ArticleBMC medical imaging2025
CT texture analysis of pediatric teratomas-associations with identification and grading of immature teratoma.
Article in BMC medical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Pebbled mosaic-like heterogeneous echogenicity in ovarian immature teratomas and correlation of ultrasound patterns with pathological grades.Acta obstetricia et gynecologica Scandinavica · 2026Article
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6 authors.
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Abstract
backgroundTeratomas are categorized into mature teratomas (MT) and immature teratomas (IT) of grades I-III based on the content of immature tissues. The existing diagnostic methods are not comprehensive and objective enough. This study aims to utilize computed tomography texture analysis (CTTA) to exploring heterogeneity of tumor components and enhance the preoperative identification and grading of IT.
methodsBetween 2019 and 2023, 52 patients with pathologically confirmed MT (n = 26) and IT (n = 26) underwent preoperative CT scans. Fat, calcification, and solid components of intratumoral components were extracted using 3D slicer. CT features including size and total volume, as well as 75 texture features were analyzed. Comparisons of these features were performed between the IT and MT groups and within the IT groups. Logistic regression models were constructed and the area under the curve (AUC) was used to evaluate the effectiveness of these models. Statistical significance was set at p < 0.05.
resultsCT features showed that, IT group exhibited greater calcification size (p = 0.012), larger calcification volume (p = 0.003), and larger solid component volume (p < 0.001) than MT group. Texture features showed 22, 30, and 43 differential texture features for fat, calcification, and solid components between IT and MT group, respectively (p < 0.05). Among these, the neighborhood gray tone difference matrix busyness (NGTDM_busyness) feature for solid components was significantly higher in the IT group than in the MT group (p < 0.001) and higher in grade II than in grade I within the IT groups (p = 0.020). Logistic regression analysis indicated that IT identification efficacy of fat, calcifications, and solid components models were 0.778, 0.774, and 0.976, respectively.
conclusionsCTTA is an effective method for IT identification and grading, with NGTDM features holding unique value. Among tumor components, the solid components demonstrate excellent diagnostic value.
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