ArticleScientific reports2026
Estimating temporal treatment-effect patterns of radiotherapy and chemotherapy in lower-grade gliomas using causal machine learning.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
10 authors.
Funding
Abstract
Treatment decisions for lower-grade gliomas (WHO grades 2-3) rest on trial averages, which lack temporal resolution. We applied Causal Analysis of Survival Trajectories (CAST), a causal-machine-learning method that builds treatment-effect trajectories from horizon-specific estimates, to 776 adults from The Cancer Genome Atlas (TCGA, n = 512) and the Chinese Glioma Genome Atlas (CGGA, n = 264) across six radiotherapy and alkylating-chemotherapy scenarios on overall (OS) and progression-free survival (PFS). Elastic-net propensity scores with overlap weighting (target: average treatment effect on the overlap population, ATO) balanced age, sex, grade, IDH, 1p/19q, and extent of resection. Chemotherapy showed adjusted survival-probability gains peaking at 0.34 (95% CI -0.32 to 1.00) at 84 months (TCGA OS) and 0.48 (0.04 to 0.92) at 108 months (CGGA OS); E-values of 5.1-27.6 indicate robustness to unmeasured confounding. Radiotherapy estimates were mixed (E-values 1.1-5.1) and are reported as adjusted associations sensitive to residual confounding from missing extent-of-resection and performance-status data, not as evidence of treatment-induced effect. Age drove most heterogeneity (46-52% of splits); refutation tests supported the chemotherapy findings.
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