Determining risk and predictors of head and neck cancer treatment-related lymphedema: A clinicopathologic and dosimetric data mining approach using interpretable machine learning and ensemble feature selection-Reference-Cited by-同舟云学术

Determining risk and predictors of head and neck cancer treatment-related lymphedema: A clinicopathologic and dosimetric data mining approach using interpretable machine learning and ensemble feature selection

Published:2024-05 Issue: Volume:46 Page:100747
ISSN:2405-6308
Container-title:Clinical and Translational Radiation Oncology
language:en
Short-container-title:Clinical and Translational Radiation Oncology

Author:

Teo P. Troy,Rogacki Kevin^ORCID,Gopalakrishnan Mahesh,Das Indra J^ORCID,Abazeed Mohamed E,Mittal Bharat B,Gentile Michelle

Funder

American Lung Association

Canadian Institutes of Health Research

National Institutes of Health

Publisher

Elsevier BV

Reference52 articles.

1. Late soft tissue complications of head and neck cancer therapy: lymphedema and fibrosis;Deng;J Natl Cancer Inst Monogr,2019

2. Patient experience of head and neck lymphedema therapy: a qualitative study;Deng;Support Care Cancer,2019

3. Lymphoedema following treatment for head and neck cancer: impact on patients, and beliefs of health professionals;McGarvey;Eur J Cancer Care,2014

4. Comparing the prevalence, location, and severity of head and neck lymphedema after postoperative radiotherapy for oral cavity cancers and definitive chemoradiotherapy for oropharyngeal, laryngeal, and hypopharyngeal cancers;Jeans;Head Neck,2020

5. Biomarkers associated with lymphedema and fibrosis in patients with cancer of the head and neck;Ridner;Lymphat Res Biol,2018

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1. Clinical, Pathologic, and Dosimetric Predictors of Head and Neck Lymphedema Following Definitive or Adjuvant Radiation Therapy for Head and Neck Cancer;Advances in Radiation Oncology;2024-09

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