  The reliable and timely stratification of bone lesion evolution risk in
smoldering Multiple Myeloma plays an important role in identifying prime
markers of the disease's advance and in improving the patients' outcome. In
this work we provide an asymmetric cascade network for the longitudinal
prediction of future bone lesions for T1 weighted whole body MR images. The
proposed cascaded architecture, consisting of two distinct configured U-Nets,
first detects the bone regions and subsequently predicts lesions within bones
in a patch based way. The algorithm provides a full volumetric risk score map
for the identification of early signatures of emerging lesions and for
visualising high risk locations. The prediction accuracy is evaluated on a
longitudinal dataset of 63 multiple myeloma patients.
