ArticleMedical oncology (Northwood, London, England)2026
From glycolytic signatures to patients: A translational roadmap for reproducible, equitable deployment of multi-omics and AI in colorectal cancer.
Article in Medical oncology (Northwood, London, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent advances in Medical Oncology highlight the integration of bulk and single-cell transcriptomics to reveal glycolytic heterogeneity in colorectal cancer. Translating these discoveries into reliable clinical tools requires rigorous methods, transparent validation, and equity-minded implementation. This communication proposes a standards-first roadmap for reproducible and globally relevant biomarker development. It identifies major technical pitfalls such as batch-effect over-correction and normalization bias, and recommends the application of internationally recognized frameworks-TRIPOD + AI, PROBAST + AI, and DECIDE-AI-to ensure transparency, calibration, and staged clinical evaluation. Orthogonal validation using metabolic imaging and spectroscopy is emphasized to confirm biological realism beyond transcriptomic data. The roadmap concludes with strategies for global equity, including LMIC-inclusive trial design, FAIR data standards, and cost-aware clinical surrogates. This structured approach bridges discovery science with practical implementation, aligning precision oncology with reproducibility, accountability, and global accessibility.
Indexed as
Identifiers
41528598What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.