Postoperative Karnofsky performance status prediction in patients with IDH wild-type glioblastoma: A multimodal approach integrating clinical and deep imaging features.
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| Title: | Postoperative Karnofsky performance status prediction in patients with IDH wild-type glioblastoma: A multimodal approach integrating clinical and deep imaging features. |
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| Authors: | Sasagasako T; Department of Neurosurgery, Kyoto University Graduate School of Medicine, Kyoto, Japan.; Department of Biomedical Data Intelligence, Kyoto University Graduate School of Medicine, Kyoto, Japan., Ueda A; Department of Biomedical Data Intelligence, Kyoto University Graduate School of Medicine, Kyoto, Japan., Mineharu Y; Department of Artificial Intelligence in Healthcare and Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan., Mochizuki Y; Kyoto University Faculty of Medicine, Kyoto, Japan., Doi S; Kyoto University Faculty of Medicine, Kyoto, Japan., Park S; Department of Neurosurgery, Kyoto University Graduate School of Medicine, Kyoto, Japan., Terada Y; Department of Neurosurgery, Kyoto University Graduate School of Medicine, Kyoto, Japan., Sano N; Department of Neurosurgery, Kyoto University Graduate School of Medicine, Kyoto, Japan., Tanji M; Department of Neurosurgery, Kyoto University Graduate School of Medicine, Kyoto, Japan., Arakawa Y; Department of Neurosurgery, Kyoto University Graduate School of Medicine, Kyoto, Japan., Okuno Y; Department of Biomedical Data Intelligence, Kyoto University Graduate School of Medicine, Kyoto, Japan.; Department of Artificial Intelligence in Healthcare and Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan. |
| Source: | PloS one [PLoS One] 2024 Nov 11; Vol. 19 (11), pp. e0303002. Date of Electronic Publication: 2024 Nov 11 (Print Publication: 2024). |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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