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. 2022 Jan;49(2):527-538.
doi: 10.1007/s00259-021-05430-z. Epub 2021 Jul 13.

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

Esteban Lucas Solari  1 , Andrei Gafita  2 , Sylvia Schachoff  2 , Borjana Bogdanović  2 , Alberto Villagrán Asiares  2 , Thomas Amiel  3 , Wang Hui  2 , Isabel Rauscher  2 , Dimitris Visvikis  4 , Tobias Maurer  5 , Kristina Schwamborn  6 , Mona Mustafa  2 , Wolfgang Weber  2 , Nassir Navab  7 , Matthias Eiber  2 , Mathieu Hatt  4 , Stephan G Nekolla  2
Affiliations

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

Esteban Lucas Solari et al. Eur J Nucl Med Mol Imaging. 2022 Jan.

Abstract

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.

Keywords: Gleason score; PET/MRI; PSMA; Prostate cancer; Radiomics.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Feature extraction workflow for PSMA-PET and mpMRI images
Fig. 2
Fig. 2
Examples of radiomic signatures (three most relevant features only) of three patients, one per GS category
Fig. 3
Fig. 3
Training and validation workflow of each SVM model for the prediction of GS
Fig. 4
Fig. 4
Boxplot of the balanced accuracies of all best-performing models on the validation sets
See this image and copyright information in PMC

Comment in

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