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

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

ObjectiveTo 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).MethodsA retrospective dataset of patients with preoperative prostate MRI examination and P504s/P63 pathological immunohis...

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Bibliographic Details
Main Authors: Yun-Fan Liu, Xin Shu, Xiao-Feng Qiao, Guang-Yong Ai, Li Liu, Jun Liao, Shuang Qian, Xiao-Jing He
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-06-01
Series:Frontiers in Oncology
Subjects:
P504s/P63
machine learning
MRI
immunohistochemistry
prostate cancer
Online Access:https://www.frontiersin.org/articles/10.3389/fonc.2022.911426/full

Internet

https://www.frontiersin.org/articles/10.3389/fonc.2022.911426/full

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