{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,5]],"date-time":"2026-09-05T05:53:02Z","timestamp":1788587582222,"version":"build-2803163510"},"reference-count":28,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T00:00:00Z","timestamp":1724371200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Applied Clin Med Phys"],"published-print":{"date-parts":[[2024,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background and purpose<\/jats:title><jats:p>This study aims to externally validate a predictive model for distant metastasis (DM) with computed tomography (CT)\u2010based radiomics features in prospectively enrolled non\u2010small\u2010cell lung cancer patients undergoing dynamic tumor\u2010tracking stereotactic body radiation therapy (DTT\u2010SBRT).<\/jats:p><\/jats:sec><jats:sec><jats:title>Materials and methods<\/jats:title><jats:p>The study collected retrospective data from 567 patients across 11 institutions as the training dataset and prospectively enrolled 42 patients from four institutions as\u00a0the external test dataset. Four clinical features were collected, and 944 CT\u2010based radiomic features were extracted from gross tumor volumes. After standardization and feature selection, DM predictive models were developed using fine and gray regression (FG) and random survival forest (RSF), incorporating clinical and radiomic features, and their combinations within the training dataset. Then, the model was applied to the test dataset, dividing patients into high\u2010 and low\u2010risk groups based on medians of risk scores. Model performance was assessed using the concordance index (<jats:italic>C<\/jats:italic>\u2010index), and the statistical significance between groups was evaluated using Gray's test.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In the training dataset, 122 of 567 patients (21.5%) developed DM, compared to 9 of 42 patients (21.4%) in the test dataset. In the test dataset, the C\u2010indices of the clinical, radiomics, and hybrid models with FG were 0.559, 0.544, and 0.560, respectively, whereas those with RSF were 0.576, 0.604, and 0.627, respectively.<\/jats:p><jats:p>The hybrid model with RSF, which exhibited the best predictive performance of all models, identified 7 of 23 patients (30.4%) as high risk and 2 of 19 patients (10.5%) as low risk for DM incidence in the test dataset (<jats:italic>p<\/jats:italic>\u00a0=\u00a00.116).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Although predictive models for DM lack significance when applied to prospectively enrolled cases undergoing DTT\u2010lung SBRT, the model with RSF exhibits a consistent capacity to effectively classify patients at a high risk of developing DM.<\/jats:p><\/jats:sec>","DOI":"10.1002\/acm2.14475","type":"journal-article","created":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T16:44:59Z","timestamp":1724431499000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Prospective external validation of radiomics\u2010based predictive model of distant metastasis after dynamic tumor tracking stereotactic body radiation therapy in patients with non\u2010small\u2010cell lung cancer: A multi\u2010institutional analysis"],"prefix":"10.1002","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1356-5118","authenticated-orcid":false,"given":"Takanori","family":"Adachi","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology and Image\u2010Applied Therapy Graduate School of Medicine Kyoto University  Kyoto 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