Radiomics-Based Machine Learning Models for Predicting P504s/P63 Immunohistochemical Expression: A Noninvasive Diagnostic Tool for Prostate Cancer

OBJECTIVE: To develop and validate a noninvasive radiomic-based machine learning (ML) model to identify P504s/P63 status and further achieve the diagnosis of prostate cancer (PCa). METHODS: A retrospective dataset of patients with preoperative prostate MRI examination and P504s/P63 pathological immu...

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Detalles Bibliográficos
Autores principales: Liu, Yun-Fan, Shu, Xin, Qiao, Xiao-Feng, Ai, Guang-Yong, Liu, Li, Liao, Jun, Qian, Shuang, He, Xiao-Jing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Oncology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9252170/
https://www.ncbi.nlm.nih.gov/pubmed/35795067
http://dx.doi.org/10.3389/fonc.2022.911426

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9252170/
https://www.ncbi.nlm.nih.gov/pubmed/35795067
http://dx.doi.org/10.3389/fonc.2022.911426

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