10075-CO-3 PREDICTION OF KPS DETERIORATION IN GLIOBLASTOMA USING MULTIMODAL LEARNING WITH PRE- AND POST-OPERATIVE MRI IMAGES AND CLINICAL INFORMATION

Abstract

Abstract BACKGROUND Previous AI studies on glioblastoma have mostly focused on predicting genetic subtypes or survival outcomes using preoperative imaging alone. We aimed to investigate the feasibility of utilizing postoperative imaging in combination with clinical chart data for multimodal learning, to evaluate systemic functional outcome. METHODS We included 77 glioblastoma cases that received treatment at Kyoto University between December 2001 and January 2022. These cases had both pre- and postoperative MRI images, including T1WI, CE-T1WI, FLAIR, ADC, and DWI. Molecular diagnoses were confirmed for all cases. First, using external data (BRaTS2020), we constructed models for tumor segmentation prediction from MRI images followed by an image feature extraction model using an autoencoder (VAE), extracting 192 image feature variables from preoperative and postoperative images. These variables were combined with 24 clinical variables, and 6 variables of radiologists' findings. We built a CNN model to predict a decrease of 30% or more KPS at 6 months and evaluated its accuracy using the AUC. Feature importance was calculated using PermutationImportance. RESULTS When predicting KPS deterioration using only clinical data, the AUC was 0.79. When combined with radiologists' findings, the AUC increased to 0.84. Combination with preoperative image features resulted in an AUC of 0.78. On the other hand, when combined with both pre- and postoperative image features, the AUC increased to 0.94. Both pre- and post-operative images were identified as important features. DISCUSSION Our findings demonstrate the utility of incorporating postoperative imaging into multimodal learning for predicting systemic functional outcomes in glioblastoma patients. Further validation in independent cases from different medical facilities is warranted to confirm these results.