Accurate breast tumor segmentation in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is important for downstream tasks such as treatment planning and pathological complete response (pCR) assessment. In this work, we address both segmentation and pCR classification using the large-scale MAMA-MIA DCE-MRI dataset. We employ a large-kernel MedNeXt architecture with a two-stage training strategy that expands the receptive field from \(3 \times 3 \times 3\) to \(5 \times 5 \times 5\) kernels using the UpKern algorithm. This approach allows stable transfer of learned features to larger kernels, improving segmentation performance on the unseen validation set. An ensemble of large-kernel models achieved a Dice score of 0.67 and a normalized Hausdorff Distance (NormHD) of 0.24. For pCR classification, we trained a self-normalizing network (SNN) on radiomic features extracted from the predicted segmentations and first post-contrast DCE-MRI, reaching an average balanced accuracy of 57%, and up to 75% in some subgroups. Our findings highlight the benefits of combining larger receptive fields and radiomics-driven classification while motivating future work on advanced ensembling and the integration of clinical variables to further improve performance and generalization. (Code available at: https://github.com/toufiqmusah/caladan-mama-mia.git ).