{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T00:34:09Z","timestamp":1777941249323,"version":"3.51.4"},"reference-count":33,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Technol Cancer Res Treat"],"published-print":{"date-parts":[[2025,6]]},"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>This study aimed to develop machine learning-based models to predict local recurrence in patients with lung oligometastases receiving stereotactic body radiotherapy (SBRT), using both clinical and radiomic features.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 80 lung oligometastases from 65 patients treated with SBRT were retrospectively evaluated. Clinical variables and radiomic features extracted from non-contrast planning computed tomography (CT) scans were collected. The dataset was randomly divided into training (70%) and test (30%) sets. Multivariable Cox proportional hazards models were developed to predict local recurrence using three feature sets: clinical only, radiomic only, and combined. Predictive performance was assessed using the concordance index (C-index).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The median follow-up duration was 11.8 months (range, 6.0-31.5), during which local recurrence was observed in 12 out of 80 lesions (15.0%) treated with SBRT. Multivariable Cox proportional hazards models for predicting local recurrence achieved C-index of 0.75 for the clinical model, 0.74 for the radiomic model, and 0.78 for the combined model. The combined model incorporated three features: soft tissue sarcoma histology (HR 7.70, 95% CI 1.65-35.87, p\u2009=\u20090.009), metastasis size (HR 1.07, 95% CI 1.01-1.14, p\u2009=\u20090.036), and Rad-score (HR 4.05, 95% CI 1.58-10.36, p\u2009=\u20090.003).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>These findings highlight the potential of machine learning-based models that integrate clinical and radiomic features to predict local recurrence in patients with lung oligometastases undergoing SBRT. Further validation in large, multicenter, and independent cohorts is needed.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/15330338251400229","type":"journal-article","created":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T16:06:19Z","timestamp":1763654779000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Prediction of Local Recurrence Using Clinical and Radiomic Features in Lung Oligometastases Treated with Stereotactic Body Radiotherapy"],"prefix":"10.1177","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5399-5430","authenticated-orcid":false,"given":"Rahmi At\u0131l","family":"Aksoy","sequence":"first","affiliation":[{"name":"Akdeniz University School of Medicine"},{"name":"Department of Radiation Oncology, Izmir City Hospital, Izmir, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7400-1837","authenticated-orcid":false,"given":"Timur","family":"Koca","sequence":"additional","affiliation":[{"name":"Akdeniz University School of Medicine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3607-4058","authenticated-orcid":false,"given":"Taner","family":"Danisman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hulya","family":"Dirol","sequence":"additional","affiliation":[{"name":"Akdeniz University School of Medicine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aylin Fidan","family":"Korcum","sequence":"additional","affiliation":[{"name":"Akdeniz University School of Medicine"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2025,11,20]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2020.04.003"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1200\/JCO.20.00818"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1177\/0300891618766820"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.lungcan.2023.107284"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.3390\/cancers13143623"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1080\/0284186X.2018.1521986"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1038\/nrclinonc.2017.141"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.4329\/wjr.v17.i8.109373"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.4251\/wjgo.v16.i4.1256"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.3857\/roj.2021.00311"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0311910"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmp.2024.103340"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00066-023-02086-6"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00066-022-02034-w"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.7326\/0003-4819-147-8-200710160-00010"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1002\/pro6.1226"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1001\/jamaoncol.2023.3495"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejca.2008.10.026"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.21037\/tlcr-24-588"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1186\/s13014-024-02485-8"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.3390\/cancers12102862"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.mri.2012.05.001"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1158\/0008-5472.CAN-17-0339"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020191145"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcm.2016.02.012"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v039.i05"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1177\/0272989X06295361"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1038\/ncomms5006"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1002\/cac2.12002"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1111\/1759-7714.13349"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1177\/1533033818782788"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1186\/s13014-021-01805-6"}],"container-title":["Technology in Cancer Research &amp; 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