The added value of PSMA PET/MR radiomics for prostate cancer staging

Purpose To evaluate the performance of combined PET and multiparametric MRI (mpMRI) radiomics for the group-wise prediction of postsurgical Gleason scores (psGSs) in primary prostate cancer (PCa) patients. Methods Patients with PCa, who underwent [(68) Ga]Ga-PSMA-11 PET/MRI followed by radical prostatectomy, were included in this retrospective analysis (n = 101). Patients were grouped by psGS in three categories: ISUP grades 1-3, ISUP grade 4, and ISUP grade 5. mpMRI images included T1-weighted, T2-weighted, and apparent diffusion coefficient (ADC) map. Whole-prostate segmentations were performed on each modality, and image biomarker standardization initiative (IBSI)-compliant radiomic features were extracted. Nine support vector machine (SVM) models were trained: four single-modality radiomic models (PET, T1w, T2w, ADC); three PET + MRI double-modality models (PET + T1w, PET + T2w, PET + ADC), and two baseline models (one with patient data, one image-based) for comparison. A sixfold stratified cross-validation was performed, and balanced accuracies (bAcc) of the predictions of the best-performing models were reported and compared through Student's t-tests. The predictions of the best-performing model were compared against biopsy GS (bGS). Results All radiomic models outperformed the baseline models. The best-performing (mean +/- stdv [%]) single-modality model was the ADC model (76 +/- 6%), although not significantly better (p > 0.05) than other single-modality models (T1w: 72 +/- 3%, T2w: 73 +/- 2%; PET: 75 +/- 5%). The overall best-performing model combined PET + ADC radiomics (82 +/- 5%). It significantly outperformed most other double-modality (PET + T1w: 74 +/- 5%, p = 0.026; PET + T2w: 71 +/- 4%, p = 0.003) and single-modality models (PET: p = 0.042; T1w: p = 0.002; T2w: p = 0.003), except the ADC-only model (p = 0.138). In this initial cohort, the PET + ADC model outperformed bGS overall (82.5% vs 72.4%) in the prediction of psGS. Conclusion All single- and double-modality models outperformed the baseline models, showing their potential in the prediction of GS, even with an unbalanced cohort. The best-performing model included PET + ADC radiomics, suggesting a complementary value of PSMA-PET and ADC radiomics.

Automated summary

The study evaluated the performance of combined PET and mpMRI radiomics for predicting postsurgical Gleason scores in primary prostate cancer patients. The results showed that the PET + ADC double-modality model performed the best, significantly outperforming other models, and had better predictive ability for psGS compared to biopsy GS.