Evaluating the Impact of Sequence Combinations on Breast Tumor Segmentation in Multiparametric MRI

TL;DR

This study investigates how different combinations of mpMRI sequences affect deep learning-based breast tumor segmentation, revealing that DCE sequences are most effective and T2w adds limited benefit, guiding future research in treatment response prediction.

Contribution

It systematically evaluates the impact of various mpMRI sequence combinations on tumor segmentation accuracy using nnU-Net, highlighting the importance of DCE sequences and the limited role of T2w.

Findings

DCE sequences achieved a DSC of 0.69 for FTV segmentation.

Adding FTV to DWI and ADC improved DSC from 0.57 to 0.60.

T2w did not significantly improve segmentation performance.

Abstract

Multiparametric magnetic resonance imaging (mpMRI) is a key tool for assessing breast cancer progression. Although deep learning has been applied to automate tumor segmentation in breast MRI, the effect of sequence combinations in mpMRI remains under-investigated. This study explores the impact of different combinations of T2-weighted (T2w), dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) map on breast tumor segmentation using nnU-Net. Evaluated on a multicenter mpMRI dataset, the nnU-Net model using DCE sequences achieved a Dice similarity coefficient (DSC) of 0.69 $\pm$ 0.18 for functional tumor volume (FTV) segmentation. For whole tumor mask (WTM) segmentation, adding the predicted FTV to DWI and ADC map improved the DSC from 0.57 $\pm$ 0.24 to 0.60 $\pm$ 0.21. Adding T2w did not yield significant improvement, which still requires further investigation under a more standardized imaging protocol. This study serves as a foundation for future work on predicting breast cancer treatment response using mpMRI.