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Predicting clinically significant prostate cancer from quantitative image features including compressed sensing radial MRI of prostate perfusion using machine learning: comparison with PI-RADS v2 assessment scores

Research article (Quantitative Imaging in Medicine and Surgery, 2020) · cited 26× · AI/ML
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Predicting clinically significant prostate cancer from quantitative image features including compressed sensing radial MRI of prostate perfusion using machine learning: comparison with PI-RADS v2 assessment scores

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Predicting clinically significant prostate cancer from quantitative image features including compressed sensing radial MRI of prostate perfusion using machine learning: comparison with PI-RADS v2 assessment scores is a scholarly article[1].

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  • Predicting clinically significant prostate cancer from quantitative image features including compressed sensing radial MRI of prostate perfusion using machine learning: comparison with PI-RADS v2 assessment scores's instance of is recorded as scholarly article[2].

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  1. [1] ↑ . Wikidata. wikidata.org.

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APA 4ort.xyz Knowledge Graph. (2026). Predicting clinically significant prostate cancer from quantitative image features including compressed sensing radial MRI of prostate perfusion using machine learning: comparison with PI-RADS v2 assessment scores. Retrieved May 24, 2026, from https://4ort.xyz/entity/predicting-clinically-significant-prostate-cancer-from-quantitative-image-features-including-compressed-sensing-radial-m
MLA “Predicting clinically significant prostate cancer from quantitative image features including compressed sensing radial MRI of prostate perfusion using machine learning: comparison with PI-RADS v2 assessment scores.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/predicting-clinically-significant-prostate-cancer-from-quantitative-image-features-including-compressed-sensing-radial-m.
BibTeX @misc{4ortxyz_predicting-clinically-significant-prostate-cancer-from-quantitative-image-features-including-compressed-sensing-radial-m_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Predicting clinically significant prostate cancer from quantitative image features including compressed sensing radial MRI of prostate perfusion using machine learning: comparison with PI-RADS v2 assessment scores}}, year = {2026}, url = {https://4ort.xyz/entity/predicting-clinically-significant-prostate-cancer-from-quantitative-image-features-including-compressed-sensing-radial-m}, note = {Accessed: 2026-05-24}}
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