Perfusion imaging in deep prostate cancer detection from mp-MRI: can we take advantage of it?

TL;DR

This study investigates the integration of perfusion imaging into deep neural networks for prostate cancer detection using mp-MRI, demonstrating that perfusion maps improve segmentation and grading accuracy over bi-parametric MRI models.

Contribution

It introduces methods to incorporate perfusion imaging into deep learning architectures for prostate cancer detection, highlighting the benefits of specific perfusion maps and fusion strategies.

Findings

Perfusion maps enhance prostate cancer segmentation accuracy.

Maximum slope perfusion maps improve grading performance.

mp-MRI models outperform bp-MRI models in this task.

Abstract

To our knowledge, all deep computer-aided detection and diagnosis (CAD) systems for prostate cancer (PCa) detection consider bi-parametric magnetic resonance imaging (bp-MRI) only, including T2w and ADC sequences while excluding the 4D perfusion sequence,which is however part of standard clinical protocols for this diagnostic task. In this paper, we question strategies to integrate information from perfusion imaging in deep neural architectures. To do so, we evaluate several ways to encode the perfusion information in a U-Net like architecture, also considering early versus mid fusion strategies. We compare performance of multiparametric MRI (mp-MRI) models with the baseline bp-MRI model based on a private dataset of 219 mp-MRI exams. Perfusion maps derived from dynamic contrast enhanced MR exams are shown to positively impact segmentation and grading performance of PCa lesions, especially the 3D MR volume corresponding to the maximum slope of the wash-in curve as well as Tmax perfusion maps. The latter mp-MRI models indeed outperform the bp-MRI one whatever the fusion strategy, with Cohen's kappa score of 0.318$\pm$0.019 for the bp-MRI model and 0.378 $\pm$ 0.033 for the model including the maximum slope with a mid fusion strategy, also achieving competitive Cohen's kappa score compared to state of the art.