Development and validation of a nomogram for preoperative prediction of immature teratoma in children with teratoma: a retrospective, multicenter, diagnostic study

Haichun Zhou; Xu Li; Jing Chen; Yushuang Ding; Xiaohui Ma; Can Lai; Junfen Fu

doi:10.21037/qims-23-600

Development and validation of a nomogram for preoperative prediction of immature teratoma in children with teratoma: a retrospective, multicenter, diagnostic study

Quant Imaging Med Surg. 2023 Dec 1;13(12):8067-8078. doi: 10.21037/qims-23-600. Epub 2023 Sep 15.

Authors

Haichun Zhou¹, Xu Li², Jing Chen³, Yushuang Ding¹, Xiaohui Ma¹, Can Lai¹, Junfen Fu⁴

Affiliations

¹ Department of Radiology, Children's Hospital of Zhejiang University School of Medicine, National Clinical Research Center for Child Health, National Children's Regional Medical Center, Hangzhou, China.
² Department of Radiology, Children's Hospital of Anhui Medical University, Hefei, China.
³ Department of Medical Imaging, Tianjin Children's Hospital, Tianjin, China.
⁴ Department of Endocrinology, Children's Hospital of Zhejiang University School of Medicine, National Clinical Research Center for Child Health, National Children's Regional Medical Center, Hangzhou, China.

Abstract

Background: Teratomas are the most common germ cell tumors in children, and histologically classified as mature teratomas (MTs) and immature teratomas (ITs). Preoperative IT identification can affect the surgical approach, the type of procedure, and future possible reproductive health. However, there is no complete diagnostic criterion for ITs nowadays. We aimed to establish and validate a nomogram based on clinical and computed tomography (CT) features for preoperative prediction of ITs in children.

Methods: We retrospectively reviewed 519 teratoma patients from hospital I for training (n=364) and validation (n=155), and 113 patients from hospital II for external validation. Univariate and multivariate logistic regression analyses were performed on the training set to screen risk factors, including alpha-fetoprotein (AFP), age, gender, tumor site, size, tumor composition, calcification and fat. Then, a nomogram was established based on identified risk factors and validated on the validation set. The performance of the nomogram was evaluated in terms of discrimination, calibration and the clinical usefulness.

Results: Multivariate logistic regression showed that tumor composition, AFP, age, calcification and fat were independent risk factors for preoperative prediction of IT. The area under the receiver operating characteristic (ROC) curves (AUCs) for the nomogram on the training set, internal and external validation set were 0.92 (0.88-0.96), 0.91 (0.84-0.97) and 0.92 (0.86-0.97), respectively. The model demonstrated sensitivity of 80%, specificity of 90% at the cut-off value of 0.262. Whatever the set, the calibration curve indicated good calibration. Decision curve analysis (DCA) curves demonstrated that the nomogram had greater net benefits than either the treat-all tactics or the treat-none tactics within a large scope of threshold.

Conclusions: The nomogram established based on clinical and CT findings had the favorable accuracy for the preoperative prediction of IT, and may help in clinical decision-making and risk stratification.

Keywords: Teratoma; children; computed tomography (CT); nomogram; predictive model.