Multimodal integration of [¹⁸F]PSMA-1007 PET/CT semiquantitative parameters and clinicopathological data for predicting prostate cancer metastasis.

ABSTRACT:

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.

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

SUBMITTER: Yang J 

PROVIDER: S-EPMC12568342 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature