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Cancer Biol Ther ; 25(1): 2287120, 2024 12 31.
Article in English | MEDLINE | ID: mdl-38117551

ABSTRACT

This study was designed to evaluate the diagnostic efficacy of relevant parameters of 18F-prostate-specific membrane antigen (PSMA)-1007 PET/CT in predicting the pathological grade of primary prostate cancer. Briefly, a prospective analysis was performed on 53 patients diagnosed with prostate cancer by systematic puncture biopsy, followed by 18F-PSMA-1007 PET/CT examination prior to treatment within 10 d. The patients were grouped in accordance with the Gleason grading system revised by the International Association of Urology Pathology (ISUP). They were divided into high-grade group (ISUP 4-5 group) and low-grade group (ISUP 1-3 group). The differences in maximum standardized uptake value (SUVmax), tumor-to-background ratio (TBR), intraprostatic PSMA-derived tumor volume (iPSMA-TV), and intraprostatic total lesion PSMA (iTL-PSMA) between the high- and low-grade group were statistically significant (p < .001). No significant difference was found for mean standardized uptake value (SUVmean) between the high- and low-grade groups (Z = -1.131, p = .258). Besides, binary multivariate logistic regression analysis showed that only iPSMA-TV and iTL-PSMA were independent predictors of the pathological grading, for which the odds ratios were 18.821 [95% confidence interval (CI): 2.040-173.614, p = .010] and 0.758 (95% CI: 0.613-0.938, p = .011), respectively. The area under the ROC of this regression model was 0.983 (95% CI: 0.958-1.00, p < .001). Only iTL-PSMA was a significant parameter for distinguishing ISUP-4 and ISUP-5 groups (Z = -2.043, p = .041). In a nutshell, 18F-PSMA-1007 PET/CT has good application value in predicting the histopathological grade of primary prostate cancer. Three-dimensional volume metabolism parameters iPSMA-TV and iTL-PSMA were found to be independent predictors for pathological grade.


Subject(s)
Positron Emission Tomography Computed Tomography , Prostatic Neoplasms , Male , Humans , Prostatic Neoplasms/diagnostic imaging , Multivariate Analysis , Niacinamide
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