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In glioblastoma, an aggressive brain tumor with a low survival rate, accurately predicting patient survival is crucial for effective treatment planning. For a deep learning model developed using MRI images for glioblastoma prognosis, it’s essential to identify which MRI sequence – T1, T2, T1-contrast enhanced, or FLAIR – yields the most insightful prognostic features. This study utilizes a specialized autoencoder architecture, specifically a Unet model with an attention mechanism equipped with a custom-designed loss function for reconstructing tumor subregions from multimodal MRI scans. Once the autoencoder is trained, it extracts unique imaging features. Subsequently, these extracted features are analyzed using a Cox regression neural network, which is then applied to predict the survival probabilities of glioblastoma patients. The method was applied based on 5-fold cross-validation. The survival prediction performance of the model was assessed using various MRI sequences. The concordance index (C-index) values obtained were 0.62 for T1, 0.63 for T1CE, 0.63 for T2, and 0.66 for FLAIR. Additionally, the area under the curve (AUC) values recorded were 0.76 for T1, 0.74 for T1CE, 0.76 for T2, and 0.82 for FLAIR, indicating a variation in predictive accuracy across different sequences. The findings from our study underscore a discernible variation in the effectiveness of different MRI sequences for glioblastoma survival prognosis. Notably, the FLAIR sequence emerged as the most informative, providing the highest level of prognostic detail in comparison to T1, T1CE, and T2 sequences. This highlights the significance of sequence selection in enhancing the accuracy of survival predictions in glioblastoma cases.
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