ArticleCancer imaging : the official publication of the International Cancer Imaging Society2025
Using radiomics model for predicting extraprostatic extension with PSMA PET/CT studies: a comparative study with the Mehralivand grading system.
Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Multimodal artificial intelligence for prostate cancer imaging: workflow-relevant fusion of mpMRI, PSMA PET, ultrasound, and clinical data for diagnosis, local staging, and treatment personalization.Abdominal radiology (New York) · 2026Review
- Cross-cohort projection of clinically anchored latent risk enables multi-omics interpretation without refitting.Briefings in bioinformatics · 2026Article
- [Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Advances in proton therapy technology and global clinical applications.Frontiers in oncology · 2026Review
- Impact ofDiagnostics (Basel, Switzerland) · 2025Article
- Multimodal imaging deep learning model for predicting extraprostatic extension in prostate cancer using MpMRI and 18 F-PSMA-PET/CT.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
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Funding
Abstract
purposeThis study aimed to evaluate the effectiveness of using a radiomics model to predict extraprostatic extension (EPE) in prostate cancer from PSMA PET/CT, and to directly compare its performance with the Mehralivand Grading System, an MRI-based method for EPE assessment.
methodsA total of 206 patients who underwent radical prostatectomy were included in this study. Radiomics features were extracted from PSMA PET/CT images to construct predictive models using Support Vector Machine (SVM) and Random Forest algorithms. In addition, among the 63 patients who underwent both PSMA PET/CT and multiparametric MRI (mpMRI), the performance of the radiomics model was compared with that of the Mehralivand Grading System. Key performance metrics, including the area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), were reported.
resultsAmong the 63 patients who underwent both PSMA PET/CT and multiparametric MRI (mpMRI), the radiomics model achieved an AUC of 76.8% (95% CI: 64.4-86.5%), sensitivity of 72.0%, specificity of 81.5%, PPV of 72.0%, and NPV of 81.6%. In comparison, the Mehralivand Grading System yielded AUCs of 66.8%, 63.5%, and 60.2% from three independent readers. DeLong's test showed that the radiomics model significantly outperformed all three readers in terms of AUC (p = 0.013, 0.003, and 0.001, respectively).
conclusionThe radiomics model derived from PSMA PET/CT can better capture features associated with EPE and shows promise for aiding preoperative assessment in prostate cancer. However, further validation in larger, independent cohorts is necessary to confirm its stability and clinical utility.
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