Predicting the need for adaptive radiotherapy in head and neck cancer. Issue 1 (July 2015)

• Record Type: Journal Article
• Authors: Brown, Elizabeth; Owen, Rebecca; Harden, Fiona; Mengersen, Kerrie; Oestreich, Kimberley; Houghton, Whitney; Poulsen, Michael; Harris, Selina; Lin, Charles; Porceddu, Sandro
• Is Part Of: Radiotherapy and oncology. Volume 116:Issue 1(2015:Jul.)
• Page Start: 57
• Page End: 63
• Publication Date: 2015-07
• Subjects: Oncology -- Periodicals; Radiotherapy -- Periodicals; Tumors -- Periodicals; Medical Oncology -- Periodicals; Neoplasms -- radiotherapy -- Periodicals; Radiotherapy -- Periodicals; Radiothérapie -- Périodiques; Cancérologie -- Périodiques; Tumeurs -- Périodiques; Electronic journals; 616.9940642
• DOI: 10.1016/j.radonc.2015.06.025
• Languages: English
• ISSNs: 0167-8140

Abstract

<abstract xml:lang="en" abstract-type="author" id="ab005"> <title id="st005">Abstract</title> <sec> <title id="st010">Background and purpose</title> <p id="sp0005">Adaptive radiotherapy (ART) can account for the dosimetric impact of anatomical change in head and neck cancer patients; however it can be resource intensive. Consequently, it is imperative that patients likely to require ART are identified. The purpose of this study was to find predictive factors that identify oropharyngeal squamous cell carcinoma (OPC) and nasopharyngeal carcinoma (NPC) patients more likely to need ART.</p> </sec> <sec> <title id="st015">Materials and methods</title> <p id="sp0010">One hundred and ten patients with OPC or NPC were analysed. Patient demographics and tumour characteristics were compared between patients who were replanned and those that were not. Factors found to be significant were included in logistic regression models. Risk profiles were developed from these models. A dosimetric analysis was performed.</p> </sec> <sec> <title id="st020">Results</title> <p id="sp0015">Nodal disease stage, pre-treatment largest involved node size, diagnosis and initial weight (categorised in 2 groups) were identified as significant for inclusion in the model. Two models were found to be significant (<italic>p</italic> = 0.001), correctly classifying 98.2% and 96.1% of patients respectively. Three ART risk profiles were developed.</p> </sec> <sec> <title id="st025">Conclusion</title> <p id="sp0020">Predictive factors identifying OPC or NPC patients more likely to require ART were reported. A risk profile approach could facilitate the effective implementation of ART into radiotherapy departments through forward planning and appropriate resource allocation.</p> </sec> </abstract>