2023
DOI: 10.1007/s12325-023-02460-x
|View full text |Cite
|
Sign up to set email alerts
|

Management of Older Patients with Head and Neck Cancer: A Comprehensive Review

Abstract: The projected increase in life expectancy over the next few decades is expected to result in a rise in age-related diseases, including cancer. Head and neck cancer (HNC) is a worldwide health problem with high rates of morbidity and mortality. In this report, we have critically reviewed the literature reporting the management of older patients with HNC. Older adults are more prone to complications and toxicities secondary to HNC treatment, especially those patients who are frail or have comorbidities. Thus, th… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

1
1
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
6

Relationship

0
6

Authors

Journals

citations
Cited by 6 publications
(2 citation statements)
references
References 110 publications
1
1
0
Order By: Relevance
“…Furthermore, Mohanti et al reported a partial response rate of 37% in patients with advanced head and neck cancer treated with an RT protocol of 20 Gy delivered in 5 fractions of 4 Gy each over one week (10), while Vargas A. documented significant symptom alleviation in patients with soft tissue tumors treated with a single fraction of RT (11). These studies collectively reinforce the potential of hypofractionated RT to effectively manage pain in this patient population (14)(15)(16).…”
Section: Discussionsupporting
confidence: 62%
“…Furthermore, Mohanti et al reported a partial response rate of 37% in patients with advanced head and neck cancer treated with an RT protocol of 20 Gy delivered in 5 fractions of 4 Gy each over one week (10), while Vargas A. documented significant symptom alleviation in patients with soft tissue tumors treated with a single fraction of RT (11). These studies collectively reinforce the potential of hypofractionated RT to effectively manage pain in this patient population (14)(15)(16).…”
Section: Discussionsupporting
confidence: 62%
“…Few studies have been done to predict quality of life based on questionnaires using advanced machine learning. Predicting quality of life helps identify patients at higher risk of negative impacts, enabling targeted interventions like psychological support or symptom management strategies for those needing additional assistance [33][34][35][36]. We aim to predict the quality of life using light gradient Boost Tree classifiers based on questionnaires from oral cancer patients.…”
Section: Background and Introductionmentioning
confidence: 99%