Deep learning for identification of critical regions associated with toxicities after liver stereotactic body radiation therapy-Reference-Cited by-同舟云学术

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Deep learning for identification of critical regions associated with toxicities after liver stereotactic body radiation therapy

Published:2020-06-03 Issue:8 Volume:47 Page:3721-3731
ISSN:0094-2405
Container-title:Medical Physics
language:en
Short-container-title:Med. Phys.

Author:

Ibragimov Bulat¹,Toesca Diego A. S.²,Chang Daniel T.²,Yuan Yixuan³,Koong Albert C.⁴,Xing Lei²,Vogelius Ivan R.⁵

Affiliation:

1. Department of Computer Science University of Copenhagen Copenhagen Denmark

2. Department of Radiation Oncology Stanford University School of Medicine Stanford CA USA

3. Department of Electronic Engineering City University of Hong Kong Hong Kong

4. Department of Radiation Oncology MD Anderson Cancer Center Houston Texas

5. Department of Oncology Faulty of Health & Medical Sciences Rigshospitalet University of Copenhagen Copenhagen Denmark

Funder

Varian Medical Systems

Publisher

Wiley

Subject

General Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/mp.14235

Reference45 articles.

1. The Lessons of QUANTEC: Recommendations for Reporting and Gathering Data on Dose–Volume Dependencies of Treatment Outcome

2. Lymphocyte Count Kinetics, Factors Associated with the End-of-Radiation-Therapy Lymphocyte Count, and Risk of Infection in Patients with Solid Malignant Tumors Treated with Curative-Intent Radiation Therapy

3. Assessment of hepatic function decline after stereotactic body radiation therapy for primary liver cancer

4. Radiotherapy toxicity

5. Sparing the region of the salivary gland containing stem cells preserves saliva production after radiotherapy for head and neck cancer

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