{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T18:04:11Z","timestamp":1779905051075,"version":"3.53.1"},"reference-count":35,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2024,11,18]],"date-time":"2024-11-18T00:00:00Z","timestamp":1731888000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"Key Medical Discipline Construction Unit of Jiangsu Province for the 14th Five-year plan in China","award":["JSDW202236"],"award-info":[{"award-number":["JSDW202236"]}]},{"name":"Suzhou Radiotherapy Clinical Medical Center in China","award":["Szlcyxzx202103"],"award-info":[{"award-number":["Szlcyxzx202103"]}]},{"DOI":"10.13039\/501100018556","name":"Science and Technology Program of Suzhou","doi-asserted-by":"publisher","award":["SLJ2022009"],"award-info":[{"award-number":["SLJ2022009"]}],"id":[{"id":"10.13039\/501100018556","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018556","name":"Science and Technology Program of Suzhou","doi-asserted-by":"publisher","award":["SYSD2022112"],"award-info":[{"award-number":["SYSD2022112"]}],"id":[{"id":"10.13039\/501100018556","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Acta Radiol"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Radiomics and deep learning (DL) can individually and efficiently identify the pathological type of brain metastases (BMs).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To investigate the feasibility of utilizing multi-parametric MRI-based deep transfer learning radiomics (DTLR) for the classification of lung adenocarcinoma (LUAD) and non-LUAD BMs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Material and Methods<\/jats:title>\n                    <jats:p>A retrospective analysis was performed on 342 patients with 1389 BMs. These instances were randomly assigned to a training set of 273 (1179 BMs) and a testing set of 69 (210 BMs) in an 8:2 ratio. Eight machine learning algorithms were employed to construct the radiomics models. A DL model was developed using four pre-trained convolutional neural networks (CNNs). The DTLR model was formulated by integrating the optimal performing radiomics model and the DL model using a classification probability averaging approach. The area under the curve (AUC), calibration curve, and decision curve analysis (DCA) were utilized to assess the performance and clinical utility of the models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The AUC for the optimal radiomics and DL model in the testing set were 0.824 (95% confidence interval [CI]= 0.726\u20130.923) and 0.775 (95% CI=0.666\u20130.884), respectively. The DTLR model demonstrated superior discriminatory power, achieving an AUC of 0.880 (95% CI=0.803\u20130.957). In addition, the DTLR model exhibited good consistency between actual and predicted probabilities based on the calibration curve and DCA analysis, indicating its significant clinical value.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Our study's DTLR model demonstrated high diagnostic accuracy in distinguishing LUAD from non-LUAD BMs. This method shows potential for the non-invasive identification of the histological subtype of BMs.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/02841851241292528","type":"journal-article","created":{"date-parts":[[2024,11,18]],"date-time":"2024-11-18T03:27:12Z","timestamp":1731900432000},"page":"24-34","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":4,"title":["A combined model integrating radiomics and deep learning based on multiparametric magnetic resonance imaging for classification of brain metastases"],"prefix":"10.1177","volume":"66","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4865-0665","authenticated-orcid":false,"given":"Bo","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, PR China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinling","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, PR China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruizhe","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, The Second Affiliated Hospital of Soochow University, Suzhou, PR China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Zou","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, The Second 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