  Lymph node (LN) metastasis status is one of the most critical prognostic and
cancer staging factors for patients with resectable pancreatic ductal
adenocarcinoma (PDAC), or in general, for any types of solid malignant tumors.
Preoperative prediction of LN metastasis from non-invasive CT imaging is highly
desired, as it might be straightforwardly used to guide the following
neoadjuvant treatment decision and surgical planning. Most studies only capture
the tumor characteristics in CT imaging to implicitly infer LN metastasis and
very few work exploit direct LN's CT imaging information. To the best of our
knowledge, this is the first work to propose a fully-automated LN segmentation
and identification network to directly facilitate the LN metastasis status
prediction task. Nevertheless LN segmentation/detection is very challenging
since LN can be easily confused with other hard negative anatomic structures
(e.g., vessels) from radiological images. We explore the anatomical spatial
context priors of pancreatic LN locations by generating a guiding attention map
from related organs and vessels to assist segmentation and infer LN status. As
such, LN segmentation is impelled to focus on regions that are anatomically
adjacent or plausible with respect to the specific organs and vessels. The
metastasized LN identification network is trained to classify the segmented LN
instances into positives or negatives by reusing the segmentation network as a
pre-trained backbone and padding a new classification head. More importantly,
we develop a LN metastasis status prediction network that combines the
patient-wise aggregation results of LN segmentation/identification and deep
imaging features extracted from the tumor region. Extensive quantitative nested
five-fold cross-validation is conducted on a discovery dataset of 749 patients
with PDAC.
