With the gradual development of neoadjuvant immunotherapy and/or chemotherapy in the clinic, the pCR has become more and more accessible in the AGC. Preoperative accurate prediction of pCR is of great clinical significance. The contrast-enhanced CT and 3.0T MRI were carried out in patients within 1 week prior to commencing neoadjuvant treatment, as well as 1 week within surgery after the completion of neoadjuvant treatment, respectively. Based on the information extracted from the CT/MRI, the clinical completed response (cCR) and the clinical T staging were compared with pCR, pathologic T staging. The pathologic results were considered as the golden standard. With the ROC curve analysis, the diagnosis coincidence rate, sensitivity and specificity were assessed. The AI prediction model would be constructed and trained. The depth convolution neural network based on contrast-enhanced CT and multi-modal MR quantitative images which can automatically mine key images characterization, combined with imaging features and histopathologic response, could further help to improve the prediction of response of gastric cancer treated with systematic therapy. The abdominal contrast-enhanced CT will focus on parameters: Local T Staging, nodal status, diameter, according to RECIST 1.1. MRI T2 (1-3mm slice as per NS Radiology protocol and ESGAR guideline) will focus on parameters: DWI & ADC value (preferably on a single camera with reproducible ADC value), Local T Staging, MRF involvement, EMVI, nodal status, MR volumetry, and desmoplastic reaction.
This is a prospective and observational clinical study for seeking out a better way to predict the pathologic complete response (pCR) in patients with advanced gastric cancer (AGC) based on the post-neoadjuvant treatment Magnetic Resonance Imaging (MRI) and CT data. This study will help the surgeons to better formulate treatment regimens for gastric cancer in the clinical practice.