1. The added value of PSMA PET/MR radiomics for prostate cancer staging
- Author
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Stephan G. Nekolla, Mona Mustafa, Wang Hui, Thomas Amiel, Kristina Schwamborn, Alberto Villagran Asiares, Sylvia Schachoff, Matthias Eiber, Borjana Bogdanovic, Isabel Rauscher, Tobias Maurer, Esteban Lucas Solari, Mathieu Hatt, Wolfgang A. Weber, Dimitris Visvikis, Andrei Gafita, and Nassir Navab
- Subjects
Male ,0301 basic medicine ,medicine.medical_treatment ,Naturwissenschaften ,03 medical and health sciences ,Prostate cancer ,0302 clinical medicine ,Original Article ,Advanced Image Analyses (Radiomics and Artificial Intelligence) ,PET/MRI ,PSMA ,Radiomics ,Gleason score ,medicine ,Informatik, Wissen, Systeme ,Humans ,Effective diffusion coefficient ,Radiology, Nuclear Medicine and imaging ,Gleason scores ,ddc:610 ,Multiparametric Magnetic Resonance Imaging ,Retrospective Studies ,Prostatectomy ,business.industry ,Prostatic Neoplasms ,General Medicine ,Patient data ,medicine.disease ,ddc ,030104 developmental biology ,030220 oncology & carcinogenesis ,Psma pet ,ddc:000 ,ddc:500 ,Neoplasm Grading ,Prostate cancer staging ,business ,Nuclear medicine - 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.
- Published
- 2021
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