New imaging research points to whole-body diffusion-weighted MRI as a predictive tool after neoadjuvant chemotherapy for advanced ovarian cancer. In a British Journal of Cancer study, the investigators evaluated WB-DWI/MRI for forecasting treatment outcomes, aligning systemic diffusion imaging with clinical decision-making. In parallel, a distributed computing approach is reported for breast cancer recurrence prediction using a distributed fusion framework with MapReduce and a tailored machine-learning pipeline. The work aims to improve prognostic modeling by leveraging distributed processing for large, heterogeneous datasets. Both updates highlight growing emphasis on data integration—radiology-derived biomarkers and scalable analytics—to sharpen risk stratification beyond single-modality signals.