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