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 prostate cancer after radical prostatectomy
Year: 2024
Type: article
Study design: Observational Study
Abstract: Background: F-PSMA-1007 PET/CT characteristics could predict PSA persistence in patients with PCa treated with radical prostatectomy (RP). Methods: This retrospective study involved consecutive patients diagnosed with PCa who underwent both preoperative mpMRI and PSMA PET/CT scans between April 2019 and June 2022. Scatter plots and heat maps were employed to determine the correlation of mpMRI and PSMA PET/CT features with preoperative PSA. Univariate logistic regression analyses were used assess the correlation between age, maximum Prostate Imaging-Reporting and Data System (PI-RADS) score, prostate-specific antigen density (PSAD), extracapsular extension (EPE), seminal vesicle invasion (SVI), total lesion PSMA (PSMA-TL), and PSA persistence. Multivariate logistic regression analyses were used to develop a predictive model for PSA persistence, while decision tree analysis was used to classify patients into different risk groups for easy interpretation and visualization. We divided the patient cohort into training and validation sets in an 8:2 ratio. To ensure the reliability of the model, we performed five-fold cross-validation of the validation results. Results: were more likely to have PSA persistence. Decision tree analysis stratified patients into low-risk (5%), intermediate-risk (36%), and high-risk (48%) categories for PSA persistence. The model exhibited good discriminatory capability in internal validation (AUC 0.93, 95% CI: 0.850-0.930). Conclusions: 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: Fangansheng Chen, Jia Jiang, Yushi Peng, Ling Wang, Junping Lan, Shuying Bian, Hanzhe Wang, Zhe Xiao, Yimin Chen, Yinuo Fu, Xiangwu Zheng, Kun Tang
Language: English
Subfield: Pulmonary and Respiratory Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4405763400