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TRUVACE RECORD VERSION
record: TRV-2026-0866
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-08-24T06:06:34.247199Z
status: published
lens: trace
sector: health
headline: The Lymph Node Ratio as a Predictive Biomarker for Individualized Benefit from Adjuvant Chemotherapy in Gastric Cancer: A Retrospective Cohort and Causal Machine Learning Study
dek: Background Optimizing adjuvant chemotherapy (AC) for gastric cancer (GC) remains challenging due to patient heterogeneity. While the lymph node ratio (LNR) is a known prognostic factor, its role in predicting individualized AC benefit remains underexplored. This study aimed to leverage causal machine learning to explore LNR's role for personalized treatment. Methods We conducted a retrospective cohort study of 2,748 patients undergoing radical gastrectomy (2007-2017, re-staged by AJCC 8th edition). While the ful…
gain_title: Causal Forest analysis of Stage II-III gastric cancer patients found lymph node ratio was the dominant predictor of individualized benefit from adjuvant chemotherapy, with high-LNR patients showing improved survival.
problem_title: In the matched Stage II-III cohort, patients with low lymph node ratio showed potential harm from adjuvant chemotherapy, indicating heterogeneous treatment effects and risk of overtreatment.
trace_subject: individualized benefit and harm from adjuvant chemotherapy in Stage II-III gastric cancer stratified by lymph node ratio
gain_reading: Causal Forest analysis of Stage II-III gastric cancer patients found lymph node ratio was the dominant predictor of individualized benefit from adjuvant chemotherapy, with high-LNR patients showing improved survival.
gain_evidence: identified LNR as the most dominant predictor of AC benefit | LNR is identified as a robust predictive biomarker for AC benefit in GC | Causal Forest model (Area Under the Uplift Curve = 31.08)
problem_reading: In the matched Stage II-III cohort, patients with low lymph node ratio showed potential harm from adjuvant chemotherapy, indicating heterogeneous treatment effects and risk of overtreatment.
problem_evidence: while showing potential harm in the Low LNR (
quick_read: Researchers used a Causal Forest causal machine learning model on a retrospective cohort of gastric cancer patients treated between 2007 and 2017 to estimate who benefits from adjuvant chemotherapy. The model identified lymph node ratio as the most dominant predictor of benefit, with a significant interaction at a 0.25 threshold in a propensity-matched Stage II-III cohort.

The result matters because adjuvant chemotherapy decisions in gastric cancer remain heterogeneous and guidelines lack individualized predictors; using LNR to target therapy could improve survival for high-risk patients while avoiding toxicity for low-risk patients. Uncertainty remains because the analysis is retrospective, exploratory, and based on out-of-bag predictions without prospective or external validation by the August 2026 publication date.
limitation: Findings derive from a retrospective single-cohort design and are framed as exploratory, limiting causal certainty and generalizability without prospective validation.
tag: Dual reading
key_points: Retrospective cohort of 2,748 patients undergoing radical gastrectomy from 2007-2017, re-staged by AJCC 8th edition. | Analytic models focused on 325 untreated Stage IB patients for prognosis and 825 Stage II-III patients for AC benefit estimation using Causal Forest with out-of-bag predictions. | Propensity score matching was used to mitigate treatment allocation bias, yielding a matched cohort of 434 patients for subgroup Cox interaction analysis. | Authors developed an exploratory web-based decision support tool to facilitate clinical application of LNR-based personalization.
rundown: The study analyzed 2,748 radical gastrectomy cases, then applied a Causal Forest model with out-of-bag predictions to 825 Stage II-III patients to estimate individualized treatment effects of adjuvant chemotherapy.

After propensity score matching to 434 patients, subgroup Cox analysis reported a threshold effect at LNR 0.25 with HR 0.41, P=0.016 for the high-LNR group, contrasted with potential harm in the low-LNR group, supporting LNR as a predictive rather than only prognostic marker.
sources:
- peer_reviewed | Journal of Gastrointestinal Cancer | https://doi.org/10.1007/s12029-026-01556-1 | 2026-08-22
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