Published online Apr 15, 2020. doi: 10.4251/wjgo.v12.i4.447
Peer-review started: December 27, 2019
First decision: January 19, 2020
Revised: March 13, 2020
Accepted: March 25, 2020
Article in press: March 25, 2020
Published online: April 15, 2020
Processing time: 110 Days and 5.9 Hours
Gastric cancer (GC) is one of the most commonly diagnosed malignancies and the second leading cause of cancer-related deaths worldwide. The status of lymph node (LN) metastasis is an important prognostic factor in patients with GC. However, the evaluation of LN metastasis status in the preoperative setting is not accurate.
A few studies have been conducted to develop a nomogram for the prediction of LN metastasis in GC. However, a preoperative LN metastasis prediction model, based on the tumor metabolic information as measured by F-18 fluorodeoxyglucose (F-18 FDG) positron emission tomography/computed tomography (PET/CT) and laboratory findings, does not exist for GC. The purpose of this study was to develop a preoperative nomogram for LN metastasis in patients with GC.
This study aims to identify predictive factors and to develop a preoperative nomogram for the prediction of LN metastasis using F-18 FDG PET/CT and preoperative laboratory findings in patients with GC.
Between 2008 and 2010, a total of 566 GC patients who underwent preoperative F-18 FDG PET/CT and subsequent surgical treatment without any preoperative intervention were analyzed. The LN metastasis prediction model was developed in the training cohort (n = 377) and validated in the internal validation cohort (n = 189). Clinicopathological data were retrieved from the patients’ medical records and the F-18 FDG PET/CT images were retrospectively interpreted. Univariate and multivariable logistic regression was performed to validate the preoperative predictive factors for LN metastasis.
The multivariate logistic analysis showed that the combination of maximum standardized uptake value (SUVmax) of the primary tumor (T_SUVmax) and SUVmax of LN (N_SUVmax), serum albumin, and carbohydrate antigen (CA) 19-9 was the best model to predict the risk of LN metastasis. The preoperative nomogram for the prediction of LN metastasis using T_SUVmax, N_SUVmax, serum albumin, and CA 19-9 showed good performance in the validation cohort as well as the training cohort.
The combination of preoperative F-18 FDG PET/CT metabolic parameters (T_SUVmax and N_SUVmax) and laboratory findings (albumin and CA 19-9) could be a useful tool for LN metastasis assessment and treatment planning in patients with GC.
The preoperative nomogram for the prediction of LN should be verified on a larger and external validation cohort for widespread acceptance.