Prediction of relapse-free survival in stage III gastric cancer patients treated with postoperative adjuvant chemotherapy: Random survival forest analysis from the START-2 trial.
Authors: Hiroki Sato, Wataru Ichikawa, Kazuhiro Yoshida, Yasuhiro Kodera, Yoshihiro Kakeji, Mitsugu Kochi, Masazumi Takahashi, Akinori Takagane, Kiyoshi Ishigure, Masahide Kaji, Narutoshi Nagao, Makoto Yamada, Akiharu Ishiyama, Takanobu Yamada, Norimasa Fukushima, Toshifumi Yamaguchi, Masahiro Takeuchi, Yu Sunakawa, Masashi Fujii, Takeshi Sano
Journal: Journal of Clinical Oncology (2025)

446 Background: The START-2 trial demonstrated that docetaxel plus S-1 (DS) was superior to S-1 alone (S-1) in terms of recurrence-free survival (RFS) and overall survival (OS) as the adjuvant chemotherapy for stage III gastric cancer (GC) patients (pts) (J Clin Oncol 2019, Gastric Cancer 2022). We conducted the START-2 AR study as a retrospective analysis using data from the START-2 trial to develop a prognostic model for stage III GC pts treated with postoperative chemotherapy. Methods: Among 912 pts in the START-2 trial, 599 pts signed the consent form to participate in the START-2 AR study. There were 542 pts (265 pts in DS group and 277 pts in S-1 group) without missing values of prognostic candidates. Pts were randomly divided into a training set and a validation set with a ratio of 7:3. Prognostic model for RFS was developed using random survival forest (RSF) with the training set. The RSF model was applied to the validation set to assess its performance, evaluated using Harrell's C-index. We calculated tertiles of risk scores from the RSF model to divide the training set into low-, middle-, and high-risk subgroups. These cut-off values were used to classify the validation set. A Cox proportional hazard model was applied to compare the prognosis among the risk groups. Results: There were no statistically significant differences in patient characteristicsbetween the 599 pts in the START-2 AR study and the 912 pts in the STRAT-2 trial. RFS was comparable between the two groups. The RSF model for the training set (n = 380) yielded a C-index of 0.873, identifying the number of metastatic lymph nodes, serum albumin, CEA, tumor diameter, the primary tumor location, platelet count, age as prognostic factors in order of importance. In the validation set (n = 162), the C-index was 0.648. Both the low-risk group (n = 35) (hazard ratio [HR]: 0.34, 95% confidence interval [CI]: 0.17 – 0.68, p &lt; 0.01) and the middle-risk group (n = 74) (HR: 0.55, 95% CI: 0.33 – 0.89, p = 0.02) in the validation set had significantly better RFS compared to the high-risk group (n = 53). Among the 76 pts in the low-risk group treated with DS, 44 were categorized into the low-risk group when assuming they had been treated with S-1 alone. The analysis of 599 pts after multiple imputation yielded results consistent with those from the complete data. Conclusions: We developed the RSF-based predictive model for stage III GC pts, which demonstrated good performance. The number of metastatic lymph modes was identified as the most important prognostic factor. Risk stratification based on this model effectively differentiated prognostic outcomes. Future external validation of the model is required. Clinical trial information: UMIN000011438.