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Image-based brain cancer prediction models, based on radiomics, quantify theradiologic phenotype from magnetic resonance imaging (MRI). However, thesefeatures are difficult to reproduce because of variability in acquisition andpreprocessing pipelines. Despite evidence of intra-tumor phenotypicheterogeneity, the spatial diversity between different slices within an MRIscan has been relatively unexplored using such methods. In this work, wepropose a deep anchor attention aggregation strategy with a Vision Transformerto predict survival risk for brain cancer patients. A Deep Anchor AttentionLearning (DAAL) algorithm is proposed to assign different weights toslice-level representations with trainable distance measurements. We evaluatedour method on N = 326 MRIs. Our results outperformed attention multipleinstance learning-based techniques. DAAL highlights the importance of criticalslices and corroborates the clinical intuition that inter-slice spatialdiversity can reflect disease severity and is implicated in outcome.

Brain Cancer Survival Prediction on Treatment-na ive MRI using Deep Anchor Attention Learning with Vision Transformer