ArticleFrontiers in oncology2025
Multiparametric MRI-based radiomics for preoperative prediction of parametrial invasion in early-stage cervical cancer.
Article in Frontiers in oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Preoperative prediction of parametrial invasion in early-stage cervical cancer: a radiomics nomogram fusing multi-parametric MRI and clinical biomarkers.Abdominal radiology (New York) · 2026Article
- Radiomics based on habitat analysis in predicting parametrial invasion of early stage cervical cancer.Frontiers in oncology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: The aim of this study was to evaluate the performance of radiomics based on multiparametric magnetic resonance imaging (MRI) for the preoperative prediction of parametrial invasion (PMI) in cervical cancer (CC). Materials and methods: This retrospective study included 110 consecutive patients with International Federation of Obstetrics and Gynecology (FIGO) stage IB-IIA CC. Patients were randomly divided into a training and a testing cohort in an 8:2 ratio. The region of interest (ROI) was manually delineated. Radiomics features were extracted separately from T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC), and contrast-enhanced T1-weighted imaging (T1C). Feature selection was performed using the correlation coefficient, recursive feature cancellation, and the least absolute shrinkage and selection operator algorithm. Radiomics models based on single-sequence, dual-sequence, and multi-sequence combinations were then constructed. Model performance was assessed using receiver operating characteristic (ROC) curve analysis. The DeLong test was used to compare the area under the curve (AUC), supplemented by net reclassification improvement and comprehensive discrimination improvement measures. Results: A total of 2,264 radiomics features were initially extracted. After feature selection, 7, 10, 6, and 8 valid features were retained from T1C, T2WI, ADC, and DWI sequence, respectively. A total of 15 radiomics models were developed, namely, 4 single-sequence models, 6 double-sequence models, and 5 multi-sequence models. All models showed good classification performance for PMI in both training and testing cohorts, with an AUC ranging from 0.755 to 1.000 in the training cohort and from 0.758 to 0.917 in the testing cohort. Among them, the T1C+ADC+DWI model demonstrated the best diagnostic performance, significantly outperforming all other models ( Conclusion: Radiomics based on multiparametric MRI can effectively predict PMI status in patients with early-stage CC, offering valuable support for individualized treatment planning and clinical decision-making.
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.