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Precision medicine for risk prediction of oral complications of cancer therapy–The example of oral mucositis in patients receiving radiation therapy for cancers of the head and neck

Oral complications of cancer therapy are common, markedly symptomatic, negatively impact patients' quality of life, and add significantly to the cost of care. Patients' risk of treatment-related toxicities is not uniform; most patients suffer at least one side effect, while others tolerate...

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Detalles Bibliográficos
Autor principal: Sonis, Stephen T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9435998/
https://www.ncbi.nlm.nih.gov/pubmed/36060117
http://dx.doi.org/10.3389/froh.2022.917860
Descripción
Sumario:Oral complications of cancer therapy are common, markedly symptomatic, negatively impact patients' quality of life, and add significantly to the cost of care. Patients' risk of treatment-related toxicities is not uniform; most patients suffer at least one side effect, while others tolerate treatment without any. Understanding those factors which impact risk provides opportunities to customize cancer treatment plans to optimize tumor kill and minimize regimen-related toxicities. Oral mucositis (OM) is an iconic example of a clinically significant and common complication of head and neck radiotherapy. Individuals' OM risk is governed by the cumulative impact of factors related to treatment, the tumor, and the patient. In addition to OM risk prediction, a second opportunity to apply precision medicine will evolve as viable treatment options become available. Patients vary widely in how well or poorly they respond to specific treatments. What works well in one individual, might fail in another. Prospective determination of the likelihood of a patient's response or non-response is based on a range of biological interactions. Coupled with risk determination, the application of precision medicine will allow caregivers, patients, and payers to integrate risk/benefit to optimize the probability that the best treatment is be given to the most appropriate patients.