ReviewFrontiers in oncology2026
Artificial intelligence and computational prediction models for risk stratification, treatment response, and outcomes in colorectal cancer: a narrative review.
Review in Frontiers in oncology, 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To critically evaluate, within a decision-centred framework, the clinical readiness of artificial-intelligence (AI) and computational prediction models used for risk stratification, treatment-response assessment, and outcome prediction in colorectal cancer (CRC), while distinguishing tumour site, intended clinical decision, assessment time point, and validation level, and identifying the gap between predictive performance and clinical impact. Methods: PubMed/MEDLINE and Web of Science were searched for English-language studies published from January 2010 through 31 May 2026; ScienceDirect, Google Scholar, reference lists, and forward citation tracking were used as supplementary sources. Two reviewers independently screened studies and extracted design, endpoint, data modality, unit of analysis, validation, calibration, decision analysis, workflow evaluation, accessibility, and clinical-impact evidence. Study-level methodological features were assessed separately from task-level translational readiness. Results: Comparatively stronger evidence was found for computer-aided endoscopic assessment of invasion depth, MRI-based lymph-node prediction in rectal cancer, H&E whole-slide-image pre-screening for MSI/MMR status, and selected externally tested models for pathological complete response. Evidence remained early-stage for CT-based nodal staging in colon cancer, tumour mutational burden prediction, neoadjuvant immunotherapy-response prediction, synchronous distant-metastasis assessment, recurrence, survival, and most perioperative-risk models. Prospective validation was uncommon and was not equivalent to demonstration of clinical benefit. Conclusion: A decision-centred synthesis indicates that CRC AI is most mature as site-specific and task-specific reader assistance or triage rather than autonomous decision-making. Clinical translation requires representative external validation, calibration, predefined action thresholds, model availability, workflow testing, safety monitoring, and prospective evidence that model-guided care improves decisions or outcomes.
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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.