ArticleFrontiers in oncology2022
Evaluation of a radiomics nomogram derived from Fluoride-18 PSMA-1007 PET/CT for risk stratification in newly diagnosed prostate cancer.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed, 13 citations in OpenAlex.
- Efficacy of PSMA PET/CT radiomics analysis for risk stratification in newly diagnosed prostate cancer: a multicenter study.BMC medical imaging · 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
- Review
- A Critical Analysis of the Robustness of Radiomics to Variations in Segmentation Methods inDiagnostics (Basel, Switzerland) · 2023Article
- Emerging Role of Nuclear Medicine in Prostate Cancer: Current State and Future Perspectives.Cancers · 2023Review
- Editorial: Opportunities for PET imaging for the identification, staging, and monitoring of cancers.Frontiers in oncology · 2023Article
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: The aim of this study was to evaluate the performance of Fluoride-18 ( Materials and Methods: A total of 161 pathological proven PCa patients in a single center were retrospectively analyzed. Prostate-specific antigen (PSA), Gleason Score (GS) and PET/CT indexes (SUVmin, SUVmax, and SUVmean) were compared according to risk stratification. Radiomics features were extracted from PCa Results: The radiomics signature, which consisted of 30 selected features, was significantly associated with PSA level and Gleason score (P < 0.001 for both primary and validation cohorts). Predictors contained in the individualized prediction nomogram included radiomics score, PSA level and metastasis status. The model showed good discrimination with an area under the ROC curve of 0.719 for the GS. Combined clinical-radiomic score nomogram had a similar benefit to utilizing the PET/CT radiomic features alone for GS discrimination. Conclusion: The
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