This repository plays a pivotal role in our research paper titled "PET/CT-based Cross-Modal Deep Learning Signature for Predicting Occult Nodal Metastasis in Lung Cancer." The raw code underlying our manuscript, which has been submitted to Nature Communications, is organized into five distinct components: AUCMloss, Focalloss, datasets_auc, train_kornia, and utils. You can delve into the specifics of these five parts by examining the corresponding files, which are named "NC_codes_AUCMloss.py," "NC_codes_Focalloss.py," "NC_codes_datasets_auc.py," "NC_codes_train_kornia.py," and "NC_codes_utils.py." Moreover, our deep learning signature is trained using a pre-existing ResNet-18 architecture as its foundation. You can gain a comprehensive understanding of this pre-trained ResNet-18 architecture by referring to the files labeled "resnet18_pretrain.part1.rar" and "resnet18_pretrain.part2.rar." These resources contain the intricate details that are integral to our research findings.