A machine learning model incorporating 18F-prostate-specific membrane antigen-1007 positron emission tomography/computed tomography and multiparametric magnetic resonance imaging for predicting prostate-specific antigen persistence in patients with p
<sup>18</sup>F-PSMA-1007 PET/CT and mpMRI parameters were proved effective in predicting PSA persistence in postoperative patients with PCa. The decision tree classification model could help clinicians to assess patients with individualized risk stratification. Patients with PSMA-TL levels below the threshold are highly likely not to have PSA persistence.
Authors
- Jia Jiang (ORCID: https://orcid.org/0000-0001-9337-3006)
- Fangansheng Chen
- Shuying Bian
- Haibo Wang (ORCID: https://orcid.org/0000-0003-4809-4897)
- Yimin Chen (ORCID: https://orcid.org/0000-0002-1602-3253)
- Ling Wang (ORCID: https://orcid.org/0000-0002-1974-1680)
- Yinuo Fu
- Xiangwu Zheng (ORCID: https://orcid.org/0000-0001-7512-6036)
- Zhe Xiao
- Yushi Peng
- Junping Lan
- Kun Tang (ORCID: https://orcid.org/0000-0003-0996-0481)
Institutions
- Wenzhou Medical University (CN)
- First Affiliated Hospital of Wenzhou Medical University (CN)
Publication Details
- Journal
- Quantitative Imaging in Medicine and Surgery
- Published
- 2024-12-24
- DOI
- https://doi.org/10.21037/qims-24-1149
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.81