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

Abstract

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. METHODSClinical, 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. RESULTSTotally, 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. CONCLUSIONRad-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. G astric adenocarcinomas are the most common type of stomach cancer. 1,2 There are 1 033 701 new gastric cancer (GC) cases and 872 685 GC-related deaths worldwide in 2018. 3 Some regions including Eastern Asia, Eastern Europe, and South America have the highest incidence of GC. 2,4 The treatment management of GC includes surgery, chemotherapy, radiation therapy, and targeted therapy. 1,4,5 The 5-year survival of GC patients with localized, regional, and distant-stage diseases are 67%, 31%, and 5%, respectively. 1,4,5 The classical prognostic factors for GC include tumor size, lymphovascular invasion, nodal involvement, positive peritoneal cytology, signet ring cell adenocarcinoma, 1,2,4 age, sex, 6 and obesity. 7,8 TNM (T: primary tumor, N: lymph node, M: distant metastasis) staging system and histopathological classification have been widely used as the prognostic tools for GC, which can help to formulate the treatment strategy. 1,4,5 However, their predictive ability remains limited. 9-11 Novel prognostic biomarkers are also being explored, with similar restrictions. [...

Artificial Intelligence in Gastric Cancer Imaging With Emphasis on Diagnostic Imaging and Body Morphometry

“…These approaches often focus on disease-free survival with the aim of evaluating the likelihood of recurrence following gastrectomy. By analyzing and quantifying a wide range of radiomic features, radiomic models offer the potential to enhance prognostic assessments, enabling clinicians to identify patients at higher risk of recurrence and tailor treatment strategies accordingly [ 16 17 ]. However, a notable challenge with these radiomics approaches is the need for manual segmentation of tumors, which poses a significant hurdle to their integration into routine clinical practice.…”