Postoperative Karnofsky performance status prediction in patients with IDH wild-type glioblastoma: A multimodal approach integrating clinical and deep imaging features.

Journal: PloS one
PMID: 39527541

Abstract

BACKGROUND AND PURPOSE: Glioblastoma is a highly aggressive brain tumor with limited survival that poses challenges in predicting patient outcomes. The Karnofsky Performance Status (KPS) score is a valuable tool for assessing patient functionality and contributes to the stratification of patients with poor prognoses. This study aimed to develop a 6-month postoperative KPS prediction model by combining clinical data with deep learning-based image features from pre- and postoperative MRI scans, offering enhanced personalized care for glioblastoma patients.

Authors

  • Tomoki Sasagasako
    Department of Neurosurgery, Kyoto University Graduate School of Medicine, Kyoto, Japan.
  • Akihiko Ueda
    Department of Gynecology and Obstetrics, Kyoto University, Kyoto 606-8507, Japan.
  • Yohei Mineharu
    Department of Artificial Intelligence in Healthcare and Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan.
  • Yusuke Mochizuki
    Kyoto University Faculty of Medicine, Kyoto, Japan.
  • Souichiro Doi
    Kyoto University Faculty of Medicine, Kyoto, Japan.
  • Silsu Park
    Department of Neurosurgery, Kyoto University Graduate School of Medicine, Kyoto, Japan.
  • Yukinori Terada
    Department of Neurosurgery, Kyoto University Graduate School of Medicine, Kyoto, Japan.
  • Noritaka Sano
    Department of Neurosurgery, Kyoto University Graduate School of Medicine, Kyoto, Japan.
  • Masahiro Tanji
    Department of Neurosurgery, Kyoto University Graduate School of Medicine, Kyoto, Japan.
  • Yoshiki Arakawa
    Department of Neurosurgery, Kyoto University Graduate School of Medicine, Kyoto, Japan.
  • Yasushi Okuno
    Graduate School of Medicine, Kyoto University, Shogoin-kawaharacho, city/>Sakyo-ku Kyoto, 606-8507, Japan.

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