ArticleInternational journal of surgery (London, England)2026
A machine learning-based transcriptomic signature for predicting tumor recurrence after curative resection in T1 colorectal cancer: a retrospective multicenter cohort study (The Tw1CE trial).
Article in International journal of surgery (London, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 1 paper.
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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.
The trial behind it
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.
Predicting Recurrence After Curative-Intent Resection of T1 Colorectal Cancer With Transcriptomics
An Exosome-based and Machine-learning-powered Liquid Biopsy for Pancreatic Cancer Early-detection and Disease Monitoring
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1 citing paper in PubMed.
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundT1 colorectal cancer (T1 CRC) is increasingly treated with curative-intent endoscopic resection, but tumor recurrence remains a critical factor influencing patient prognosis. However there is no validated biomarker exists to reliably predict post-resection recurrence, limiting risk-adapted follow-up and adjuvant therapy decisions. MATERIALS AND
methodsIn this multicenter retrospective cohort study across academic centers in Spain, 138 patients with T1 CRC (2023-2025; ClinicalTrials.gov NCT06314971) were enrolled. From FFPE endoscopic specimens, expression of five mRNAs and two miRNAs was quantified by RT-qPCR, and an XGBoost-based transcriptomic panel was developed. Patients were assigned to training and independent testing cohorts by treatment type. The primary outcome was 3-year recurrence-free survival (RFS); secondary outcomes included 5-year RFS and overall survival (OS).
resultsThe transcriptomic panel demonstrated high predictive performance in both the training (AUROC = 91.7%) and testing (AUROC = 88.2%) cohorts. Patients classified as high-risk by the panel exhibited significantly worse RFS and OS compared with those classified as low-risk (log-rank P < 0.001). Furthermore, integrating lymphatic invasion with the transcriptomic panel into a combined risk stratification model further improved predictive accuracy (AUROC = 94.6%), and decision curve analysis confirmed its superior clinical utility compared to conventional criteria.
conclusionThis study established a validated machine learning-based transcriptomic classifier derived from endoscopic resection specimens that accurately predicts tumor recurrence in patients with T1 CRC. Our findings highlight the potential of this biomarker panel to enable risk-adapted surveillance strategies and guide decisions regarding additional therapy after curative resection.
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