Document Type



Dentistry | Medicine and Health Sciences | Oral Biology and Oral Pathology


Purpose of review

Mucositis has long been viewed as an unavoidable consequence of high-dose chemotherapy and/or radiation. Management has been directed to supportive care including oral pain control, nutritional support, infection treatment and control of diarrhea. While these interventions have been valuable for clinical management, they have not been collectively directed to molecularly targeted prevention and treatment. This review addresses recent advances regarding mucosal injury in cancer patients, with emphasis on symptom clusters, genetically-based tissue susceptibility and risk prediction, imaging technology, and computational biology.

Recent findings

Modeling of symptom clusters in cancer patients continues to mature. Although integration of mucositis into the paradigm is at an early stage, recent reports suggest that important molecular and clinical insights will emerge in this regard. Initial studies of genetic-based tissue risk are also providing a research basis that may lead to clinical risk prediction models.

These advances are in part being engineered via new imaging and computational biology technologies, drawing upon literature in non-mucositis systems. Just as the past decade has been hallmarked by linkage of pathobiology with clinical expression of mucosal toxicity, the next decade promises to identify new molecular interactions and risk prediction models based on novel application of the analytic technologies.


Recent research has culminated in convergence of molecular pathobiology with models of symptom clusters, genetic-based risk, and imaging and computational biology. The field is poised to further delineate this paradigm, with the goal of development of molecularly targeted drugs and devices for mucositis management.


Curr Opin Oncol. Author manuscript; available in PMC 2013 July 19.

Published in final edited form as: Curr Opin Oncol. 2010 July; 22(4): 318–322.

doi: 10.1097/CCO.0b013e32833a9fab

PMCID: PMC3716390