Radiomics signature for predicting postoperative disease-free survival of patients with gastric cancer: development and validation of a predictive nomogram.

Abstract

PURPOSE Radiomics can be used to determine the prognosis of gastric cancer (GC). The objective of this study was to predict the disease-free survival (DFS) after GC surgery based on computed tomography-enhanced images combined with clinical features. METHODS Clinical, imaging, and pathological data of patients who underwent gastric adenocarcinoma resection from June 2015 to May 2019 were retrospectively analyzed. The primary outcome was DFS. Radiomics features were selected using Least Absolute Shrinkage and Selection Operator algorithm and converted into the Rad-score. A nomogram was constructed based on the Radscore and other clinical factors. The Rad-score and nomogram were validated in the training and validation groups. RESULTS Totally, 179 patients were randomly divided into the training (n=124) and validation (n=55) groups. In the training group, validation group, and overall population, the Rad-score could be divided into categories indicating low, moderate, and high risk of recurrence, metastasis, or death; all risk categories showed a significant difference between the training, validation, and overall population groups (all P < .001). Positive lymph nodes (hazard ratio (HR)=3.07, 95% CI: 1.52-6.23, P=.002), cancer antigen-125 (HR=3.24, 95% CI: 1.54-6.80, P=.002), and the Radscore (HR=0.73, 95% CI: 0.61-0.87, P < .001) were independently associated with DFS. These 3 variables were used to construct a nomogram. In the training group, the areas under the curve at 3 years were 0.758 and 0.776 for the Rad-score and the nomogram, respectively, while they were both 1.000 in the validation group. The net benefit rate was analyzed using a decision curve in the training and validation groups, and the nomogram was superior to the single Rad-score. CONCLUSION Rad-score is an independent factor for DFS after gastrectomy for GC. The nomogram established in this study could be an effective tool for the clinical prediction of DFS after gastrectomy.