Deep learning-based radiomics does not improve residual cancer burden prediction post-chemotherapy in LIMA breast MRI trial

Question It is unknown if and which deep radiomics approach is most suitable to extract relevant features to assess neoadjuvant chemotherapy response on breast MRI. Findings Radiomic features extracted from deep-learning networks yield similar results in predicting neoadjuvant chemotherapy response as tumor volume and subtype in the LIMA study. However, they do not provide complementary information. Clinical relevance For predicting response to neoadjuvant chemotherapy in breast cancer patients, tumor volume on MRI and subtype remain important predictors of treatment outcome; deep radiomics might be an alternative when determining tumor volume and/or subtype is not feasible.