{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T09:48:48Z","timestamp":1791884928869,"version":"4.3.4"},"reference-count":45,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T00:00:00Z","timestamp":1791849600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T00:00:00Z","timestamp":1791849600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance Imaging"],"abstract":"ABSTRACT<\/jats:title>\n \n Background<\/jats:title>\n Distinct treatment strategies for small\u2010cell lung cancer (SCLC) and non\u2010small\u2010cell lung cancer (NSCLC) brain metastases require accurate noninvasive subtyping.<\/jats:p>\n <\/jats:sec>\n \n Purpose<\/jats:title>\n To develop and externally validate a biparametric MRI\u2010based 2.5D multi\u2010instance learning (MIL) deep learning (DL) model for discriminating SCLC from NSCLC brain metastases, with performance compared against clinical, radiomics, and 3D\u2010DL models.<\/jats:p>\n <\/jats:sec>\n \n Study Type<\/jats:title>\n Retrospective.<\/jats:p>\n <\/jats:sec>\n \n Population<\/jats:title>\n \n This study included 366 patients from four institutions. Center 1 provided the training set (\n n<\/jats:italic>\n \u2009=\u2009199; mean age, 63.88\u2009\u00b1\u200910.79\u2009years; 72.36% male) and an internal tuning set (\n n<\/jats:italic>\n \u2009=\u200985; mean age, 64.73\u2009\u00b1\u20099.53\u2009years; 67.06% male). Centers 2\u20134 were pooled into a single external test set (\n n<\/jats:italic>\n \u2009=\u200982; mean age, 62.26\u2009\u00b1\u200910.60\u2009years; 63.41% male).\n <\/jats:p>\n <\/jats:sec>\n \n Field Strength\/Sequence<\/jats:title>\n \n 1.5\u2009T and 3.0\u2009T; contrast\u2010enhanced\n T1<\/jats:styled-content>\n \u2010weighted imaging and fluid\u2010attenuated inversion\u2010recovery imaging.\n <\/jats:p>\n <\/jats:sec>\n \n Assessment<\/jats:title>\n The 2.5D\u2010MIL model was benchmarked against clinical, radiomics, and 3D\u2010DL models and evaluated on internal tuning and pooled external test sets.<\/jats:p>\n <\/jats:sec>\n \n Statistical Tests<\/jats:title>\n \n Shapiro\u2013Wilk, Student's\n t<\/jats:italic>\n \u2010test, Mann\u2013Whitney U, chi\u2010square, univariable and multivariable logistic regression, and DeLong tests were used. Performance measures included the area under the receiver operating characteristic curve (AUC) with 95% confidence intervals and classification and calibration metrics. Statistical significance was defined as two\u2010sided\n p<\/jats:italic>\n \u2009<\u20090.05.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n In the internal tuning set, AUCs were 0.842 (2.5D\u2010MIL), 0.740 (clinical), 0.710 (radiomics), and 0.747 (3D\u2010DL). In the external test set, AUCs were 0.826 (2.5D\u2010MIL), 0.826 (clinical), 0.696 (radiomics), and 0.664 (3D\u2010DL). Differences between 2.5D\u2010MIL and the clinical (\n p<\/jats:italic>\n \u2009=\u20091.000), radiomics (\n p<\/jats:italic>\n \u2009=\u20090.139), and 3D\u2010DL\u00a0(\n p<\/jats:italic>\n \u2009=\u20090.119) models were not significant.\n <\/jats:p>\n <\/jats:sec>\n \n Data Conclusion<\/jats:title>\n The MRI\u2010based 2.5D\u2010MIL model showed potential for noninvasive differentiation of SCLC from NSCLC brain metastases. Larger prospective studies are needed before clinical use.<\/jats:p>\n <\/jats:sec>\n \n Evidence Level<\/jats:title>\n 3.<\/jats:p>\n <\/jats:sec>\n \n Technical Efficacy<\/jats:title>\n Stage 2.<\/jats:p>\n <\/jats:sec>","DOI":"10.1002\/jmri.70588","type":"journal-article","created":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T09:30:02Z","timestamp":1791883802000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MRI\u2010Based 2.5D Multi\u2010Instance Deep Learning for Differentiating Pathological Subtypes of Brain Metastases From Lung Cancer"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-9158-6629","authenticated-orcid":false,"given":"Jinling","family":"Zhu","sequence":"first","affiliation":[{"name":"From the Department of Radiology The Second Affiliated Hospital of Soochow University Suzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cairui","family":"Liu","sequence":"additional","affiliation":[{"name":"From the Department of Radiology The Second Affiliated Hospital of Soochow University Suzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Zou","sequence":"additional","affiliation":[{"name":"Department of Radiotherapy & Oncology The Second Affiliated Hospital of Soochow University Suzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruizhe","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Radiotherapy & Oncology The Second Affiliated Hospital of Soochow University Suzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-3605-7509","authenticated-orcid":false,"given":"Xin","family":"Xie","sequence":"additional","affiliation":[{"name":"From the Department of Radiology The Second Affiliated Hospital of Soochow University Suzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ye","family":"Tian","sequence":"additional","affiliation":[{"name":"Department of Radiotherapy & Oncology The Second Affiliated Hospital of Soochow University Suzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6664-7224","authenticated-orcid":false,"given":"Longjiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School Nanjing University Nanjing Jiangsu China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4865-0665","authenticated-orcid":false,"given":"Bo","family":"Zhang","sequence":"additional","affiliation":[{"name":"From the Department of Radiology The Second Affiliated Hospital of Soochow University Suzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,10,13]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"crossref","first-page":"1173","DOI":"10.1016\/j.ejso.2016.10.006","article-title":"The Evolving Clinical Management of Cerebral Metastases","volume":"43","author":"Sinha R.","year":"2017","journal-title":"European Journal of Surgical Oncology"},{"key":"e_1_2_12_3_1","first-page":"620","article-title":"Clinical Insights Into Small Cell Lung Cancer: Tumor Heterogeneity, Diagnosis, Therapy, and Future Directions","volume":"73","author":"Megyesfalvi Z.","year":"2023","journal-title":"CA: A Cancer Journal for Clinicians"},{"key":"e_1_2_12_4_1","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1200\/JCO.21.02314","article-title":"Treatment for Brain Metastases: ASCO\u2010SNO\u2010ASTRO Guideline","volume":"40","author":"Vogelbaum M. 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