# CT texture analysis of pediatric teratomas—associations with identification and grading of immature teratoma

## TL;DR

This study shows that CT texture analysis can help identify and grade immature teratomas in children, improving preoperative diagnosis.

## Contribution

The study introduces CT texture analysis as a novel method for differentiating and grading immature teratomas in pediatric patients.

## Key findings

- Immature teratomas showed significantly larger calcification and solid component volumes compared to mature teratomas.
- NGTDM_busyness in solid components was higher in immature teratomas and increased with tumor grade.
- Solid component texture features achieved high diagnostic accuracy (AUC of 0.976) for immature teratoma identification.

## Abstract

Teratomas 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.

Between 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.

CT 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.

CTTA 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.