Evidence map›Paper›PMID 42683640›Full record

ArticleKorean journal of radiology2026

Multicenter Study of MRI Features in Uterine Myxoid Leiomyosarcoma: Revealing and Overcoming a Critical Pitfall in Current Consensus Criteria.

Tsukasa Saida, Natsumi Kawamatsu, Shinya Fujii, Saki Shibuki, Nao Kikkawa, Go Nakai, Yuki Himoto, Hideyuki Fukui, Takahiro Ueda, Ayumi Ohya and 5 more

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Korean journal of radiology, 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

15 authors.

Tsukasa SaidaDepartment of Radiology, Institute of Medicine, University of Tsukuba, Tsukuba, Japan. saida_sasaki_tsukasa@md.tsukuba.ac.jp.ORCID https://orcid.org/0000-0003-4530-7375
Natsumi KawamatsuDepartment of Diagnostic Pathology, University of Tsukuba Hospital, Tsukuba, Japan.ORCID https://orcid.org/0009-0009-9003-8283
Shinya FujiiDivision of Radiology, Department of Pathophysiological and Therapeutic Science, Faculty of Medicine, Tottori University, Yonago, Japan.ORCID https://orcid.org/0000-0002-0048-0396
Saki ShibukiDepartment of Radiology, Tsukuba Medical Center Hospital, Tsukuba, Japan.ORCID https://orcid.org/0000-0003-3113-141X
Nao KikkawaDepartment of Diagnostic Radiology, National Cancer Center Hospital, Tokyo, Japan.ORCID https://orcid.org/0000-0002-5694-6407
Go NakaiDepartment of Diagnostic Radiology, Osaka Medical and Pharmaceutical University, Takatsuki, Japan.ORCID https://orcid.org/0000-0002-5419-3907
Yuki HimotoDepartment of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan.ORCID https://orcid.org/0000-0001-8508-8221
Hideyuki FukuiDepartment of Radiology, Kindai University, Faculty of Medicine, Sakai, Japan.ORCID https://orcid.org/0000-0002-6330-2482
Takahiro UedaDepartment of Diagnostic Radiology, Fujita Health University School of Medicine, Toyoake, Japan.ORCID https://orcid.org/0000-0003-2577-3619
Ayumi OhyaDepartment of Radiology, Shinshu University School of Medicine, Matsumoto, Japan.ORCID https://orcid.org/0000-0003-0786-7531
Takashi HiyamaDepartment of Diagnostic Radiology, National Cancer Center Hospital East, Kashiwa, Japan.ORCID https://orcid.org/0000-0002-3274-4341
Yoko UchikawaDepartment of Radiology, Hitachi General Hospital, Hitachi, Japan.ORCID https://orcid.org/0009-0004-2197-4215
Takahiro TsuboyamaDepartment of Radiology, Kindai University, Faculty of Medicine, Sakai, Japan.ORCID https://orcid.org/0000-0002-7243-1454
Takahito NakajimaDepartment of Radiology, Institute of Medicine, University of Tsukuba, Tsukuba, Japan.ORCID https://orcid.org/0000-0001-6704-2297
Aki KidoDepartment of Radiology, Toyama University Hospital, Toyama, Japan.ORCID https://orcid.org/0000-0001-5131-2870

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo clarify the qualitative and quantitative MRI features of myxoid leiomyosarcoma (MLMS) in comparison with conventional leiomyosarcoma (cLMS) and to identify the specific diagnostic pitfalls of the current consensus criteria when applied to the MLMS subtype. MATERIALS AND

methodsThis retrospective multicenter study (12 institutions) included patients with histopathologically confirmed leiomyosarcoma (MLMS or cLMS) who underwent preoperative MRI. A total of 38 women (15 with MLMS and 23 with cLMS; median age, 55 years) were evaluated. Three radiologists independently evaluated features, including T2 signal patterns (purely hyperintense, mixed, iso-to-moderately hyperintense) and a characteristic "marbled appearance" (heterogeneous signal intermingling). The tumors were classified as benign or malignant on the basis of the apparent diffusion coefficient (ADC) values in accordance with the current consensus criteria. A histogram analysis was performed using multiparametric sequences.

resultsMLMS frequently exhibited purely T2-hyperintense or mixed patterns (12 of 15 [80%]), whereas cLMS predominantly showed T2-isointensity to moderate hyperintensity (21 of 23 [91%]) (

conclusionStrict reliance on ADC-based consensus criteria may lead to systematic under-recognition of MLMS. Recognizing the "marbled appearance" on MRI is crucial to prevent misclassifying these tumors as benign.

Indexed as

LeiomyosarcomaMagnetic Resonance ImagingUterine NeoplasmsAdultAgedContrast MediaDiagnosis, DifferentialFemaleHumansMiddle AgedRetrospective StudiesContrast MediaApparent diffusion coefficientDiffusion-weighted imagingMagnetic resonance imagingSarcomaUterus

Identifiers

PMID42683640
PMCPMC13537877

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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.