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Lymph node (LN) metastasis status is one of the most critical prognostic andcancer staging factors for patients with resectable pancreatic ductaladenocarcinoma (PDAC), or in general, for any types of solid malignant tumors.Preoperative prediction of LN metastasis from non-invasive CT imaging is highlydesired, as it might be straightforwardly used to guide the followingneoadjuvant treatment decision and surgical planning. Most studies only capturethe tumor characteristics in CT imaging to implicitly infer LN metastasis andvery few work exploit direct LN's CT imaging information. To the best of ourknowledge, this is the first work to propose a fully-automated LN segmentationand identification network to directly facilitate the LN metastasis statusprediction task. Nevertheless LN segmentation/detection is very challengingsince LN can be easily confused with other hard negative anatomic structures(e.g., vessels) from radiological images. We explore the anatomical spatialcontext priors of pancreatic LN locations by generating a guiding attention mapfrom related organs and vessels to assist segmentation and infer LN status. Assuch, LN segmentation is impelled to focus on regions that are anatomicallyadjacent or plausible with respect to the specific organs and vessels. Themetastasized LN identification network is trained to classify the segmented LNinstances into positives or negatives by reusing the segmentation network as apre-trained backbone and padding a new classification head. More importantly,we develop a LN metastasis status prediction network that combines thepatient-wise aggregation results of LN segmentation/identification and deepimaging features extracted from the tumor region. Extensive quantitative nestedfive-fold cross-validation is conducted on a discovery dataset of 749 patientswith PDAC.

A deep local attention network for pre-operative lymph node metastasis prediction in pancreatic cancer via multiphase CT imaging