ArticleDiscover oncology2026
Investigation of the clinical value of artificial intelligence-derived prognostic signature in cervical cancer based on machine learning algorithms.
Article in Discover 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
Abstract
Women around the world are troubled by life-threatening cervical cancer. It is urgent to identify a biomarker to improve the prognosis of cervical cancer patients. Based on gene expression profiles and single-cell sequencing data obtained from public databases, we performed dimensionality reduction and clustering analyses, Scissor analysis, WGCNA, and machine-learning modeling using 10 base algorithms and their 101 individual or combined strategies. We finally screened 24 consensus prognostic genes to develop a novel model artificial intelligence-derived prognostic signature (AIDPS), based on C-index which was detected in six validation datasets (TCGA_Test, TCGA_Entire, CGCI-HTMCP-CC, GSE39001, GSE44001, and GSE52903). AIDPS demonstrated modest but consistent prognostic performance across multiple independent cervical cancer cohorts, with an average C-index of 0.665.The accuracy of AIDPS in predicting CESC was significantly better than that of other clinical characteristics including age, pathological TNM stages, and grade. In conclusion, our study developed a consensus model AIDPS, an effective strategy to further guide the clinical management and individualized treatment of cervical cancer.
Indexed as
Identifiers
What 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.