Computed tomography 3D reconstruction and texture analysis for evaluating the efficacy of neoadjuvant chemotherapy in advanced gastric cancer.

Background: Gastric cancer (GC) remains a significant global health challenge, with high incidence and mortality rates. Neoadjuvant chemotherapy is increasingly used to improve surgical outcomes and long-term survival in advanced cases. However, individual responses to treatment vary widely, and current imaging methods often fall short in accurately predicting efficacy. Advanced imaging techniques, such as computed tomography (CT) 3D reconstruction and texture analysis, offer potential for more precise assessment of therapeutic response.

Aim: To explore the application value of CT 3D reconstruction volume change rate, texture feature analysis, and visual features in assessing the efficacy of neoadjuvant chemotherapy for advanced GC.

Methods: A retrospective analysis was conducted on the clinical and imaging data of 97 patients with advanced GC who received S-1 plus Oxaliplatin combined chemotherapy regimen neoadjuvant chemotherapy from January 2022 to March 2024. CT texture feature analysis was performed using MaZda software, and ITK-snap software was used to measure the tumor volume change rate before and after chemotherapy. CT visual features were also evaluated. Using postoperative pathological tumor regression grade (TRG) as the gold standard, the correlation between various indicators and chemotherapy efficacy was analyzed, and a predictive model was constructed and internally validated.

Results: The minimum misclassification rate of texture features in venous phase CT images (7.85%) was lower than in the arterial phase (13.92%). The volume change rate in the effective chemotherapy group (75.20%) was significantly higher than in the ineffective group (41.75%). There was a strong correlation between volume change rate and TRG grade (r = -0.886, P < 0.001). Multivariate analysis showed that gastric wall peristalsis (OR = 0.286) and thickness change rate ≥ 40% (OR = 0.265) were independent predictive factors. Receiver operating characteristic curve analysis indicated that the volume change rate [area under the curve (AUC) = 0.885] was superior to the CT visual feature model (AUC = 0.795). When the cutoff value was 82.56%, the sensitivity and specificity were 85.62% and 96.45%, respectively.

Conclusion: The CT 3D reconstruction volume change rate can serve as a preferred quantitative indicator for evaluating the efficacy of neoadjuvant chemotherapy in GC. Combining it with a CT visual feature predictive model can further improve the accuracy of efficacy evaluation.