Retrospective analysis shows the Newstage system improves prognostic discrimination in locally advanced gastric cancer, indicating better survival outcomes with neoadjuvant chemoimmunotherapy.
Background: This study aimed to evaluate the prognostic value of a newly proposed Newstage system integrating tumor regression grade (TRG) and ypN category versus the 8th edition American Joint Committee on Cancer (AJCC) post-neoadjuvant therapy (ypTNM) staging system in patients with locally advanced gastric cancer (LAGC) treated with neoadjuvant chemoimmunotherapy (NCIT). Methods: This retrospective study included 318 patients who underwent radical gastrectomy after NCIT. Of these, 168 patients from two institutions comprised the development cohort, whereas 150 patients from another hospital formed the validation cohort. We compared patient survival outcomes between the AJCC 8th edition ypTNM staging system and the Newstage system. Results: In the development cohort, 2-year overall survival rates according to pathological complete response (pCR) group, ypT0N + group, ypTNM stage I, II, and III were 95.8%, 100.0%, 89.2%, 88.5%, and 66.0%, respectively ( p < 0.001). For Newstage System I, II, III, and IV, the figures were 96.2%, 91.2%, 78.6%, and 62.6%. Compared with the AJCC 8th edition ypTNM staging system, the Newstage system demonstrated superior prognostic discrimination (higher C-index), better fit (lower Akaike Information Criteria and Bayesian Information Criterion values), and greater temporal stability (time-dependent AUC analysis). The multivariate Cox regression analysis further supported the prognostic advantage of the Newstage system over the AJCC 8th edition ypTNM staging system. Similar results were obtained in the validation cohort. Conclusions: The Newstage system provides superior prognostic stratification for patients with LAGC following NCIT, outperforming the AJCC 8th edition ypTNM staging system. Its enhanced capacity for survival discrimination supports its incorporation into future clinical staging algorithms.
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Wang et al. (2025) studied this question.
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