Revealing Hidden Potentials of the q-Space Signal in Breast Cancer

TL;DR

This paper introduces a CNN-based method that leverages the full q-space signal in diffusion-weighted MRI to improve breast cancer detection, reducing false positives and enhancing clinical decision accuracy.

Contribution

It presents a novel deep learning framework that integrates all processing steps for q-space data, outperforming traditional biophysical models in breast cancer diagnosis.

Findings

Significant improvement in diagnostic accuracy over existing methods.

Effective reduction of false positive biopsy recommendations.

Robust performance across multicentric data sets.

Abstract

Mammography screening for early detection of breast lesions currently suffers from high amounts of false positive findings, which result in unnecessary invasive biopsies. Diffusion-weighted MR images (DWI) can help to reduce many of these false-positive findings prior to biopsy. Current approaches estimate tissue properties by means of quantitative parameters taken from generative, biophysical models fit to the q-space encoded signal under certain assumptions regarding noise and spatial homogeneity. This process is prone to fitting instability and partial information loss due to model simplicity. We reveal unexplored potentials of the signal by integrating all data processing components into a convolutional neural network (CNN) architecture that is designed to propagate clinical target information down to the raw input images. This approach enables simultaneous and target-specific optimization of image normalization, signal exploitation, global representation learning and classification. Using a multicentric data set of 222 patients, we demonstrate that our approach significantly improves clinical decision making with respect to the current state of the art.

Figures

Full text

See pages 1-last of miccai_paper_ver3.pdf