Evidence map›Paper›PMID 39841225›Full record

ArticleAbdominal radiology (New York)2025

Multi-parametric MRI combined with radiomics for the diagnosis and grading of endometrial fibrosis.

Huanhuan Wang, Li Zhu, Hui Zhu, Jie Meng, Huanhuan Liang, Danyan Li, Yali Hu, Zhengyang Zhou

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Article in Abdominal radiology (New York), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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

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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

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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.

Huanhuan WangDepartment of Radiology, Nanjing Drum Tower Hospital, Clinical College of Nanjing University of Chinese Medicine, No. 321 Zhongshan Road, Nanjing, 210008, China.
Li ZhuDepartment of Radiology, Nanjing Drum Tower Hospital, Clinical College of Nanjing University of Chinese Medicine, No. 321 Zhongshan Road, Nanjing, 210008, China.
Hui ZhuDepartment of Obstetrics and Gynecology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, No. 321 Zhongshan Road, Nanjing, 210008, China.
Jie MengDepartment of Radiology, Nanjing Drum Tower Hospital, Clinical College of Nanjing University of Chinese Medicine, No. 321 Zhongshan Road, Nanjing, 210008, China.
Huanhuan LiangDepartment of Radiology, Nanjing Drum Tower Hospital, Clinical College of Nanjing University of Chinese Medicine, No. 321 Zhongshan Road, Nanjing, 210008, China.
Danyan LiDepartment of Radiology, Nanjing Drum Tower Hospital, Clinical College of Nanjing University of Chinese Medicine, No. 321 Zhongshan Road, Nanjing, 210008, China. lidanyan1982@163.com.
Yali HuDepartment of Obstetrics and Gynecology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, No. 321 Zhongshan Road, Nanjing, 210008, China. yalihu@nju.edu.cn.
Zhengyang ZhouDepartment of Radiology, Nanjing Drum Tower Hospital, Clinical College of Nanjing University of Chinese Medicine, No. 321 Zhongshan Road, Nanjing, 210008, China. zyzhou@nju.edu.cn.

Funding

the National Natural Science Foundation of China 81671751 and 81871410the Strategic Priority Research Program of the Chinese Academy of Sciences XDA16040302
6 · The paper itself

Abstract

purposeTo evaluate the application of multi-parametric MRI (MP-MRI) combined with radiomics in diagnosing and grading endometrial fibrosis (EF).

methodsA total of 74 patients with severe endometrial fibrosis (SEF), 41 patients with mild to moderate fibrosis (MMEF) confirmed by hysteroscopy, and 40 healthy women of reproductive age were prospectively enrolled. The enrolled data were randomly stratified and divided into a train set (108 cases: 28 healthy women, 29 with MMEF, and 51 with SEF) and a test set (47 cases: 12 healthy women, 12 MMEF and 23 SEF) at a ratio of 7:3. All participants underwent T2 and DWI sequence scans. By freely delineating the volume of interest (VOI) of the endometrium in three subgroups, radiomic features were extracted and selected. Two feature selection methods and four machine learning (ML) classifiers were combined in pairs to establish five prediction models [model

resultsThe "UFS-LR" model, which combined unsupervised feature selection (UFS) with the logistic regression (LR) classifier, performed the best, with an average AUC of 0.92 on the test set. Among the five models constructed via UFS-LR, model

conclusionMP-MRI radiomics analysis using ML has excellent performance in grading EF. This approach is non-invasive and has the potential to reduce the reliance on hysteroscopy.

Indexed as

EndometriumMagnetic Resonance ImagingMultiparametric Magnetic Resonance ImagingUterine DiseasesAdultFemaleFibrosisHumansImage Interpretation, Computer-AssistedMachine LearningMiddle AgedProspective StudiesRadiomicsSensitivity and SpecificityEndometriumFibrosisMachine learningMagnetic resonance imagingRadiomics

Identifiers

PMID39841225

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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.