Aluariachy Larbi, Oba Koji, Matsuyama Yutaka, Kuroda Akihiro, Okumura Yasuhiro, Yagi Koichi, Oshima Yoko, Fukagawa Takeo, Shimada Hideaki, Seto Yasuyuki
Graduate School of Medicine, International University of Health and Welfare, 4-1-26, Akasaka, Minato-ku, Tokyo 107-8402, Japan.
Interfaculty Initiative in Information Studies, the University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan.
Jpn J Clin Oncol. 2025 Aug 3;55(8):871-879. doi: 10.1093/jjco/hyaf075.
Postoperative recurrence prediction models for gastric cancer often rely on preoperative or immediate postoperative data, overlooking time-dependent biomarkers from follow-up visits. By incorporating longitudinal biomarker data through a landmarking approach, this study aims to enhance recurrence risk prediction.
This multicenter study included patients who underwent curative surgery for stage II-III gastric cancer from January 2010 to December 2016 in three hospitals in Tokyo, Japan. Their demographic, clinical, and biomarker data were collected from medical records. Biomarkers were collected at surgery and 3, 6, 9, and 12 months postoperatively. Three prediction models-baseline model, landmarking 1.0, and landmarking 1.5-were developed and compared in terms of their prediction accuracy using four measures: concordance probability, calibration plot, Kaplan-Meier curves stratified with predicted risk, and Net Reclassification Improvement. The models aimed to predict recurrence within three years after surgery, with predictions made one year postsurgery.
The study included 274 patients with gastric cancer, with 62 (22.6%) events occurring within three years. As a result of the variable selection process, lymphatic venous Invasion (LVI), pathological T (pT) stage, pathological N (pN) stage, and baseline prognostic nutritional index (PNI) were chosen. Additionally, in landmarking 1.0 and 1.5, S1 treatment status and PNI-change were also selected as time-dependent predictors. Landmarking 1.5, which incorporates time-dependent biomarkers until one year postsurgery, showed superior performance to the other models in all four measures.
Prediction models incorporating postoperative information could serve as a decision-making tool in clinical practice to more precisely distinguish between patients with high and low risk of recurrence.
胃癌术后复发预测模型通常依赖术前或术后即刻的数据，而忽略了随访中的时间依赖性生物标志物。本研究通过一种标志性方法纳入纵向生物标志物数据，旨在提高复发风险预测能力。
这项多中心研究纳入了2010年1月至2016年12月在日本东京三家医院接受II-III期胃癌根治性手术的患者。从病历中收集他们的人口统计学、临床和生物标志物数据。在手术时以及术后3、6、9和12个月收集生物标志物。开发了三个预测模型——基线模型、标志性1.0模型和标志性1.5模型，并使用四种指标比较它们的预测准确性：一致性概率、校准图、按预测风险分层的Kaplan-Meier曲线以及净重新分类改善。这些模型旨在预测术后三年内的复发情况，预测在术后一年进行。
该研究纳入了274例胃癌患者，其中62例（22.6%）在三年内出现复发事件。经过变量选择过程，选择了淋巴管侵犯（LVI）、病理T（pT）分期、病理N（pN）分期和基线预后营养指数（PNI）。此外，在标志性1.0和1.5模型中，S1治疗状态和PNI变化也被选为时间依赖性预测因子。纳入术后一年时间依赖性生物标志物的标志性1.5模型在所有四项指标上均表现优于其他模型。
纳入术后信息的预测模型可作为临床实践中的决策工具，以更精确地区分复发风险高和低的患者。
