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

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.

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).

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.

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.

European journal of nuclear medicine and molecular imaging. 2021 Jul 13 [Epub ahead of print]

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

School of Medicine, Department of Nuclear Medicine, Klinikum rechts der Isar, Technical University Munich, Munich, Germany. ., School of Medicine, Department of Nuclear Medicine, Klinikum rechts der Isar, Technical University Munich, Munich, Germany., School of Medicine, Department of Urology, Klinikum rechts der Isar, Technical University Munich, Munich, Germany., INSERM, UMR 1101, LaTIM, Univ Brest, Brest, France., Department of Urology and Martini-Klinik Prostate Cancer Center, University Hospital Hamburg-Eppendorf, Hamburg, Germany., School of Medicine, Institute of Pathology, Klinikum rechts der Isar, Technical University Munich, Munich, Germany., School of Computer Science, Computer Aided Medical Procedures and Augmented Reality, Technical University Munich, Munich, Germany.

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