Construction and validation of a prognostic nomogram model integrating machine learning-pathomics and clinical features in IDH-wildtype glioblastoma

Abstract Background Novel diagnostic criteria for glioblastoma (GBM) in the 2021 WHO classification emphasize the importance of integrating pathological and molecular features. Pathomics, which involves the extraction of digital pathology data, is gaining significant interest in the field of tumor r...

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Auteurs principaux:	Yaomin Li, Pei Ouyang, Zongliao Zheng, Jiapeng Deng, Aishun Guo, Weiwei Wang, Yawei Liu, Yuping Peng, Yankai Liao, Xiran Wang, Hai Wang, Zhaojun Wang, Zhitai Mo, Jianming Weng, Haiyan Xv, Xiaoxia Zheng, Junlu Liu, Yajuan Wang, Yongfu Cao, Guanglong Huang, Xian Zhang, Songtao Qi
Format:	Artigo
Langue:	Inglês
Publié:	BMC 2025-06-01
Collection:	Journal of Translational Medicine
Sujets:	Glioblastoma Machine learning Pathomics Nomogram Collagen
Accès en ligne:	https://doi.org/10.1186/s12967-025-06482-7
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