ArticleFrontiers in oncology2024
Development and validation of serological dynamic risk score to predict outcome in gastric cancer with adjuvant chemotherapy: a multicentre, longitudinal, cohort study.
Article in Frontiers in oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 4 citations in OpenAlex.
- Development and validation of machine learning-based survival analysis to predict outcome in gastric cancer with adjuvant chemotherapy: A multicenter, longitudinal, cohort study.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2025Article
- Prognostic value of CA125 in diffuse large B-cell lymphoma.Frontiers in oncology · 2025Article
- Development of a predictive model for metachronous liver metastasis in gastric cancer.Frontiers in oncology · 2025Article
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Authors and funding
19 authors at 4 institutions in 1 country.
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Abstract
Background: Baseline serological biomarkers have the potential to predict the benefits of adjuvant chemotherapy in patients with gastric cancer. However, the fluctuating nature of postoperative recurrence risk makes precise treatment challenging. We aimed to develop a risk score in real-time predicting outcomes for postoperative GC patients using blood chemistry tests. Materials and methods: This was a retrospective, multicentre, longitudinal cohort study from three cancer centres in China, with a total of 2737 GC patients in the pTNM stage Ib to III. Among them, 1651 patients with at least two serological records were assigned to the training cohort. Model validation was carried out using separate testing data with area under curve (AUC). The least absolute shrinkage and selection operator (LASSO) and random forest-recursive feature elimination (RF-RFE) algorithm were used to select the parameters. Results: The Cox regression model derived six risk factors to construct a composite score (low-risk: 0-2 score; high risk: 3-6 score), including CEA, CA125, CA199, haemoglobin, albumin, and neutrophil to lymphocyte ratio. The risk score accurately predicted mortality in 1000-time bootstrap (AUROCs:0.658; 95% CI: 0.645, 0.670), with the highest AUROC (0.767; 95% CI: 0.743, 0.791) after 1 year since the gastrectomy. In validation dataset, the risk score had an AUROC of 0.586 (95% CI 0.544, 0.628). Furthermore, patients with high risk at 1 month derived significant clinical benefits from adjuvant chemotherapy ( Conclusion: The dynamic risk score is an accurate and user-friendly serological risk assessment tool for predicting outcomes and assisting clinical decisions after gastrectomy.
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