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.

Materials and Methods Using 1,476 MRI datasets from the Brain Tumor Segmentation Challenge 2020 public database, we pretrained two variational autoencoders (VAEs). Imaging features from the latent spaces of the VAEs were used for KPS prediction. Neural network-based KPS prediction models were developed to predict scores below 70 at 6 months postoperatively. In this retrospective single-center analysis, we incorporated clinical parameters and pre- and postoperative MRI images from 150 newly diagnosed IDH wild-type glioblastoma, divided into training (100 patients) and test (50 patients) sets. In training set, the performance of these models was evaluated using the area under the curve (AUC), calculated through fivefold cross-validation repeated 10 times. The final evaluation of the developed models assessed in the test set.

Results Among the 150 patients, 61 had 6-month postoperative KPS scores below 70 and 89 scored 70 or higher. We developed three models: a clinical-based model, an MRI-based model, and a multimodal model that incorporated both clinical parameters and MRI features. In the training set, the mean AUC was 0.785±0.051 for the multimodal model, which was significantly higher than the clinical-based model (0.716±0.059, P=0.038) using only clinical parameters and MRI-based model (0.651±0.028, P<0.001) using only MRI features. In the test set, the multimodal model achieved an AUC of 0.810, outperforming the clinical-based (0.670) and MRI-based (0.650) models.

Conclusion The integration of MRI features extracted from VAEs with clinical parameters in the multimodal model substantially enhanced KPS prediction performance. This approach has the potential to improve prognostic prediction, paving the way for more personalized and effective treatments for patients with glioblastoma.

The authors have declared no competing interest.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The Ethics Committee of Kyoto University Hospital approved this study (R2088).

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Abbreviations: KPS, Karnofsky performance status. IDH, isocitrate dehydrogenase. VAE, variational autoencoder. BraTS, Brain Tumor Segmentation challenge

Disclosure of potential conflicts of interest: The authors declare no conflicts of interest related to the content of this article.

The clinical data in this study cannot be shared publicly because it contains potentially identifying or sensitive patient information. However, the clinical data are available, on reasonable request, from the corresponding author.

https://github.com/TomokiSasagasako/GBM_KPS_prediction.git