RESUMO
BACKGROUND: Gastric cancer (GC) is the fifth most common and the fourth most lethal malignant tumour in the world. Most patients are already in the advanced stage when they are diagnosed, which also leads to poor overall survival. The effect of postoperative adjuvant chemotherapy for advanced GC is unsatisfactory with a high rate of distant metastasis and local recurrence. AIM: To investigate the safety and efficacy of a programmed cell death 1 (PD-1) inhibitor combined with oxaliplatin and S-1 (SOX) in the treatment of Borrmann large type III and IV GCs. METHODS: A retrospective analysis (IRB-2022-371) was performed on 89 patients with Borrmann large type III and IV GCs who received neoadjuvant therapy (NAT) from January 2020 to December 2021. According to the different neoadjuvant treatment regimens, the patients were divided into the SOX group (61 patients) and the PD-1 + SOX (P-SOX) group (28 patients). RESULTS: The pathological response (tumor regression grade 0/1) in the P-SOX group was significantly higher than that in the SOX group (42.86% vs 18.03%, P = 0.013). The incidence of ypN0 in the P-SOX group was higher than that in the SOX group (39.29% vs 19.67%, P = 0.05). The use of PD-1 inhibitors was an independent factor affecting tumor regression grade. Meanwhile, the use of PD-1 did not increase postoperative complications or the adverse effects of NAT. CONCLUSION: A PD-1 inhibitor combined with SOX could significantly improve the rate of tumour regression during NAT for patients with Borrmann large type III and IV GCs.
RESUMO
BACKGROUND: Lymph node metastasis (LNM) significantly impacts the treatment and prognosis of early gastric cancer (EGC). Consequently, the precise prediction of LNM risk in EGC patients is essential to guide the selection of appropriate surgical approaches in clinical settings. AIM: To develop a novel nomogram risk model for predicting LNM in EGC patients, utilizing preoperative clinicopathological data. METHODS: Univariate and multivariate logistic regression analyses were performed to examine the correlation between clinicopathological factors and LNM in EGC patients. Additionally, univariate Kaplan-Meier and multivariate Cox regression analyses were used to assess the influence of clinical factors on EGC prognosis. A predictive model in the form of a nomogram was developed, and its discrimination ability and calibration were also assessed. RESULTS: The incidence of LNM in the study cohort was 19.6%. Multivariate logistic regression identified tumor size, location, degree of differentiation, and pathological type as independent risk factors for LNM in EGC patients. Both tumor pathological type and LNM independently affected the prognosis of EGC. The model's performance was reflected by an area under the curve of 0.750 [95% confidence interval (CI): 0.701-0.789] for the training group and 0.763 (95%CI: 0.687-0.838) for the validation group. CONCLUSION: A clinical prediction model was constructed (using tumor size, low differentiation, location in the middle-lower region, and signet ring cell carcinoma), with its score being a significant prognosis indicator.