD Anderson Cancer Center  Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-2871-6692","authenticated-orcid":false,"given":"Caleb","family":"O'Connor","sequence":"additional","affiliation":[{"name":"Department of Imaging Physics The University of Texas MD Anderson Cancer Center  Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-9298-6644","authenticated-orcid":false,"given":"Mais","family":"Altaie","sequence":"additional","affiliation":[{"name":"Department of Imaging Physics The University of Texas MD Anderson Cancer Center  Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0178-0921","authenticated-orcid":false,"given":"Aashish","family":"Gupta","sequence":"additional","affiliation":[{"name":"Department of Imaging Physics The University of Texas MD Anderson Cancer Center  Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-3215-5555","authenticated-orcid":false,"given":"Shanli","family":"Ding","sequence":"additional","affiliation":[{"name":"Department of Imaging Physics The University of Texas MD Anderson Cancer Center  Houston Texas USA"},{"name":"Graduate School of Biomedical Sciences The University of Texas MD Anderson Cancer Center  Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-2181-6423","authenticated-orcid":false,"given":"Allyson","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Department of Imaging Physics The University of Texas MD Anderson Cancer Center  Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7675-3461","authenticated-orcid":false,"given":"Eugene J","family":"Koay","sequence":"additional","affiliation":[{"name":"Department of Gastrointestinal Radiation Oncology The University of Texas MD Anderson Cancer Center  Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9364-5040","authenticated-orcid":false,"given":"Kristy K","family":"Brock","sequence":"additional","affiliation":[{"name":"Department of Imaging Physics The University of Texas MD Anderson Cancer Center  Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,9,15]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1102\/1470\u20107330.2004.0011"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1102\/1470\u20107330.2012.0044"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.3390\/cancers16061206"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1148\/rycan.230011"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejca.2008.10.026"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1593\/tlo.11232"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2015.12.019"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.clon.2024.07.006"},{"key":"e_1_2_8_10_1","doi-asserted-by":"crossref","unstructured":"RonnebergerO FischerP BroxT U\u2010net: convolutional networks for biomedical image segmentation. 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