{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T12:21:26Z","timestamp":1766578886493},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685366","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T00:00:00Z","timestamp":1724976000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,8,30]]},"abstract":"<jats:p>Introduction: Glioblastoma (GB) is one of the most aggressive tumors of the brain. Despite intensive treatment, the average overall survival (OS) is 15\u201318 months. Therefore, it is helpful to be able to assess a patient\u2019s OS to tailor treatment more specifically to the course of the disease. Automated analysis of routinely generated MRI sequences (FLAIR, T1, T1CE, and T2) using deep learning-based image classification has the potential to enable accurate OS predictions. Methods: In this work, a method was developed and evaluated that classifies the OS into three classes \u2013 \u201cshort\u201d, \u201cmedium\u201d and \u201clong\u201d. For this purpose, the four MRI sequences of a person were corrected using bias-field correction and merged into one image. The pipeline was realized by a bagging model using 5-fold cross-validation and the ResNet50 architecture. Results: The best model was able to achieve an F1-score of 0.51 and an accuracy of 0.67. In addition, this work enabled a largely clear differentiation of the \u201cshort\u201d and \u201clong\u201d classes, which offers high clinical significance as decision support. Conclusion: Automated analysis of MRI scans using deep learning-based image classification has the potential to enable accurate OS prediction in glioblastomas.<\/jats:p>","DOI":"10.3233\/shti240878","type":"book-chapter","created":{"date-parts":[[2024,9,5]],"date-time":"2024-09-05T09:14:58Z","timestamp":1725527698000},"source":"Crossref","is-referenced-by-count":4,"title":["Predicting Overall Survival of Glioblastoma Patients Using Deep Learning Classification Based on MRIs"],"prefix":"10.3233","author":[{"given":"Katharina","family":"Ott","sequence":"first","affiliation":[{"name":"IT-Infrastructure for Translational Medical Research, University of Augsburg, Germany"}]},{"given":"Santiago","family":"Cepeda","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, R\u00edo Hortega University Hospital, Spain"}]},{"given":"Dennis","family":"Hartmann","sequence":"additional","affiliation":[{"name":"IT-Infrastructure for Translational Medical Research, University of Augsburg, Germany"}]},{"given":"Frank","family":"Kramer","sequence":"additional","affiliation":[{"name":"IT-Infrastructure for Translational Medical Research, University of Augsburg, Germany"}]},{"given":"Dominik","family":"M\u00fcller","sequence":"additional","affiliation":[{"name":"IT-Infrastructure for Translational Medical Research, University of Augsburg, Germany"},{"name":"Institute for Digital Medicine, University Hospital Augsburg, Germany"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","German Medical Data Sciences 2024"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI240878","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,5]],"date-time":"2024-09-05T09:14:59Z","timestamp":1725527699000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI240878"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,30]]},"ISBN":["9781643685366"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti240878","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,30]]}}}