Evidence map›Paper›PMID 40371382›Full record

ArticleInternational journal of women's health2025

A Dual-Sequence MRI-Based Radiomics Model for Predicting High-Intensity Focused Ultrasound Ablation Efficacy in Adenomyosis Treatment.

Mengmeng Sun, Xinyi Deng, Hui Xing, Rui Zhang, Bingbing Fu, Tao Ai, Fang Wang, Xuechun Wang, Lei Chen, Xiaogang Mao and 1 more

Abstract read
In one paragraph

Article in International journal of women's health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Review
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

11 authors.

Mengmeng Sun *Department of Obstetrics and Gynecology, Xiangyang Central Hospital, Affiliated Hospital of Hubei, University of Arts and Science, Xiangyang, People's Republic of China.
Xinyi Deng *Department of Radiology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, People's Republic of China.
Hui XingDepartment of Obstetrics and Gynecology, Xiangyang Central Hospital, Affiliated Hospital of Hubei, University of Arts and Science, Xiangyang, People's Republic of China.
Rui ZhangDepartment of Radiology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, People's Republic of China.
Bingbing FuDepartment of Obstetrics and Gynecology, Xiangyang Central Hospital, Affiliated Hospital of Hubei, University of Arts and Science, Xiangyang, People's Republic of China.
Tao AiDepartment of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People's Republic of China.
Fang WangDepartment of Research and Development, United Imaging Intelligence, Shanghai, People's Republic of China.
Xuechun WangDepartment of Research and Development, United Imaging Intelligence, Shanghai, People's Republic of China.ORCID 0000-0003-3847-6533
Lei ChenDepartment of Research and Development, United Imaging Intelligence, Shanghai, People's Republic of China.ORCID 0000-0002-9582-922X
Xiaogang MaoDepartment of Obstetrics and Gynecology, Xiangyang Central Hospital, Affiliated Hospital of Hubei, University of Arts and Science, Xiangyang, People's Republic of China.
Feng WuDepartment of Radiology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: The aim of this study is to develop a predictive model for the therapeutic efficacy of high-intensity focused ultrasound (HIFU) ablation in the treatment of adenomyosis, utilizing dual-sequence MRI radiomics. Methods: A retrospective analysis was conducted on 114 patients diagnosed with adenomyosis who underwent ultrasound-guided HIFU ablation under conscious sedation between July 2021 and July 2023. Patients were randomly allocated into a training set and a test set at a ratio of 7:3. The study aimed to evaluate the distribution of clinical characteristics among patients experiencing effective versus ineffective ablation at two distinct classification thresholds (0.7 and 0.5). Multiple models were developed to explore the combination of effective radiomic features derived from dual-sequence MRI and clinical data. Radiomic features were extracted from the MRI images of adenomyosis lesions in the training set. This process included feature extraction, selection, model construction, and evaluation. Logistic regression was used to construct the predictive model, and its performance was assessed on the test set using the receiver operating characteristic (ROC) curve. The Delong test, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were used to compare the predictive accuracy of the models. Results: The predictive model showed better alignment with actual ablation outcomes, particularly for predicting ablation success rates exceeding 50%. The combination of radiomic features from the two MRI sequences achieved an AUC of 0.84 in the test set. Decision curve analysis demonstrated that the combined model provided greater net benefit than the single-sequence radiomics model across a broader range of risk thresholds. For the prediction of 70% efficacy, the combined model achieved an AUC of 0.804 in the test set, slightly lower the 50% efficacy prediction task. Conclusion: The model, based on dual-sequence MRI radiomics, emerges as a promising tool for predicting the efficacy of HIFU ablation, potentially aiding clinicians in anticipating the outcomes of HIFU ablation procedures.

Indexed as

ablationadenomyosishigh-intensity focused ultrasoundmagnetic resonance imagingradiomics analysis

Identifiers

PMID40371382
PMCPMC12075450

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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.