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

• URL: http://arxiv.org/abs/2301.01448v1
• Date: Wed, 4 Jan 2023 05:14:31 GMT
• Title: A deep local attention network for pre-operative lymph node metastasis prediction in pancreatic cancer via multiphase CT imaging
• Authors: Zhilin Zheng, Xu Fang, Jiawen Yao, Mengmeng Zhu, Le Lu, Lingyun Huang, Jing Xiao, Yu Shi, Hong Lu, Jianping Lu, Ling Zhang, Chengwei Shao, Yun Bian
• Abstract summary: We propose a fully-automated LN segmentation and identification network to directly facilitate the LN metastasis status prediction task. We explore the anatomical spatial context priors of pancreatic LN locations by generating a guiding attention map from related organs and vessels. 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.
• Score: 22.57399272278884
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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