ArticleNuclear medicine and molecular imaging2024
Convoluted Neural Network for Detection of Clinically Significant Prostate Cancer on
Article in Nuclear medicine and molecular imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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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Who cites it
2 citing papers in PubMed.
- The Role of Artificial Intelligence in Theranostics.Journal of nuclear medicine technology · 2025Review
- Efficacy of PSMA PET/CT radiomics analysis for risk stratification in newly diagnosed prostate cancer: a multicenter study.BMC medical imaging · 2025Article
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Authors and funding
4 authors.
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Abstract
Purpose: To assess the utility of convoluted neural network (CNN) in differentiating clinically significant and insignificant prostate cancer in patients with Methods: In this retrospective study, 142 patients with clinical suspicion of prostate cancer were evaluated who underwent Results: The csPCa was revealed in 69/98 (70.4%) patients, and insignificant PCa was noticed in 29/98 (29.6%) patients. The software extracted 124 RF from the delayed Conclusion: CNN is a feasible pre-biopsy screening tool for identifying clinically significant prostate cancer and can be used as an adjunct in the initial diagnosis and early treatment planning. Supplementary Information: The online version contains supplementary material available at 10.1007/s13139-023-00832-3.
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Registered trials
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