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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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Tomoki Sasagasako,
Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Validation, Visualization, Writing – original draft
Affiliations Department of Neurosurgery, Kyoto University Graduate School of Medicine, Kyoto, Japan, Department of Biomedical Data Intelligence, Kyoto University Graduate School of Medicine, Kyoto, Japan
⨯ -
Akihiko Ueda,
Roles Data curation, Formal analysis, Methodology, Project administration, Software, Validation, Visualization
Affiliation Department of Biomedical Data Intelligence, Kyoto University Graduate School of Medicine, Kyoto, Japan
⨯ -
Yohei Mineharu ,
Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Validation, Writing – review & editing
* E-mail: mineharu@kuhp.kyoto-u.ac.jp
Affiliation Department of Artificial Intelligence in Healthcare and Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
⨯ - Yusuke Mochizuki,
- Souichiro Doi,
- Silsu Park,
- Yukinori Terada,
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Noritaka Sano,
Roles Project administration
Affiliation Department of Neurosurgery, Kyoto University Graduate School of Medicine, Kyoto, Japan
⨯ - Masahiro Tanji,
- Yoshiki Arakawa,
- Yasushi Okuno
https://orcid.org/0000-0003-0157-8093
Postoperative Karnofsky performance status prediction in patients with IDH wild-type glioblastoma: A multimodal approach integrating clinical and deep imaging features
- Tomoki Sasagasako,
- Akihiko Ueda,
- Yohei Mineharu,
- Yusuke Mochizuki,
- Souichiro Doi,
- Silsu Park,
- Yukinori Terada,
- Noritaka Sano,
- Masahiro Tanji,
- Yoshiki Arakawa
- Published: November 11, 2024
- https://doi.org/10.1371/journal.pone.0303002