Yonghe Chen, Dan Liu, Kaikai Wei, Yi Lin, Zhong Wang, Qian Sun, Huashe Wang, Junsheng Peng, Lei Lian
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引用次数: 0
Abstract
Aims: This study aims to develop a simple, clinically applicable classification system to predict pCR based on carcinoembryonic antigen (CEA) trajectory during NAC.
Methods: This study included 366 AGC patients who received NAC followed by radical gastrectomy. CEA levels were measured before, during, and after NAC, with changes classified into three trajectory types: Type I (>=80% decline), Type II (>=40% but <80% decline), and Type III (<40% decline or increase). We analyzed associations between these CEA trajectories, pCR, lymph node remission, and survival.
Results: pCR was achieved in 10.4% (38/366) of patients. pCR rates were significantly higher in Type I (41%) and Type II (15.8%) trajectories compared to Type III (6.7%). Lymph node remission also correlated with CEA trajectories, with Type I having the highest proportion of ypN0 (79.2%). Multivariate analysis identified CEA trajectory subtypes and tumor differentiation as independent predictors of pCR. This classification system proved robust across subgroups. Although no significant differences in overall survival were observed between subtypes, higher initial CEA levels were associated with worse survival.
Conclusion: The trajectory of CEA change during NAC is a promising predictor of pCR in AGC. This simple and accessible classification system may facilitate personalized surgical strategies for patients with AGC.
期刊介绍:
Cancer Imaging and Diagnosis is dedicated to the publication of results from clinical and research studies applied to cancer diagnosis and treatment. The section aims to publish studies from the entire field of cancer imaging: results from routine use of clinical imaging in both radiology and nuclear medicine, results from clinical trials, experimental molecular imaging in humans and small animals, research on new contrast agents in CT, MRI, ultrasound, publication of new technical applications and processing algorithms to improve the standardization of quantitative imaging and image guided interventions for the diagnosis and treatment of cancer.