A novel hematological classifier predicting chemotherapy benefit and recurrence hazard for locally advanced gastric cancer A multicenter IPTW analysis.

ABSTRACT

BACKGROUND: Effective classifiers for the prediction of individual adjuvant chemotherapy (AC) benefits are scarce. PURPOSE: This study aimed to construct a useful classifier to predict the AC benefit and recurrence hazard based on preoperative hematological indices through a multicenter database. METHODS AND RESULTS: Multivariate analysis revealing GCRF (comprehensive deep learning classifier) as an independent prognostic factor associated with overall survival (OS) and disease-free survival (DFS). Locally advanced gastric cancer (LAGC) patients are categorized into the high-risk group (HRG) and low-risk group (LRG). In HRG, OS and DFS of the AC group are significantly higher than those of the non-AC group (all p˂0.05), whereas in LRG, OS and DFS of the AC group are comparable to those of the non-AC group (all p > 0.05). Furthermore, combined GCRF with 8th AJCC TNM staging system, only 650 (51.1%) patients can benefit most from AC among 1273 patients with pStage II-III. From the perspective of recurrence pattern, the recurrence rate of HRG is significantly higher than that of LRG in any recurrence type, including local recurrence, peritoneal recurrence, and distant recurrence (all p˂0.05). Furthermore, the mean time to peritoneal recurrence and lung metastasis in HRG is earlier than that in the LRG (p = 0.028 and 0.011, respectively). CONCLUSION: In summary, our novel classifier based on deep learning preoperative hematological indices can predict not only the AC benefit of LAGC patients, but also the recurrence hazard after surgery. This classifier is expected to be an effective supplement to the 8th AJCC TNM staging system for the prediction of AC benefits and is helpful for clinical decision in AC individual administration. Further large-scale western studies are warranted.