Abstract
Background This study aimed to explore the value of gastric cancer (GC) tumor markers and dual-energy CT（DECT） scans of arterial and venous GC lesions with quantitative iodine concentration (IC) and radiomics, to predict lymph node metastasis (LNM) in patients with GC. Methods This prospective study comprised of 177 patients that underwent dual-energy CT scans before surgery, and were subsequently diagnosed with GC by postoperative pathology. Serum tumor markers and arterial phases (AP) and venous phases (VP) of GC lesion iodine concentration (IC) and normalized iodine concentration (nIC) were analyzed. Patients were divided into either the LNM group or non-LNM group according to pathological results. The Wilcoxon rank-sum test was used to compare the serum tumor markers, IC, and nIC of the 2 groups, and a ROC curve was drawn to evaluate their effectiveness in predicting LNM in patients with GC. After using the Siemens syngo.via Frontier Radiomics software to extract radiomics features , all patients were randomly divided into a train set and a test set with a 7:3 ratio to predict GC LNM. Results Among the 177 patients with GC, 83 were diagnosed with LNM, while 94 did not have LNM. The preoperative serum tumor markers CA125, CA199, and CEA were statistically significant for predicting the presence of LNM (P<0.05). In the transfer group, AP and VP IC were 2.63 mg/ml (2.3, 3.00) and 3.60 mg/ml (3.23, 4.03) respectively, with corresponding areas under the curve (AUC) of 0.83 and 0.91. The nIC was 0.18 mg/ml (0.15, 0.21) and 0.78 mg/ml (0.65, 0.86); the AUC curve was 0.79 and 0.87. Both the IC and nIC were higher in the patients with LNM than those without LNM (P<0.05). Establishing a random forest (RF) model based on the radiomics extracted from the GC lesions had a high diagnostic value in predicting whether the lymph nodes in patients with GC were metastatic. The RF model AUC value was 0.959 for the train set and 0.977 for the test set. The AUC value of the nomogram predicting LNM was 0.996 for the train set and 0.976 for the test set. Conclusion Models based on preoperative serum tumor markers (CA125, CA199, and CEA) in patients with GC, quantitative dual-energy CT parameter values of the lesions (AP and VP IC, nIC), and radiomics have a higher diagnosis of LNM. The value of nomogram in combination with multi-parameter analysis is higher diagnosis of LNM, which can provide a reliable basis for preoperative evaluation of LNM.
Comparison of virtual non-contrast dual-energy CT and a true non-contrast CT for contouring in radiotherapy of 3D printed lung tumour models in motion: a phantom study
