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Inform. med."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    As artificial intelligence becomes increasingly integrated into medical imaging practice, its robustness across heterogeneous real-world settings remains a major challenge. We quantified the effect of real-world distribution shifts on three-dimensional AI models for lung nodule analysis on CT and, motivated by these shifts, developed and evaluated MedStyle-3DG, an open-source 3D vision-language framework for domain generalization. This retrospective multicenter study assembled 2679 chest CTs acquired from 2010 to 2025, with institutional review approval and waiver of informed consent. The study was designed in two stages: first, to quantify the extent of performance degradation from in-distribution (ID) to out-of-distribution (OOD), under clinically realistic distribution shifts; and second, to test whether a domain generalization strategy can mitigate these gaps. Data were split into training, validation, test-ID, and test-OOD to model three shifts: exposure variations, device manufacturer, and geographic changes. We then proposed MedStyle-3DG, a 3D vision-language domain generalization framework combining feature statistics mixing, stochastic weight averaging, vision-language alignment, and a three-branch ensemble. OOD F1 score and the gaps between test-ID and test-OOD were the primary outcomes. The 3D ResNet50 baseline showed consistent ID-OOD degradation across all shifts, largest for exposure (F1, 0.630 vs 0.521; gap, 10.9 percentage points;\n                    <jats:italic>P<\/jats:italic>\n                    \u2009&lt;\u2009.001). MedStyle-3DG improved exposure OOD F1 to 0.601 and reduced the gap to 7.7 percentage points, with similar improvements for manufacturer and geographic shifts. Across all three factors, MedStyle-3DG achieved state-of-the-art performance in mean OOD F1 (\n                    <jats:italic>P<\/jats:italic>\n                    \u2009&lt;\u2009.001) and reduction of the F1 generalization gap (\n                    <jats:italic>P<\/jats:italic>\n                    \u2009&lt;\u2009.041). Real-world protocol, vendor, and population shifts substantially degrade volumetric lung nodule CAD performance. MedStyle-3DG reduces generalization gaps and its code is available  at\u00a0\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/RafaelMedelean\/MedCLIP-3DG\" ext-link-type=\"uri\">https:\/\/github.com\/RafaelMedelean\/MedCLIP-3DG<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1007\/s10278-026-02196-4","type":"journal-article","created":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T19:36:19Z","timestamp":1789068979000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Understanding and Mitigating Distribution Shifts in Volumetric Lung Nodule CAD Using a 3D Vision-Language Framework"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2443-085X","authenticated-orcid":false,"given":"Bogdan A.","family":"Bercean","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rafael","family":"Medelean","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrei","family":"Tenescu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marius","family":"Benta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ravi","family":"Jain","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Moritz C.","family":"Halfmann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fabio","family":"Souschek","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert","family":"Enache","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexandru-Stefan","family":"Baicoianu-Nitescu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adrian","family":"Dijmarescu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ioana","family":"Lupescu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mugur","family":"Grasu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marius","family":"Marcu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,10]]},"reference":[{"key":"2196_CR1","doi-asserted-by":"publisher","first-page":"388","DOI":"10.1038\/s41746-025-01800-1","volume":"8","author":"R Singh","year":"2025","unstructured":"1. 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Kuestner, A whole-body FDG-PET\/CT dataset with manually annotated tumor lesions (FDG-PET-CT-Lesions), The Cancer Imaging Archive (2022); https:\/\/doi.org\/10.7937\/GKR0-XV29.","journal-title":"The Cancer Imaging Archive"}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-026-02196-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-026-02196-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-026-02196-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T19:36:26Z","timestamp":1789068986000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-026-02196-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,10]]},"references-count":44,"alternative-id":["2196"],"URL":"https:\/\/doi.org\/10.1007\/s10278-026-02196-4","relation":{},"ISSN":["2948-2933"],"issn-type":[{"value":"2948-2933","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,10]]},"assertion":[{"value":"27 April 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 July 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 August 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 September 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This retrospective study used de-identified chest CT examinations from public repositories and private multicenter clinical datasets. Public datasets were accessed and used in accordance with their respective data-use terms. Private clinical data were de-identified before analysis and were processed in compliance with applicable institutional and data protection requirements. Institutional review board or ethics committee approval was obtained where required by the contributing institutions, and the requirement for informed consent was waived due to the retrospective design and use of de-identified data. No prospective recruitment, patient contact, or intervention was performed.","order":1,"name":"Ethics","label":"Ethics Approval","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The requirement for informed consent was waived by the Institutional Review Boards of all participating institutions due to the retrospective design of the study and the use of de-identified imaging data.","order":2,"name":"Ethics","label":"Consent to Participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All patient images included in the manuscript and supplementary material are fully de-identified. The Institutional Review Boards of the participating institutions waived the requirement for additional consent for publication.","order":3,"name":"Ethics","label":"Consent for Publication","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Rafael Medelean, Bogdan A. Bercean, Andrei Tenescu, and Marius Benta are employees of Rayscape and own equity in the company. The remaining authors declare no financial interests. No Rayscape product was used in this study.","order":4,"name":"Ethics","label":"Competing Interests","group":{"name":"EthicsHeading","label":"Declarations"}}]}}