{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T00:07:05Z","timestamp":1758672425550,"version":"3.44.0"},"reference-count":34,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T00:00:00Z","timestamp":1758585600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Brain metastases (BM), originating from extracranial malignancies, significantly threaten patient health. Accurate BM identification is crucial but labor-intensive manually. This study developed and validated a system for BM diagnosis, assessing its performance and stability.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>470 patients diagnosed with BM were divided into an 80% training set (n=379) and a 20% internal test set (n=91) using systematic sampling. An additional 172 patients were retrospectively enrolled for external validation. A comprehensive preprocessing pipeline was implemented. We developed a 3D U-Net model with a ResNet-34 backbone for BM prediction. MRI scans were resampled to 0.833 mm\u00b3 isotropic voxels, underwent skull stripping using SynthStrip, and were intensity-normalized via Z-score normalization. The model was trained on MRI scans paired with segmentation masks, utilizing ImageNet-pretrained encoder weights and a patch-based strategy (128\u00d7128\u00d7128 voxels).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The model maintained perfect specificity and AUCs across gender and age groups, with no significant differences in other metrics, confirming false positive exclusion unaffected by demographics. By cancer type: Internal testing showed significant difference of AUC (<jats:italic>p<\/jats:italic>&amp;lt;0.001) between lung cancer (n=74) and other cancers (n=17). The differences of other performance metrics were not statistically significant (<jats:italic>p<\/jats:italic>&amp;gt;0.13), though other cancers showed higher median F1\/IoU\/MCC. External validation showed other cancers (n=79) had significantly higher precision than lung cancer (n=93) (p&amp;lt;0.05). Lung cancer AUC (0.82) was significantly lower than other cancers (0.89) (p&amp;lt;0.001), suggesting need for sensitivity optimization; both maintained specificity=1.0000. Model time was significantly shorter than manual annotation (internal: 69s vs 113s; external: 66s vs 96s; both <jats:italic>p<\/jats:italic>&amp;lt;0.001), with high agreement.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The model demonstrated strong robustness and perfect specificity across demographics. While showing cancer type dependency (requiring improved lung cancer sensitivity), its high efficiency (40%-50% time reduction) and generalization provide a solid foundation for clinical translation.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fonc.2025.1657604","type":"journal-article","created":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T05:32:06Z","timestamp":1758605526000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Development and validation of a deep learning model using MR imaging for predicting brain metastases: an accuracy-focused study"],"prefix":"10.3389","volume":"15","author":[{"given":"Dan","family":"Shi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meng","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Min","family":"Dong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ning","family":"Xuan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yinsu","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoqiong","family":"Lv","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chao","family":"Xie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Xia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lingchun","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qinglei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Na","family":"Yin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2025,9,23]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1093\/neuonc\/noab101","article-title":"Epidemiology of brain metastases and leptomeningeal disease","volume":"23","author":"Lamba","year":"2021","journal-title":"Neuro Oncol"},{"key":"B2","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1038\/s41568-019-0220-y","article-title":"Brain metastasis","volume":"20","author":"Boire","year":"2020","journal-title":"Nat Rev Cancer."},{"key":"B3","doi-asserted-by":"publisher","first-page":"492","DOI":"10.1200\/JCO.21.02314","article-title":"Treatment for Brain Metastases: ASCO-SNO-ASTRO Guideline [published correction appears in J Clin Oncol","volume":"40","author":"Vogelbaum","year":"2022","journal-title":"2022 Apr 20;40(12):1392. doi: 10.1200\/JCO.22.00593.]. 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