Deep learning-based super-resolution US radiomics to differentiate testicular seminoma and non-seminoma: an international multicenter study

Deep learning-based super-resolution US radiomics to differentiate testicular seminoma and non-seminoma: an international multicenter study

Abstract Objectives Subvariants of testicular germ cell tumor (TGCT) significantly affect therapeutic strategies and patient prognosis. However, preoperatively distinguishing seminoma (SE) from non-seminoma (n-SE) remains a challenge. This study aimed to evaluate the performance of a deep learning-b...

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Main Authors: Yafang Zhang, Shilin Lu, Chuan Peng, Shichong Zhou, Irene Campo, Michele Bertolotto, Qian Li, Zhiyuan Wang, Dong Xu, Yun Wang, Jinshun Xu, Qinfu Wu, Xiaoying Hu, Wei Zheng, Jianhua Zhou
Format: Article
Language:English
Published: SpringerOpen 2025-08-01
Series:Insights into Imaging
Subjects:
Seminoma
Non-seminoma
Radiomics
Super-resolution
Ultrasound
Online Access:https://doi.org/10.1186/s13244-025-02045-y
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https://doi.org/10.1186/s13244-025-02045-y

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