Deep Attention Learning for Pre-operative Lymph Node Metastasis Prediction in Pancreatic Cancer via Multi-object Relationship Modeling

Abstract

Lymph node (LN) metastasis status is one of the most critical prognostic and cancer staging clinical factors for patients with resectable pancreatic ductal adenocarcinoma (PDAC, generally for any types of solid malignant tumors). Pre-operative prediction of LN metastasis from non-invasive CT imaging is highly desired, as it might be directly and conveniently used to guide the follow-up neoadjuvant treatment decision and surgical planning. Most previous studies only use the tumor characteristics in CT imaging alone to implicitly infer LN metastasis. 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 for patients with PDAC. Specially, (1) 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. (2) 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. (3) Importantly, we develop a LN metastasis status prediction network that combines and aggregates the holistic patient-wise diagnosis information of both LN segmentation/identification and deep imaging characteristics by the PDAC tumor region. Extensive quantitative nested five-fold cross-validation is conducted on a discovery dataset of 749 patients with PDAC. External multi-center clinical evaluation is further performed on two other hospitals of 191 total patients. Our multi-staged LN metastasis status prediction network statistically significantly outperforms strong baselines of nnUNet and several other compared methods, including CT-reported LN status, radiomics, and deep learning models.