{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T15:40:13Z","timestamp":1771515613396,"version":"3.50.1"},"reference-count":44,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T00:00:00Z","timestamp":1733961600000},"content-version":"vor","delay-in-days":346,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["75N91019D00024"],"award-info":[{"award-number":["75N91019D00024"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["3U2CHL156291-03S2"],"award-info":[{"award-number":["3U2CHL156291-03S2"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100001794","name":"Pediatric Brain Tumor Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100001794","id-type":"DOI","asserted-by":"publisher"}]},{"name":"DIPG\/DMG Research Funding Alliance"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>Fully automatic skull-stripping and tumor segmentation are crucial for monitoring pediatric brain tumors (PBT). Current methods, however, often lack generalizability, particularly for rare tumors in the sellar\/suprasellar regions and when applied to real-world clinical data in limited data scenarios. To address these challenges, we propose AI-driven techniques for skull-stripping and tumor segmentation.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>Multi-institutional, multi-parametric MRI scans from 527 pediatric patients (n\u2005=\u2005336 for skull-stripping, n\u2005=\u2005489 for tumor segmentation) with various PBT histologies were processed to train separate nnU-Net-based deep learning models for skull-stripping, whole tumor (WT), and enhancing tumor (ET) segmentation. These models utilized single (T2\/FLAIR) or multiple (T1-Gd and T2\/FLAIR) input imaging sequences. Performance was evaluated using Dice scores, sensitivity, and 95% Hausdorff distances. Statistical comparisons included paired or unpaired 2-sample t-tests and Pearson\u2019s correlation coefficient based on Dice scores from different models and PBT histologies.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Dice scores for the skull-stripping models for whole brain and sellar\/suprasellar region segmentation were 0.98\u2005\u00b1\u20050.01 (median 0.98) for both multi- and single-parametric models, with significant Pearson\u2019s correlation coefficient between single- and multi-parametric Dice scores (r\u2005&amp;gt;\u20050.80; P\u2005&amp;lt;\u2005.05 for all). Whole tumor Dice scores for single-input tumor segmentation models were 0.84\u2005\u00b1\u20050.17 (median\u2005=\u20050.90) for T2 and 0.82\u2005\u00b1\u20050.19 (median\u2005=\u20050.89) for FLAIR inputs. Enhancing tumor Dice scores were 0.65\u2005\u00b1\u20050.35 (median\u2005=\u20050.79) for T1-Gd+FLAIR and 0.64\u2005\u00b1\u20050.36 (median\u2005=\u20050.79) for T1-Gd+T2 inputs.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>Our skull-stripping models demonstrate excellent performance and include sellar\/suprasellar regions, using single- or multi-parametric inputs. Additionally, our automated tumor segmentation models can reliably delineate whole lesions and ET regions, adapting to MRI sessions with missing sequences in limited data context.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/noajnl\/vdae190","type":"journal-article","created":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T15:53:32Z","timestamp":1734018812000},"source":"Crossref","is-referenced-by-count":3,"title":["Automated pediatric brain tumor imaging assessment tool from CBTN: Enhancing suprasellar region inclusion and managing limited data with deep learning"],"prefix":"10.1093","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9479-5427","authenticated-orcid":false,"given":"Deep B","family":"Gandhi","sequence":"first","affiliation":[{"name":"Center for Data-Driven Discovery in Biomedicine (D3b), The Children\u2019s Hospital of Philadelphia , Philadelphia, Pennsylvania 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