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This study aims to develop DL models that identify four medulloblastoma molecular subgroups and prognostic-related genetic signatures.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This retrospective study enrolled 325 patients for model development and an independent external validation cohort of 124 patients, totaling 449\u00a0MB patients from 2 medical institutes. Consecutive patients with newly diagnosed MB at MRI (T1-weighted, T2-weighted, and contrast-enhanced T1-weighted) at two medical institutes between January 2015 and June 2023 were identified. Two-stage sequential DL models were designed\u2014MB-CNN that first identifies wingless (WNT), sonic hedgehog (SHH), Group 3, and Group 4. Further, prognostic-related genetic signatures using DL models (MB-CNN_TP53\/MYC\/Chr11) were developed to predict TP53 mutation, MYC amplification, and chromosome 11 loss status. A hybrid model combining MB-CNN and conventional data (clinical information and MRI features) was compared to a logistic regression model constructed only with conventional data. Four-classification tasks were evaluated with confusion matrices (accuracy) and two-classification tasks with ROC curves (area under the curve (AUC)).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The datasets comprised 449 patients (mean age\u2009\u00b1\u2009SD at diagnosis, 13.55\u00a0years\u2009\u00b1\u20092.33, 249 males). MB-CNN accurately classified MB subgroups in the external test dataset, achieving a median accuracy of 77.50% (range in 76.29% to 78.71%). MB-CNN_TP53\/MYC\/Chr11 models effectively predicted signatures (AUC of TP53 in SHH: 0.91, MYC amplification in Group 3: 0.87, chromosome 11 loss in Group 4: 0.89). The accuracy of the hybrid model outperformed the logistic regression model (82.20% vs. 59.14%,\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u2009.009) and showed comparable performance to MB-CNN (82.20% vs. 77.50%,\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.105).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>MRI-based DL models allowed identification of the molecular medulloblastoma subgroups and prognostic-related genetic signatures.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s41016-025-00405-7","type":"journal-article","created":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T09:44:29Z","timestamp":1757929469000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Exploring deep learning and hybrid approaches in molecular subgrouping and prognostic-related genetic signatures of medulloblastoma"],"prefix":"10.1186","volume":"11","author":[{"given":"Yanong","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hailong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yawei","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jane","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hiro Hiromichi","family":"Suzuki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaou","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiang","family":"Tao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoguang","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,9,15]]},"reference":[{"issue":"6","key":"405_CR1","doi-asserted-by":"publisher","first-page":"821","DOI":"10.1007\/s00401-016-1569-6","volume":"131","author":"V Ramaswamy","year":"2016","unstructured":"Ramaswamy V, Remke M, Bouffet E, Bailey S, Clifford SC, Doz F, et al. 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