{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T15:01:48Z","timestamp":1777906908559,"version":"3.51.4"},"reference-count":33,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T00:00:00Z","timestamp":1773878400000},"content-version":"vor","delay-in-days":18,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2026,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Epidermal growth factor receptor (EGFR) and its mutation subtypes play a pivotal role in the treatment of non\u2010small cell lung cancer (NSCLC) patients. Therefore, developing an accurate, noninvasive quantitative method to predict EGFR genotype is crucial for personalized treatment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To explore a deep learning\u2010based method with whole\u2010brain information for predicting EGFR mutation and subtypes utilizing MRI images in NSCLC patients presenting with brain metastasis (BM).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study enrolled 293 patients with BM. A primary set was built with 170 patients from Center 1 (between January 2017 and December 2021). External sets were constructed with 62 patients from Center 2 (between July 2014 and October 2021) and 61 patients from Center 3 (between January 2020 and October 2022). All patients underwent contrast\u2010enhanced T1\u2010weighted (T1CE) and T2\u2010weighted (T2W) brain MRI scans before genetic testing. An EGFR site recognition network (ESR\u2010Net) was developed by mining whole\u2010brain information to predict EGFR mutations and subtypes. The ESR\u2010Net integrated deformable convolution and an auxiliary network to seek informative mutation features and enhance tumor features, respectively. Predictive performances of deep learning models were assessed using area under the curve (AUC) analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>For the prediction of EGFR mutations, the ESR\u2010Net demonstrated superior performance with AUCs ranging from 0.835 to 0.840 across primary and external validation sets, surpassing conventional state\u2010of\u2010the\u2010art methodologies. Furthermore, the ESR\u2010Net exhibited AUCs ranging from 0.858 to 0.904 for predicting EGFR exon 19 (Del19) mutation and 0.838 to 0.903 for predicting EGFR exon 21 (L858R) mutation across primary and external sets.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The developed ESR\u2010Net demonstrates promising potential for early detection of EGFR mutations and subtypes with multicenter data, which may promote optimal treatment management for patients with brain metastatic NSCLC.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70398","type":"journal-article","created":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T16:00:17Z","timestamp":1773936017000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Mining whole\u2010brain information with deep learning to predict EGFR mutation and 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