ORIGINAL RESEARCH article
Front. Oncol.
Sec. Genitourinary Oncology
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1640159
This article is part of the Research TopicLeveraging Artificial Intelligence for Biomarker Discovery in Prostate CancerView all 3 articles
Multimodal Integration of [18F]PSMA-1007 PET/CT Semiquantitative Parameters and Clinicopathological Data for Predicting Prostate Cancer Metastasis
Provisionally accepted
- 1General Hospital of Ningxia Medical University, Yinchuan, China
- 2Xi'an International Medical Center Hospital, Xi'an, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Background: Prostate cancer is one of the most prevalent malignant tumors of the male genitourinary system. The occurrence of metastasis significantly influences treatment strategies and prognosis. However, current risk assessments for metastatic disease primarily rely on single imaging or pathological indicators, which are often limited by suboptimal accuracy and considerable individual variability. This is a provisional file, not the final typeset article Objective: This study aimed to develop a high-performance predictive model for prostate cancer metastasis by integrating semiquantitative parameters from [18F]PSMA-1007 PET/CTwith key clinicopathological features. Methods: We retrospectively analyzed data from prostate cancer patients, includingPSMA PET/CT-derived features (SUVmax, SUVmean, PSMA-TVp, TL-PSMAp) and clinical-pathological variables (age, tPSA, Gleason score). Five machine learningalgorithms—Logistic Regression, Support
Keywords: [18F]PSMA-1007, positron emission tomography/computed tomography, PredictingProstate Cancer Metastasis, Multimodal prediction, machine learning, Shap
Received: 03 Jun 2025; Accepted: 25 Sep 2025.
Copyright: © 2025 Ma, Yang, Zhao, Li, Chen and HaiTong. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Zhilong Ma, 1825900637@qq.com
Jiaying Yang, sunnyjaney@163.com
Qian Zhao, cecilia_hh@126.com
YanMei Li, amay5059@163.com
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.