Evidence map›Paper›PMID 42459075›Full record

ArticleCurrent medical imaging2026

The Efficacy of a Super-Resolution Reconstruction Radiomics Model Based on T2WI for Predicting Placenta Accreta Spectrum Disorders: A Multicenter Study.

Yiwei Mou, Changyi Guo, Xirong Zhang, Dong Han, Nan Yu, Xiaoqi Huang, Yiming Li

Abstract readMulticenter Study
In one paragraph

Article in Current medical imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Yiwei MouDepartment of Medical Techniques, Shaanxi University of Chinese Medicine, Xianyang, 712000, China.ORCID 0009-0009-3184-5202
Changyi GuoDepartment of Radiology, The Second Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, 712000, China.ORCID 0009-0000-8523-2772
Xirong ZhangDepartment of Medical Techniques, Shaanxi University of Chinese Medicine, Xianyang, 712000, China.ORCID 0000-0002-6761-4790
Dong HanDepartment of Radiology, The Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, 712000, China.ORCID 0000-0002-5888-5759
Nan YuDepartment of Medical Techniques, Shaanxi University of Chinese Medicine, Xianyang, 712000, China.ORCID 0000-0001-8481-8819
Xiaoqi HuangDepartment of Radiology, Yan'an University Affiliated Hospital. Yan'an, 716000, China.ORCID 0000-0003-1365-759X
Yiming LiDepartment of Radiology, First People's Hospital of Shangqiu, Shangqiu, 476000, China.ORCID 0009-0002-1225-4692

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

INTRODUCTION/

objectivePlacenta accreta spectrum (PAS) disorders threaten maternal and fetal health. This multicenter study aimed to evaluate whether super-resolution reconstruction (SRR) of T2-weighted magnetic resonance images improves the diagnostic performance of a radiomics model for predicting PAS compared with conventional images.

methodsWe retrospectively analyzed 603 suspected PAS cases from three centers. Center A (n=480, 224 PAS vs. 256 non-PAS) served as the training dataset; Centers B (n=66) and C (n=57) were external validation sets. Deep learning-based SRR generated 2× and 4× super-resolution T2WI. An automated nnUNet model segmented the placenta. From each resolution, 107 radiomics features were extracted and reduced by LASSO regression. Three classifiers (KNN, AdaBoost, and Gradient Boosting) were developed. Model performance was assessed using AUC and DeLong's test.

resultsThe nnUNet segmentation achieved Dice coefficients of 0.863 and 0.883 on the two external sets. In the training set, the Gradient Boosting model on 4× images yielded the highest AUC of 0.874 (95% CI: 0.8441-0.9047). However, DeLong tests showed no statistically significant differences among the 1×, 2×, and 4× models for any classifier (all P > 0.05). External validation AUCs ranged from 0.542 to 0.724, indicating only moderate generalizability. DISCUSSION: Although SRR significantly enhanced image resolution, it did not provide incremental diagnostic value for PAS prediction within the radiomics framework. The diagnostic information relevant to PAS may be adequately captured at original resolution, or the radiomics features used are insensitive to SRR-enhanced textural changes.

conclusionRadiomics models based on 2× or 4× super-resolution T2WI were not superior to those based on pre-super-resolution images for predicting PAS. Routine SRR is not recommended for this clinical application.

Indexed as

Image Interpretation, Computer-AssistedMagnetic Resonance ImagingPlacenta AccretaAdultDeep LearningFemaleHumansImage Processing, Computer-AssistedPregnancyRadiomicsRetrospective StudiesDeep learningMagnetic resonance imagingMulticenter studyPlacenta accreta spectrum disordersPredictive modelRadiomicsSuper-resolution reconstruction

Identifiers

PMID42459075
PMCPMC13613300

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.