Background: The prevalence of diffuse-type gastric cancer (GC), especially signet ring cell carcinoma (SRCC), has shown an upward trend in the past decades. This study aimed to develop computed tomography (CT) based radiomics nomograms to distinguish diffuse-type and SRCC GC preoperatively.
Methods: A total of 693 GC patients from two centers were retrospectively analyzed and divided into training, internal validation and external validation cohorts. Radiomics features were extracted from CT images, and the Lauren radiomics model was established with a support vector machine (SVM) classifier to identify diffuse-type GC. The Lauren radiomics nomogram integrating radiomics features score (Rad-score) and clinicopathological characteristics were developed and evaluated regarding prediction ability. Further, the SRCC radiomics nomogram designed to identify SRCC from diffuse-type GC was developed and evaluated following the same procedures.
Results: Multivariate analysis revealed that Rad-scores was significantly associated with diffuse-type GC and SRCC (p < 0.001). The Lauren radiomics nomogram showed promising prediction performance with an area under the curve (AUC) of 0.895 (95%CI, 0.957-0.932), 0.841 (95%CI, 0.781-0.901) and 0.893 (95%CI, 0.831-0.955) in each cohort. The SRCC radiomics nomogram also showed good discrimination, with AUC of 0.905 (95%CI,0.866-0.944), 0.845 (95%CI, 0.775-0.915) and 0.918 (95%CI, 0.842-0.994) in each cohort. The radiomics nomograms showed great model fitness and clinical usefulness by calibration curve and decision curve analysis.
Conclusion: Our CT-based radiomics nomograms had the ability to identify the diffuse-type and SRCC GC, providing a non-invasive, efficient and preoperative diagnosis method. They may help guide preoperative clinical decision-making and benefit GC patients in the future.
Keywords: Gastric cancer; Nomogram; Pathology; Radiomics; Support vector machine.
© 2022. The Author(s).