Radiotherapy toxicity prediction using knowledge-constrained generalized linear model-Reference-Cited by-同舟云学术

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Radiotherapy toxicity prediction using knowledge-constrained generalized linear model

Published:2023-07-07 Issue: Volume: Page:1-11
ISSN:2472-5579
Container-title:IISE Transactions on Healthcare Systems Engineering
language:en
Short-container-title:IISE Transactions on Healthcare Systems Engineering

Author:

Hu Jiuyun¹,Fatyga Mirek²,Liu Wei²,Schild Steven E.²,Wong William W.²,Vora Sujay A.²,Li Jing³

Affiliation:

1. School of Computing & Augmented Intelligence, Arizona State University, Tempe, AZ, USA

2. Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, AZ, USA

3. H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA

Funder

Department of Radiation Oncology at Mayo Clinic Arizona with additional assistance from the North Central Cancer Treatment Group

North Central Cancer Treatment Group and Mayo Clinic with funding from the Public Health Service

Publisher

Informa UK Limited

Subject

Public Health, Environmental and Occupational Health,Safety Research,Safety, Risk, Reliability and Quality

Link

https://www.tandfonline.com/doi/pdf/10.1080/24725579.2023.2227199

Reference25 articles.

1. Development of NTCP models for head and neck cancer patients treated with three-dimensional conformal radiotherapy for xerostomia and sticky saliva: The role of dosimetric and clinical factors

2. Systematic Review of Normal Tissue Complication Models Relevant to Standard Fractionation Radiation Therapy of the Head and Neck Region Published After the QUANTEC Reports

3. Modeling late rectal toxicities based on a parameterized representation of the 3D dose distribution

4. Investigation of the support vector machine algorithm to predict lung radiation-induced pneumonitis

5. Dai, B. & Breheny, P. J. (2019). Cross validation approaches for penalized Cox regression.arXiv preprint arXiv: 1905.10432.

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