  Intraductal Papillary Mucinous Neoplasm (IPMN) cysts are pre-malignant
pancreas lesions, and they can progress into pancreatic cancer. Therefore,
detecting and stratifying their risk level is of ultimate importance for
effective treatment planning and disease control. However, this is a highly
challenging task because of the diverse and irregular shape, texture, and size
of the IPMN cysts as well as the pancreas. In this study, we propose a novel
computer-aided diagnosis pipeline for IPMN risk classification from
multi-contrast MRI scans. Our proposed analysis framework includes an efficient
volumetric self-adapting segmentation strategy for pancreas delineation,
followed by a newly designed deep learning-based classification scheme with a
radiomics-based predictive approach. We test our proposed decision-fusion model
in multi-center data sets of 246 multi-contrast MRI scans and obtain superior
performance to the state of the art (SOTA) in this field. Our ablation studies
demonstrate the significance of both radiomics and deep learning modules for
achieving the new SOTA performance compared to international guidelines and
published studies (81.9\% vs 61.3\% in accuracy). Our findings have important
implications for clinical decision-making. In a series of rigorous experiments
on multi-center data sets (246 MRI scans from five centers), we achieved
unprecedented performance (81.9\% accuracy).
