ArticlePolish journal of radiology2022
Texture analysis of low-flow vascular malformations in the oral and maxillofacial region: venous malformation vs. lymphatic malformation.
Article in Polish journal of radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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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.
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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.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it, 2 citations in OpenAlex.
- Radiomics-Based Diagnosis in Dentomaxillofacial Radiology: A Systematic Review.Journal of imaging informatics in medicine · 2025Pooled it
- Excision of a long-standing low-flow venous malformation of the fourth digit in a 30-year-old male: a case report.International journal of surgery case reports · 2026Article
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: It is challenging for radiologists to distinguish between venous malformations (VMs) and lymphatic malformations (LMs) using magnetic resonance imaging (MRI). Thus, this study aimed to differentiate VMs from LMs using non-contrast-enhanced MRI texture analysis. Material and methods: This retrospective case-control study included 12 LM patients (6 men and 6 women; mean age 43.58, range 7-85 years) and 29 VM patients (7 men and 22 women; mean age 53.10, range 19-76 years) who underwent MRI for suspected vascular malformations. LM and VM patients were identified by histopathological examination of tissues excised during surgery. The texture features of VM and LM were analysed using the open-access software MaZda version 3.3. Seventeen texture features were selected using the Fisher and probability of error and average correlation coefficient methods in MaZda from 279 original parameters calculated for VM and LM. Results: Among 17 selected texture features, the patients with LM and VM revealed significant differences in 1 histogram feature, 8 grey-level co-occurrence matrix (GLCM) features, and 1 grey-level run-length matrix feature. At the cut-off values of the histogram feature [skewness ≤ -0.131], and the GLCM features [S(0, 2) correlation ≥ 0.667, S(0, 3) correlation ≥ 0.451, S(0, 4) correlation ≥ 0.276, S(0, 5) correlation ≥ 0.389, S(1, 1) correlation ≥ 0.739, S(2, 2) correlation ≥ 0.446, S(2, -2) correlation ≥ 0.299, S(3, -3) correlation ≥ 0.091] had area under the curves of 0.724, 0.764, 0.773, 0.747, 0.733, 0.759, 0.730, 0.744 and 0.727, respectively. Conclusions: Non-contrast-enhanced MRI texture analysis allows us to differentiate between LMs and VMs.
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