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 prost...
| Published in: | European Journal of Nuclear Medicine & Molecular Imaging Vol. 49; no. 2; pp. 527 - 539 |
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| Main Authors: | , , , , , , , , , , , , , , , , |
| Format: | diagnostic images equations & formulas pictorial research tables/charts Journal Article |
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Springer Nature
Jan2022
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=154982374&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 154982374 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16197070 NPC jtl: European Journal of Nuclear Medicine & Molecular Imaging issn: 16197070 maglogo: N pubinfo: dt: Jan2022 vid: 49 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 154982374 151377609 154982374 154982374 10.1007/s00259-021-05430-z 154982374 ppf: 527 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: The added value of PSMA PET/MR radiomics for prostate cancer staging. aug: au: 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. affil: School of Medicine, Department of Nuclear Medicine, Klinikum rechts der Isar, Technical University Munich, Munich, Germany sug: subj: Positron-Emission Tomography Magnetic Resonance Imaging Prostatic Neoplasms Neoplasm Staging Neoplasm Grading Postoperative Period Human Retrospective Design Cancer Patients Prostatectomy Biological Markers Support Vector Machine Radiologists Urologists 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. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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