The utility of a deep learning-based algorithm for bone scintigraphy in patient with prostate cancer-Reference-Cited by-同舟云学术

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The utility of a deep learning-based algorithm for bone scintigraphy in patient with prostate cancer

Published:2020-09-21 Issue:12 Volume:34 Page:926-931
ISSN:0914-7187
Container-title:Annals of Nuclear Medicine
language:en
Short-container-title:Ann Nucl Med

Author:

Aoki Yuki,Nakayama Michihiro,Nomura Kenta,Tomita Yui,Nakajima Kaori,Yamashina Masaaki,Okizaki Atsutaka

Funder

Bayer Yakuhin, Ltd

FUJIFILM Toyama Chemical Co., Ltd.

Nihon Medi-Physics Co., Ltd

Publisher

Springer Science and Business Media LLC

Subject

Radiology, Nuclear Medicine and imaging,General Medicine

Link

https://link.springer.com/content/pdf/10.1007/s12149-020-01524-0.pdf

Reference21 articles.

1. Love C, Din AS, Tomas MB, Kalapparambath TP, Palestro CJ. Radionuclide bone imaging: an illustrative review. Radiographics. 2003;23(2):341–58.

2. Van den Wyngaert T, Strobel K, Kampen WU, Kuwert T, van der Bruggen W, Mohan HK, et al. The EANM practice guidelines for bone scintigraphy. Eur J Nucl Med Mol Imaging. 2016;43(9):1723–38.

3. Norgaard M, Jensen AO, Jacobsen JB, Cetin K, Fryzek JP, Sorensen HT. Skeletal related events, bone metastasis and survival of prostate cancer: a population based cohort study in Denmark (1999–2007). J Urol. 2010;184(1):162–7.

4. Coleman RE. Clinical features of metastatic bone disease and risk of skeletal morbidity. Clin Cancer Res. 6243s;12(20 Pt 2):6243s–s62496249.

5. National Comprehensive Cancer Network. Prostate cancer version 1.2020. https://www.nccn.org/professionals/physician_gls/pdf/prostate.pdf.

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