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Did you mean B. curzen (52 results)?
The hyaluronan receptor RHAMM regulates extracellular-regulated kinase.
Zhang S, Chang MC, Zylka D, Turley S, Harrison R, Turley EA. Zhang S, et al. J Biol Chem. 1998 May 1;273(18):11342-8. doi: 10.1074/jbc.273.18.11342. J Biol Chem. 1998. PMID: 9556628 Free article.
We have identified two RHAMM (receptor for hyaluronan-mediated motility) isoforms that encode an alternatively spliced exon 4 (Hall, C. L., Yang, B., Yang, X., Zhang, S., Turley, M., Samuel, S., Lange, L. A., Wang, C., Curpen, G. ...
We have identified two RHAMM (receptor for hyaluronan-mediated motility) isoforms that encode an alternatively spliced exon 4 (Hall, C. L., …
Breast-Lesion Characterization using Textural Features of Quantitative Ultrasound Parametric Maps.
Sadeghi-Naini A, Suraweera H, Tran WT, Hadizad F, Bruni G, Rastegar RF, Curpen B, Czarnota GJ. Sadeghi-Naini A, et al. Sci Rep. 2017 Oct 20;7(1):13638. doi: 10.1038/s41598-017-13977-x. Sci Rep. 2017. PMID: 29057899 Free PMC article.
This study evaluated, for the first time, the efficacy of quantitative ultrasound (QUS) spectral parametric maps in conjunction with texture-analysis techniques to differentiate non-invasively benign versus malignant breast lesions. Ultrasound B-mode images and radiofreque …
This study evaluated, for the first time, the efficacy of quantitative ultrasound (QUS) spectral parametric maps in conjunction with texture …
How Does Diagnostic Accuracy Evolve with Increased Breast MRI Experience?
Wu T, Alikhassi A, Curpen B. Wu T, et al. Tomography. 2023 Nov 6;9(6):2067-2078. doi: 10.3390/tomography9060162. Tomography. 2023. PMID: 37987348 Free PMC article.
The average radiologist's PPV3 was 16 6% (definition A) and 25 8% (definition B). MRI experience in years correlated significantly with positive cases (definition B, OR = 1.05, p = 0.03), independent of BPE or FGT. Diagnostic accuracy improved exponentially with inc …
The average radiologist's PPV3 was 16 6% (definition A) and 25 8% (definition B). MRI experience in years correlated significantly wi …
Machine learning analysis of breast ultrasound to classify triple negative and HER2+ breast cancer subtypes.
Ferre R, Elst J, Senthilnathan S, Lagree A, Tabbarah S, Lu FI, Sadeghi-Naini A, Tran WT, Curpen B. Ferre R, et al. Breast Dis. 2023;42(1):59-66. doi: 10.3233/BD-220018. Breast Dis. 2023. PMID: 36911927
This study aimed to evaluate the diagnostic performance of machine learning (ML) classification of newly diagnosed breast masses into TN versus non-TN (NTN) and HER2+ versus HER2 negative (HER2-) breast cancer, using radiomic features extracted from grayscale ultrasound (US) b …
This study aimed to evaluate the diagnostic performance of machine learning (ML) classification of newly diagnosed breast masses into TN ver …