ArticleEuropean journal of nuclear medicine and molecular imaging2022
The added value of PSMA PET/MR radiomics for prostate cancer staging.
Article in European journal of nuclear medicine and molecular imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
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48 citing papers in PubMed, 79 citations in OpenAlex.
- [18F]F-DCFPyL PET/MRI radiomics for intraprostatic prostate cancer detection and metastases prediction using whole-gland segmentation.The British journal of radiology · 2025Trial
- Emerging frontiers and challenges of artificial intelligence in PSMA-PET imaging: pioneering a new chapter in prostate cancer care.Japanese journal of radiology · 2026Review
- Multimodal artificial intelligence in prostate cancer: integrating multiparametric MRI with clinicopathological, molecular, and functional imaging data.Abdominal radiology (New York) · 2026Review
- Multimodal artificial intelligence for prostate cancer imaging: workflow-relevant fusion of mpMRI, PSMA PET, ultrasound, and clinical data for diagnosis, local staging, and treatment personalization.Abdominal radiology (New York) · 2026Review
- An interpretableEuropean journal of nuclear medicine and molecular imaging · 2026Article
- Identifying genetics biomarkers in correlation with pathological and PSMA PET/CT characteristics in prostate cancer.EJNMMI research · 2026Article
- Biomarkers for Precision Prognosis in Prostate Cancer: Imaging, Molecular, and Integrated Approaches.Cancers · 2026Review
- Preoperative assessment of IDH status in adult glioma by integratedEJNMMI research · 2026Article
- Imaging Biomarkers in Radiotherapy.Cancers · 2026Review
- Noninvasive preoperative risk stratification of prostate cancer via a foundational model based deep learning with PSMA PET/CT.BMC cancer · 2026Article
- Machine learning-based construction and validation of an radiomics model for predicting ISUP grading in prostate cancer: a multicenter radiomics study based on [68Ga]Ga-PSMA PET/CT.European journal of nuclear medicine and molecular imaging · 2025Article
- Article
- The Role of PET Image Quantification in Modern Radiation Therapy.PET clinics · 2025Review
- Efficacy of PSMA PET/CT radiomics analysis for risk stratification in newly diagnosed prostate cancer: a multicenter study.BMC medical imaging · 2025Article
- Multimodal imaging deep learning model for predicting extraprostatic extension in prostate cancer using MpMRI and 18 F-PSMA-PET/CT.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- The value of habitat analysis based onBMC medical imaging · 2025Article
- A Robust [Journal of imaging informatics in medicine · 2025Article
- The Role of Radiomics and Artificial Intelligence Applied to Staging PSMA PET in Assessing Prostate Cancer Aggressiveness.Journal of clinical medicine · 2025Review
- Multimodal radiomics based on fluorine-18 prostate-specific membrane antigen positron emission tomography and multiparametric magnetic resonance imaging in predicting persistent prostate-specific antigen after radical prostatectomy.Quantitative imaging in medicine and surgery · 2025Article
- End-to-end [Abdominal radiology (New York) · 2025Article
Corrections and comments
- Commented on byThe role of [2023
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Authors and funding
17 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo 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.
methodsPatients with PCa, who underwent [
resultsAll 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.
conclusionAll 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.
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