The added value of PSMA PET/MR radiomics for prostate cancer staging
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The added value of PSMA PET/MR radiomics for prostate cancer staging. / Solari, Esteban Lucas; Gafita, Andrei; Schachoff, Sylvia; Bogdanović, Borjana; Villagrán Asiares, Alberto; Amiel, Thomas; Hui, Wang; Rauscher, Isabel; Visvikis, Dimitris; Maurer, Tobias; Schwamborn, Kristina; Mustafa, Mona; Weber, Wolfgang; Navab, Nassir; Eiber, Matthias; Hatt, Mathieu; Nekolla, Stephan G.
In: EUR J NUCL MED MOL I, Vol. 49, No. 2, 01.2022, p. 527-538.Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review
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TY - JOUR
T1 - The added value of PSMA PET/MR radiomics for prostate cancer staging
AU - Solari, Esteban Lucas
AU - Gafita, Andrei
AU - Schachoff, Sylvia
AU - Bogdanović, Borjana
AU - Villagrán Asiares, Alberto
AU - Amiel, Thomas
AU - Hui, Wang
AU - Rauscher, Isabel
AU - Visvikis, Dimitris
AU - Maurer, Tobias
AU - Schwamborn, Kristina
AU - Mustafa, Mona
AU - Weber, Wolfgang
AU - Navab, Nassir
AU - Eiber, Matthias
AU - Hatt, Mathieu
AU - Nekolla, Stephan G
N1 - © 2021. The Author(s).
PY - 2022/1
Y1 - 2022/1
N2 - 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.
AB - 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.
U2 - 10.1007/s00259-021-05430-z
DO - 10.1007/s00259-021-05430-z
M3 - SCORING: Journal article
C2 - 34255130
VL - 49
SP - 527
EP - 538
JO - EUR J NUCL MED MOL I
JF - EUR J NUCL MED MOL I
SN - 1619-7070
IS - 2
ER -