A deep learning algorithm for detecting lytic bone lesions of multiple myeloma on CT-Reference-Cited by-同舟云学术

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A deep learning algorithm for detecting lytic bone lesions of multiple myeloma on CT

Published:2022-08-18 Issue:1 Volume:52 Page:91-98
ISSN:0364-2348
Container-title:Skeletal Radiology
language:en
Short-container-title:Skeletal Radiol

Author:

Faghani Shahriar,Baffour Francis I.,Ringler Michael D.,Hamilton-Cave Matthew,Rouzrokh Pouria,Moassefi Mana,Khosravi Bardia,Erickson Bradley J.

Publisher

Springer Science and Business Media LLC

Subject

Radiology, Nuclear Medicine and imaging

Link

https://link.springer.com/content/pdf/10.1007/s00256-022-04160-z.pdf

Reference32 articles.

1. Durie BG, Salmon SE. A clinical staging system for multiple myeloma. Correlation of measured myeloma cell mass with presenting clinical features, response to treatment, and survival. Cancer. 1975;36:842–54.

2. Hillengass J, Usmani S, Rajkumar SV, Durie BGM, Mateos M-V, Lonial S, et al. International myeloma working group consensus recommendations on imaging in monoclonal plasma cell disorders. Lancet Oncol. 2019;20:e302–12.

3. Moulopoulos LA, Koutoulidis V, Hillengass J, Zamagni E, Aquerreta JD, Roche CL, et al. Recommendations for acquisition, interpretation and reporting of whole body low dose CT in patients with multiple myeloma and other plasma cell disorders: a report of the IMWG Bone Working Group. Blood Cancer J. 2018;8:95.

4. Horger M, Claussen CD, Bross-Bach U, Vonthein R, Trabold T, Heuschmid M, et al. Whole-body low-dose multidetector row-CT in the diagnosis of multiple myeloma: an alternative to conventional radiography. Eur J Radiol. 2005;54:289–97.

5. Gavriatopoulou M, Βoultadaki A, Koutoulidis V, Ntanasis-Stathopoulos I, Bourgioti C, Malandrakis P, et al. The role of low dose whole body CT in the detection of progression of patients with smoldering multiple myeloma. Blood Cancer J. 2020;10:93.

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