Scholarlink

<p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A Non-enhanced CT Dataset for Differential Diagnosis of Pediatric Extracranial Germ Cell Tumors

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年8月3日

查看全文
  • Haichun Zhou,
  • Zhexian Sun,
  • Jian Huang,
  • Yushuang Ding,
  • Xiaohui Ma,
  • Can Lai,
  • Gang Yu

摘要

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.