LNMER-Net: A Metabolically Enhanced Lymph Node Metastasis Recognition Model Based on Lung Lymph Nodes and Microenvironment

PET/CT is the preferred device for lung cancer and lymph node metastasis diagnosis, and mining effective features from PET/CT images to identify lung lymph node metastasis has important research significance and application value. Multi-phase PET/CT has temporal properties that can better represent changes in lesions’ structural and metabolic properties. Early-phase PET images can show a wide range of lesion areas. Delayed-phase PET images can show the high uptake properties of 18F-FDG in malignant tumor cells. Thus, multi-phase PET represents the variability of benign/malignant lesions better in the temporal dimension. This paper first proposes a metabolic enhancement method for lung lymph nodes and their microenvironment, a lymph node metastasis recognition network (LNMER-Net). The network has three branches: multi-modal early-phase feature fusion channel, multi-modal delayed-phase feature fusion channel, and single-modal metabolic decay channel. To enhance the feature of the lymph node region, a multi-receptive field-based feature extraction and feature space optimization (MRFO) method is proposed to extract lymph node features by multi-scale convolution operations and embed them in the multi-modal fusion channel. To exploit the information on the metabolic changes of the lesion in the early-phase and delayed-phase, differential results of the multi-phase PET images are fed into the single-modal metabolic decay channel to enhance the microenvironmental features. To verify its effectiveness, a multi-phase PET/CT dataset from China Medical University is used. The proposed method achieves 84.5%/82.9% in Accuracy/Recall, which is better than SOTA methods such as Res2Net, Comformer, and NextViT.