  Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer in which
the tumor-vascular involvement greatly affects the resectability and, thus,
overall survival of patients. However, current prognostic prediction methods
fail to explicitly and accurately investigate relationships between the tumor
and nearby important vessels. This paper proposes a novel learnable neural
distance that describes the precise relationship between the tumor and vessels
in CT images of different patients, adopting it as a major feature for
prognosis prediction. Besides, different from existing models that used CNNs or
LSTMs to exploit tumor enhancement patterns on dynamic contrast-enhanced CT
imaging, we improved the extraction of dynamic tumor-related texture features
in multi-phase contrast-enhanced CT by fusing local and global features using
CNN and transformer modules, further enhancing the features extracted across
multi-phase CT images. We extensively evaluated and compared the proposed
method with existing methods in the multi-center (n=4) dataset with 1,070
patients with PDAC, and statistical analysis confirmed its clinical
effectiveness in the external test set consisting of three centers. The
developed risk marker was the strongest predictor of overall survival among
preoperative factors and it has the potential to be combined with established
clinical factors to select patients at higher risk who might benefit from
neoadjuvant therapy.
