ArticleQuantitative imaging in medicine and surgery2025
A machine learning model incorporating
Article in Quantitative imaging in medicine and surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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12 authors.
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Abstract
Background: Although 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: Ultimately, this study included 190 patients with PCa. The median age of the patients was 69 years [interquartile range (IQR) 64-73 years]. Among the patients, 35 (18%) experienced PSA persistence following RP. Additionally, SVI was identified in 31 (16%) patients. The median values for SUVmax and PSMA-TL were 11.83 (IQR 7.44-20.89) and 41.92 (IQR 21.25-113.83), respectively. Spearman correlation analysis indicated that the preoperative PSA levels in patients with PCa were slightly correlated with the maximum standardized uptake value (SUVmax) (r=0.41; P<0.001), significantly correlated with PSMA-TL (r=0.58, P<0.001), and strongly correlated with PSAD (r=0.865, P<0.001). Multivariate logistic regression analysis showed that the independent predictors of PSA persistence were SVI on mpMRI [area under the curve (AUC)=0.63; 95% confidence interval (CI): 0.516-0.739] and PSMA-TL (AUC =0.80; 95% CI: 0.723-0.877) on PSMA PET/CT (all P values <0.05). Patients with SVI and PSMA-TL >63.38 cm Conclusions:
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