Evidence map›Paper›PMID 40785873›Full record

ReviewQuantitative imaging in medicine and surgery2025

Image-guided cryoablation for uterine diseases: a systematic review.

Keyao Lv, Ying Huang

Abstract readReview
In one paragraph

Review in Quantitative imaging in medicine and surgery, 2025. 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

2 authors.

Keyao LvDepartment of Ultrasound, Shengjing Hospital of China Medical University, Shenyang, China.
Ying HuangDepartment of Ultrasound, Shengjing Hospital of China Medical University, Shenyang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Cryoablation is a minimally invasive treatment, with limited research on uterine diseases. This paper provides a review of image-guided cryoablation for the treatment of gynecologic diseases, such as uterine myoma, abdominal wall endometriosis (AWE) and adenomyoma, from a safety and efficacy perspective. Methods: A comprehensive literature search was conducted using the databases Medline (via PubMed), EMBASE, and Web of Science. 2 researchers were responsible for identifying relevant studies. 18 studies that reported outcomes of image-guided cryoablation in gynecologic conditions were included. The baseline characteristics of patients and the clinical outcomes of cryoablation were all assessed. The different types of uterine pathology were discussed separately and the data were statistically analyzed independently to avoid the influence of methodological heterogeneity on the results to the greatest extent possible. Results: Mean volume reduction of uterine myoma was 69.8% at 24 months after cryoablation; the initial ablation efficiency rate of AWE was 82% per patient and 93.6% per AWE lesion; and the mean volume reduction of adenomyoma at 12 months was 65.8%. A significant decrease in visual analogue score (VAS) from baseline was reported in all included studies for patients with the 3 aforementioned uterine diseases following cryoablation. Eleven studies reported postoperative complications, with major complications occurring in an average of 3.82% of patients. During follow-up, less than 10% of patients had residual symptoms or lesions. Conclusions: Image-guided cryoablation is a minimally invasive therapy for uterine diseases with statistically and clinically significant effectiveness and a low incidence of major complications.

Indexed as

Adenomyomaendometriosisimage-guidedinterventionaluterine myoma

Identifiers

PMID40785873
PMCPMC12332712

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Registered trials

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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.