A New Model for Predicting Acute Mucosal Toxicity in Head-and-Neck Cancer Patients Undergoing Radiotherapy With Altered Schedules
- Laboratory of Medical Physics and Expert Systems, Regina Elena National Cancer Institute, Rome (Italy)
- Department of Medical Physics, Regional Cancer Hospital C.R.O.B, Rionero in Vulture (Italy)
- Department of Radiotherapy, Regina Elena National Cancer Institute, Rome (Italy)
- Department of Medical Physics, Istituto Scientifico Romagnolo per lo Studio e la Cura dei tumori, Meldola (Italy)
Purpose: One of the worst radiation-induced acute effects in treating head-and-neck (HN) cancer is grade 3 or higher acute (oral and pharyngeal) mucosal toxicity (AMT), caused by the killing/depletion of mucosa cells. Here we aim to testing a predictive model of the AMT in HN cancer patients receiving different radiotherapy schedules. Methods and Materials: Various radiotherapeutic schedules have been reviewed and classified as tolerable or intolerable based on AMT severity. A modified normal tissue complication probability (NTCP) model has been investigated to describe AMT data in radiotherapy regimens, both conventional and altered in dose and overall treatment time (OTT). We tested the hypothesis that such a model could also be applied to identify intolerable treatment and to predict AMT. This AMT NTCP model has been compared with other published predictive models to identify schedules that are either tolerable or intolerable. The area under the curve (AUC) was calculated for all models, assuming treatment tolerance as the gold standard. The correlation between AMT and the predicted toxicity rate was assessed by a Pearson correlation test. Results: The AMT NTCP model was able to distinguish between acceptable and intolerable schedules among the data available for the study (AUC = 0.84, 95% confidence interval = 0.75-0.92). In the equivalent dose at 2 Gy/fraction (EQD2) vs OTT space, the proposed model shows a trend similar to that of models proposed by other authors, but was superior in detecting some intolerable schedules. Moreover, it was able to predict the incidence of {>=}G3 AMT. Conclusion: The proposed model is able to predict {>=}G3 AMT after HN cancer radiotherapy, and could be useful for designing altered/hypofractionated schedules to reduce the incidence of AMT.
- OSTI ID:
- 22149431
- Journal Information:
- International Journal of Radiation Oncology, Biology and Physics, Vol. 83, Issue 5; Other Information: Copyright (c) 2012 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.; Country of input: International Atomic Energy Agency (IAEA); ISSN 0360-3016
- Country of Publication:
- United States
- Language:
- English
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