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

TL;DR

This paper introduces a novel deep anchor attention approach with Vision Transformer for predicting brain cancer survival from MRI scans, emphasizing the importance of inter-slice spatial diversity and critical slices.

Contribution

It proposes the Deep Anchor Attention Learning (DAAL) algorithm that weights slice representations based on trainable distances, improving survival prediction accuracy.

Findings

DAAL outperforms existing attention multiple instance learning methods.

Inter-slice spatial diversity correlates with disease severity.

Critical slices significantly influence survival prediction.

Abstract

Image-based brain cancer prediction models, based on radiomics, quantify the radiologic phenotype from magnetic resonance imaging (MRI). However, these features are difficult to reproduce because of variability in acquisition and preprocessing pipelines. Despite evidence of intra-tumor phenotypic heterogeneity, the spatial diversity between different slices within an MRI scan has been relatively unexplored using such methods. In this work, we propose a deep anchor attention aggregation strategy with a Vision Transformer to predict survival risk for brain cancer patients. A Deep Anchor Attention Learning (DAAL) algorithm is proposed to assign different weights to slice-level representations with trainable distance measurements. We evaluated our method on N = 326 MRIs. Our results outperformed attention multiple instance learning-based techniques. DAAL highlights the importance of critical slices and corroborates the clinical intuition that inter-slice spatial diversity can reflect disease severity and is implicated in outcome.