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2021 | 3 |
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Page 1
Evaluating a Machine Learning Tool for the Classification of Pathological Uptake in Whole-Body PSMA-PET-CT Scans.
Tomography. 2021 Jul 29;7(3):301-312. doi: 10.3390/tomography7030027.
Tomography. 2021.
PMID: 34449727
Free PMC article.
Machine Learning Facilitates Hotspot Classification in PSMA-PET/CT with Nuclear Medicine Specialist Accuracy.
Moazemi S, Khurshid Z, Erle A, Lütje S, Essler M, Schultz T, Bundschuh RA.
Moazemi S, et al. Among authors: erle a.
Diagnostics (Basel). 2020 Aug 22;10(9):622. doi: 10.3390/diagnostics10090622.
Diagnostics (Basel). 2020.
PMID: 32842599
Free PMC article.
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Decision-support for treatment with 177Lu-PSMA: machine learning predicts response with high accuracy based on PSMA-PET/CT and clinical parameters.
Moazemi S, Erle A, Khurshid Z, Lütje S, Muders M, Essler M, Schultz T, Bundschuh RA.
Moazemi S, et al. Among authors: erle a.
Ann Transl Med. 2021 May;9(9):818. doi: 10.21037/atm-20-6446.
Ann Transl Med. 2021.
PMID: 34268431
Free PMC article.
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Estimating the Potential of Radiomics Features and Radiomics Signature from Pretherapeutic PSMA-PET-CT Scans and Clinical Data for Prediction of Overall Survival When Treated with 177Lu-PSMA.
Moazemi S, Erle A, Lütje S, Gaertner FC, Essler M, Bundschuh RA.
Moazemi S, et al. Among authors: erle a.
Diagnostics (Basel). 2021 Jan 28;11(2):186. doi: 10.3390/diagnostics11020186.
Diagnostics (Basel). 2021.
PMID: 33525456
Free PMC article.
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