Objective: To investigate the diagnostic value of radiomics features derived from iodine map of dual energy CT(DECT) in diagnosing lateral cervical lymph node metastasis(LLNM) in patients of papillary thyroid cancer(PTC). Methods: A retrospective analysis was performed on the clinical and imaging data of 200 patients with PTC who underwent preoperative DECT at the First Affiliated Hospital of Nanjing Medical University between June 2022 and June 2024, all with pathological confirmation. A total of 777 lymph nodes were included, comprising 379 metastatic lymph nodes and 398 non-metastatic lymph nodes. Patients were randomly allocated into the training and validation sets at a ratio of 7:3. Clinical and CT features of lymph nodes, including size, shape, margin, enhancement degree, calcification, cystic change, and extranodal extension were included. Quantitative DECT parameters including iodine concentration, normalized IC, effective atomic number, normalized Zeff and slope of energy spectrum curve in arterial and venous phase. Radiomics features were manually extracted from arterial and venous phase iodine maps by sketching region of interest. Results: All clinical and DECT features showed significant differences between LLNM and non-LLNM (all P < 0.05) except for extranodal extension. Compared with the conventional imaging model[training set, area under curve(AUC)=0.803, 95% CI: 0.765-0.841; validation set, AUC=0.804,95% CI: 0.748-0.861], the DECT quantitative parameter model(training set, AUC=0.939,95% CI: 0.918-0.960; validation set, AUC=0.933, 95% CI: 0.901-0.965), radiomics model(training set, AUC=0.977, 95% CI: 0.966-0.988; validation set, AUC=0.931, 95% CI: 0.900-0.962) and combined model(training set, AUC=0.977,95% CI: 0.965-0.988; validation set, AUC=0.938,95% CI:0.908-0.968)showed excellent diagnostic performance in both training and validation set(P < 0.05). In the training set, radiomics model was superior to DECT quantitative parameter model (P < 0.01), while no significant difference was observed between the radiomics and combined models(P=0.950). Conclusion: Radiomics features extracted from iodine mapping extraction have high diagnostic value in diagnosing LLNM in PTC patients.