Revealing Hidden Potentials of the q-Space Signal in Breast Cancer
This work addresses the issue of unnecessary biopsies in breast cancer detection for patients and clinicians, representing an incremental advance over existing methods.
The paper tackled the problem of high false positive rates in mammography screening for breast cancer by developing a CNN-based method that integrates all data processing steps to optimize image normalization, signal exploitation, and classification, resulting in significant improvements in clinical decision-making using a dataset of 222 patients.
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.