{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T16:55:11Z","timestamp":1770396911412,"version":"3.49.0"},"reference-count":39,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T00:00:00Z","timestamp":1770336000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurol."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Magnetic resonance (MR) imaging is essential for diagnosing central nervous system (CNS) tumors, guiding surgical planning, treatment decisions, and assessing postoperative outcomes and complications. While recent work has advanced automated tumor segmentation and report generation, most efforts have focused on preoperative data, with limited attention to postoperative imaging analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study introduces a comprehensive pipeline for standardized postsurgical reporting in CNS tumors. Using the Attention U-Net architecture, segmentation models were trained, independently targeting the preoperative tumor core, non-enhancing tumor core, postoperative contrast-enhancing residual tumor, and resection cavity. In the process, the influence of varying MR sequence combinations was assessed. Additionally, MR sequence classification and tumor type identification for contrast-enhancing lesions were explored using the DenseNet architecture. The models were integrated seamlessly into an automated and standardized reporting pipeline, following the RANO 2.0 guidelines. Training was conducted on multicentric datasets comprising 2000 to 7000 patients, incorporating both private and public data, using a 5-fold cross-validation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Evaluation included patient-, voxel-, and object-wise metrics, with benchmarking against the latest BraTS challenge results. The segmentation models achieved average voxel-wise Dice scores of 87%, 66%, 70%, and 77% for the tumor core, non-enhancing tumor core, contrast-enhancing residual tumor, and resection cavity, respectively. Classification models reached 99.5% balanced accuracy in MR sequence classification and 80% in tumor type classification.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>The pipeline presented in this study enables robust, automated segmentation, MR sequence classification, and standardized report generation aligned with RANO 2.0 guidelines, enhancing postoperative evaluation and clinical decision-making. The proposed models and methods were integrated into Raidionics, open-source software platform for CNS tumor analysis, now including a dedicated module for postsurgical analysis.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fneur.2025.1707481","type":"journal-article","created":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T05:22:40Z","timestamp":1770355360000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Automatic and standardized reporting of perioperative MRIs in patients with central nervous system tumors"],"prefix":"10.3389","volume":"16","author":[{"given":"David","family":"Bouget","sequence":"first","affiliation":[{"name":"Department of Health Research, SINTEF Digital","place":["Trondheim, Norway"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mathilde Gajda","family":"Faanes","sequence":"additional","affiliation":[{"name":"Department of Health Research, SINTEF Digital","place":["Trondheim, Norway"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Asgeir Store","family":"Jakola","sequence":"additional","affiliation":[{"name":"Department of Clinical Neuroscience, University of Gothenburg","place":["Gothenburg, Sweden"]},{"name":"Department of Neurosurgery, Sahlgrenska University Hospital","place":["Gothenburg, Sweden"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Frederik","family":"Barkhof","sequence":"additional","affiliation":[{"name":"Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Vrije Universiteit","place":["Amsterdam, Netherlands"]},{"name":"Institutes of Neurology and Healthcare Engineering, University College London","place":["London, United Kingdom"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hilko","family":"Ardon","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Elisabeth-TweeSteden Hospital","place":["Tilburg, Netherlands"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lorenzo","family":"Bello","sequence":"additional","affiliation":[{"name":"Neurosurgical Oncology Unit, Department of Oncology and Hemato-oncology, Humanitas Research Hospital","place":["Milano, Italy"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mitchel S.","family":"Berger","sequence":"additional","affiliation":[{"name":"Department of Neurological Surgery, University of California","place":["San Francisco, San Francisco, CA, United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shawn L.","family":"Hervey-Jumper","sequence":"additional","affiliation":[{"name":"Department of Neurological Surgery, University of California","place":["San Francisco, San Francisco, CA, United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Julia","family":"Furtner","sequence":"additional","affiliation":[{"name":"Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna","place":["Wien, Austria"]},{"name":"Research Center for Medical Image Analysis and Artificial Intelligence, Faculty of Medicine and Dentistry","place":["Krems, Austria"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Albert J. 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