Evidence map›Paper›PMID 37589907›Full record

SynthesisEuropean radiology2024

Development and validation of MRI-based scoring models for predicting placental invasiveness in high-risk women for placenta accreta spectrum.

Qianyun Liu, Wenming Zhou, Zhimin Yan, Da Li, Tuo Lou, Yishu Yuan, Pengfei Rong, Zhichao Feng

Abstract readValidation StudySystematic ReviewMeta-Analysis
PubMed Publisher
In one paragraph

Synthesis in European radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
7.0field-weighted citation impact, top 3% of its field
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

6 citing papers in PubMed, 12 citations in OpenAlex.

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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

8 authors at 2 institutions in 1 country.

Qianyun LiuDepartment of Radiology, The Third Xiangya Hospital of Central South University, No. 138 Tongzipo Road, Changsha, 410013, Hunan, China.
Wenming ZhouDepartment of Medical Imaging, Yueyang Central Hospital, Yueyang, Hunan, China.
Zhimin YanDepartment of Radiology, The Third Xiangya Hospital of Central South University, No. 138 Tongzipo Road, Changsha, 410013, Hunan, China.
Da LiDepartment of Medical Imaging, Yueyang Central Hospital, Yueyang, Hunan, China.
Tuo LouDepartment of Medical Imaging, Yueyang Central Hospital, Yueyang, Hunan, China.
Yishu YuanDepartment of Pathology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China.
Pengfei RongDepartment of Radiology, The Third Xiangya Hospital of Central South University, No. 138 Tongzipo Road, Changsha, 410013, Hunan, China.
Zhichao FengDepartment of Radiology, The Third Xiangya Hospital of Central South University, No. 138 Tongzipo Road, Changsha, 410013, Hunan, China. fengzc2016@163.com.ORCID http://orcid.org/0000-0001-6651-5195
Central South University · CNShanghai University of Traditional Chinese Medicine · CN

Funding

Innovation Guidance Project of Clinical Medical Technology in Hunan Province 2020SK52703Innovation Guidance Project of Clinical Medical Technology in Hunan Province 2020SK52704Leading Talents in the Scientific and Technological Innovation of Hunan Province 2021
6 · The paper itself

Abstract

objectivesTo develop and validate MRI-based scoring models for predicting placenta accreta spectrum (PAS) invasiveness. MATERIALS AND

methodsThis retrospective study comprised a derivation cohort and a validation cohort. The derivation cohort came from a systematic review of published studies evaluating the diagnostic performance of MRI signs for PAS and/or placenta percreta in high-risk women. The significant signs were identified and used to develop prediction models for PAS and placenta percreta. Between 2016 and 2021, consecutive high-risk pregnant women for PAS who underwent placental MRI constituted the validation cohort. Two radiologists independently evaluated the MRI signs. The reference standard was intraoperative and pathologic findings. The predictive ability of MRI-based models was evaluated using the area under the curve (AUC).

resultsThe derivation cohort included 26 studies involving 2568 women and the validation cohort consisted of 294 women with PAS diagnosed in 258 women (88%). Quantitative meta-analysis revealed that T2-dark bands, placental/uterine bulge, loss of T2 hypointense interface, bladder wall interruption, placental heterogeneity, and abnormal intraplacental vascularity were associated with both PAS and placenta percreta, and myometrial thinning and focal exophytic mass were exclusively associated with PAS. The PAS model was validated with an AUC of 0.90 (95% CI: 0.86, 0.93) for predicting PAS and 0.85 (95% CI: 0.79, 0.90) for adverse peripartum outcome; the placenta percreta model showed an AUC of 0.92 (95% CI: 0.86, 0.98) for predicting placenta percreta.

conclusionMRI-based scoring models established based on quantitative meta-analysis can accurately predict PAS, placenta percreta, and adverse peripartum outcome. CLINICAL RELEVANCE STATEMENT: These proposed MRI-based scoring models could help accurately predict PAS invasiveness and provide evidence-based risk stratification in the management of high-risk pregnant women for PAS. KEY POINTS: • Accurately identifying placenta accreta spectrum (PAS) and assessing its invasiveness depending solely on individual MRI signs remained challenging. • MRI-based scoring models, established through quantitative meta-analysis of multiple MRI signs, offered the potential to predict PAS invasiveness in high-risk pregnant women. • These MRI-based models allowed for evidence-based risk stratification in the management of pregnancies suspected of having PAS.

Indexed as

Magnetic Resonance ImagingPlacenta AccretaAdultFemaleHumansPredictive Value of TestsPregnancyRetrospective StudiesMagnetic resonance imagingMeta-analysisPlacenta accretaPrenatal diagnosis

Identifiers

PMID37589907
OpenAlexW4385897724

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.