Dual-energy CT–based deep learning radiomics can improve lymph node metastasis risk prediction for gastric cancer

Objectives: To build a dual-energy CT (DECT)–based deep learning radiomics nomogram for lymph node metastasis (LNM) prediction in gastric cancer. Materials and methods: Preoperative DECT images were retrospectively collected from 204 pathologically confirmed cases of gastric adenocarcinoma (mean age, 58 years; range, 28–81 years; 157 men [mean age, 60 years; range, 28–81 years] and 47 women [mean age, 54 years; range, 28–79 years]) between November 2011 and October 2018, They were divided into training (n = 136) and test (n = 68) sets. Radiomics features were extracted from monochromatic images at arterial phase (AP) and venous phase (VP). Clinical information, CT parameters, and follow-up data were collected. A radiomics nomogram for LNM prediction was built using deep learning approach and evaluated in test set using ROC analysis. Its prognostic performance was determined with Harrell’s concordance index (C-index) based on patients’ outcomes. Results: The dual-energy CT radiomics signature was associated with LNM in two sets (Mann-Whitney U test, p < 0.001) and an achieved area under the ROC curve (AUC) of 0.71 for AP and 0.76 for VP in test set. The nomogram incorporated the two radiomics signatures and CT-reported lymph node status exhibited AUCs of 0.84 in the training set and 0.82 in the test set. The C-indices of the nomogram for progression-free survival and overall survival prediction were 0.64 (p = 0.004) and 0.67 (p = 0.002). Conclusion: The DECT-based deep learning radiomics nomogram showed good performance in predicting LNM in gastric cancer. Furthermore, it was significantly associated with patients’ prognosis. Key Points: • This study investigated the value of deep learning dual-energy CT–based radiomics in predicting lymph node metastasis in gastric cancer. • The dual-energy CT–based radiomics nomogram outweighed the single-energy model and the clinical model. • The nomogram also exhibited a significant prognostic ability for patient survival and enriched radiomics studies.