| Abstract: |
Molecular breast cancer sub-types derived from core-biopsy are central for individual outcome prediction and treatment decisions. Determining sub-types by non-invasive imaging procedures would benefit early assessment. Furthermore, identifying phenotypic traits of sub-types may inform our understanding of disease processes as we become able to monitor them longitudinally. We propose a model to learn phenotypic appearance concepts of four molecular sub-types of breast cancer. A deep neural network classification model predicts sub-types from multi-modal, multi-parametric imaging data. Intermediate representations of the visual information are clustered, and clusters are scored based on testing with concept activation vectors to assess their contribution to correctly discriminating sub-types. The proposed model can predict sub-types with competitive accuracy from simultaneous18 F-FDG PET/MRI, and identifies visual traits in the form of shared and discriminating phenotypic concepts associated with the sub-types. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. |
