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BACKGROUND AND OBJECTIVE: Neoadjuvant chemotherapy results in various responses when used to treat locally advanced gastric cancer, we aimed to develop and validate a predictive model of the response to neoadjuvant chemotherapy in patients with gastric cancer. METHODS: A total of 128 patients with locally advanced gastric cancer who underwent pre-treatment computed tomography (CT) scanning followed by neoadjuvant chemoradiotherapy were included (training cohort: n = 64; validation cohort: n = 64). We built a radiomics score combined with laboratory parameters to create a nomogram for predicting the efficacy of neoadjuvant chemotherapy and calculating scores for risk factors. RESULTS: The radiomics score system demonstrated good stability and prediction performance for the response to neoadjuvant chemotherapy, with the area under the curve of the training and validation cohorts being 0.8 and 0.64, respectively. The radiomics score proved to be an independent risk factor affecting the efficacy of neoadjuvant chemotherapy. In addition to the radiomics score, four other risk factors were included in the nomogram, namely the platelet-to-lymphocyte ratio, total bilirubin, ALT/AST, and CA199. The model had a C-index of 0.8. CONCLUSIONS: Our results indicated that radiomics features could be potential biomarkers for the early prediction of the response to neoadjuvant treatment.
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Background: Traditional clinical characteristics have certain limitations in evaluating cancer prognosis. The radiomics features provide information on tumor morphology, tissue texture, and hemodynamics, which can accurately reflect personalized predictions. This study investigated the clinical value of radiomics features on contrast-enhanced computed tomography (CT) images in predicting prognosis and postoperative chemotherapy benefits for patients with gastric cancer (GC). Methods: For this study, 171 GC patients who underwent radical gastrectomy and pathology confirmation of the malignancy at the First Affiliated Hospital of Wenzhou Medical University were retrospectively enrolled. The general information, pathological characteristics, and postoperative chemotherapy information were collected. Patients were also monitored through telephone interviews or outpatient treatment. GC patients were randomly divided into the developing cohort (n=120) and validation cohort (n=51). The intra-tumor areas of interest inside the tumors were delineated, and 1,218 radiomics features were extracted. The optimal radiomics risk score (RRS) was constructed using 8 machine learning algorithms and 29 algorithm combinations. Furthermore, a radiomics nomogram that included clinicopathological characteristics was constructed and validated through univariate and multivariate Cox analyses. Results: Eleven prognosis-related features were selected, and an RRS was constructed. Kaplan-Meier curve analysis showed that the RRS had a high prognostic ability in the developing and validation cohorts (log-rank P<0.01). The RRS was higher in patients with a larger tumor size (≥3 cm), higher Charlson score (≥2), and higher clinical stage (Stages III and IV) (all P<0.001). Furthermore, GC patients with a higher RRS significantly benefited from postoperative chemotherapy. The results of univariate and multivariate Cox regression analyses demonstrated that the RRS was an independent risk factor for overall survival (OS) and disease-free survival (DFS) (P<0.001). A visual nomogram was established based on the significant factors in multivariate Cox analysis (P<0.05). The C-index was 0.835 (0.793-0.877) for OS and 0.733 (0.677-0.789) for DFS in the developing cohort. The calibration curve also showed that the nomogram had good agreement. Conclusions: A nomogram that combines the RRS and clinicopathological characteristics could serve as a novel noninvasive preoperative prediction model with the potential to accurately predict the prognosis and chemotherapy benefits of GC patients.