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Abstract Background Teratomas are categorized into mature teratomas (MT) and immature teratomas (IT) of I-III grades according to 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 examine intratumoral components and improve preoperative identification and grading of IT. Methods We analyzed the CT features and texture features of intratumoral components in teratomas (MT = 26, IT = 26). To assess intratumoral components' efficacy, logistic regression models were formulated for both MT and IT intergroups, as well as different grades within IT intragroups. Results 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). Within those, neighborhood gray tone difference_ busyness (NGLCMbusyness) feature for solid components in IT group was obviously higher than MT (p = 0. 000), with the value being higher in grade II than grade I (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. Conclusion CTTA is an effective method for IT identification and grading, with NGTDM features holding unique value. Among tumor components, the diagnostic value of solid components is the highest.
Qi et al. (Tue,) studied this question.