A Non-enhanced CT Dataset for Differential Diagnosis of Pediatric Extracranial Germ Cell Tumors.
Authors
Affiliations (5)
Affiliations (5)
- Department of Radiology, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Children and Adolescents' Health and Diseases, Hangzhou, 310052, China.
- Department of Data and Information, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Children and Adolescents' Health and Diseases, Hangzhou, 310052, China.
- Sino-Finland Joint AI Laboratory for Child Health of Zhejiang Province, Hangzhou, 310052, China.
- Department of Data and Information, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Children and Adolescents' Health and Diseases, Hangzhou, 310052, China. [email protected].
- Sino-Finland Joint AI Laboratory for Child Health of Zhejiang Province, Hangzhou, 310052, China. [email protected].
Abstract
Extracranial germ cell tumors (EGCTs) are rare pediatric neoplasms characterized by significant histological heterogeneity and variability in clinical behavior. This highlights the necessity for precise preoperative differential diagnosis. While computed tomography (CT) provides essential imaging information for differentiation, conventional visual assessments are often limited due to overlapping radiological features. Although artificial intelligence (AI) has demonstrated potential in enhancing diagnostic accuracy in medical imaging, its application to EGCTs has been constrained by the scarcity of publicly available imaging datasets. To address this gap, we present the CT Pediatric EGCTs Diagnosis (CT-PEGCT-Diag) dataset, which consists of 642 non-enhanced CT scans representing six distinct histological subtypes: mature teratoma, immature teratoma, yolk sac tumor, mixed germ cell tumor, dysgerminoma, and embryonal carcinoma. The dataset encompasses standardized preprocessing protocols, rigorous quality control measures, and expert-annotated tumor masks. Preliminary experiments employing radiomics-based machine learning models demonstrate the dataset's utility in aiding the development of diagnostic tools. The CT-PEGCT-Diag dataset is designed to facilitate the validation of AI models and advance research into imaging biomarkers for the subtype classification of pediatric EGCTs.