A new Nature Communications study introduced MRICombo, a deep-learning framework designed to unify MRI tumor workflows including segmentation, grading, staging, and malignancy detection. The aim is to reduce fragmentation across analyses that typically require separate tools and model handoffs. If validated across institutions and scanners, this kind of integrated pipeline could streamline radiology-to-oncology decision support. It also raises the bar for model evaluation that covers end-to-end performance rather than siloed outputs.