BACKGROUND: Toxicity of the respiratory system is quite common after radiotherapy of thoracic tumors; breast cancer patients represent one of the groups for which there is also a long expected survival. The quantification of lung tissue response to irradiation is important in designing treatments associated with a minimum of complications and maximum tumor control. METHODS: The study population consisted of 68 patients who received irradiation for breast cancer at Stage II. Radiation pneumonitis was retrospectively assessed on the basis of clinical symptoms and radiological findings. For each patient, a measure of the exposure (i.e., the lung dose-volume histogram [DVH]) and a measure of the outcome was available. Based on these data, a maximum likelihood fitting to the relative seriality model was performed. The uncertainties of the model parameters were calculated and their impact on the dose-response curve was studied. The optimum parameter set was then applied to 5 other patient groups treated for breast cancer, and the normal tissue complication probability (NTCP) was calculated. Each group was individuated by the radiotherapy treatment technique used; the dose distribution in the lung was described by a mean DVH and the incidence of radiation pneumonitis in each group was known. Lung radiosensitivity was assumed to be homogeneous through all of the calculations. RESULTS: The relative seriality model could describe the dataset. The volume effect was found to be relevant in the description of radiation pneumonitis. Age was found to be associated with increased risk of radiation pneumonitis. Two distinct dose-response curves were obtained by splitting the group according to age. The impact of the parameter uncertainties on the dose-response curve was quite large. The parameter set determined could be used predictively on 3 of the 5 patient groups. CONCLUSION: The complication data could be modeled with the relative seriality model. However, further independent datasets, classified according to the same endpoint, must be analyzed before introducing NTCP modeling in clinical practice.
BACKGROUND:Toxicity of the respiratory system is quite common after radiotherapy of thoracic tumors; breast cancerpatients represent one of the groups for which there is also a long expected survival. The quantification of lung tissue response to irradiation is important in designing treatments associated with a minimum of complications and maximum tumor control. METHODS: The study population consisted of 68 patients who received irradiation for breast cancer at Stage II. Radiation pneumonitis was retrospectively assessed on the basis of clinical symptoms and radiological findings. For each patient, a measure of the exposure (i.e., the lung dose-volume histogram [DVH]) and a measure of the outcome was available. Based on these data, a maximum likelihood fitting to the relative seriality model was performed. The uncertainties of the model parameters were calculated and their impact on the dose-response curve was studied. The optimum parameter set was then applied to 5 other patient groups treated for breast cancer, and the normal tissue complication probability (NTCP) was calculated. Each group was individuated by the radiotherapy treatment technique used; the dose distribution in the lung was described by a mean DVH and the incidence of radiation pneumonitis in each group was known. Lung radiosensitivity was assumed to be homogeneous through all of the calculations. RESULTS: The relative seriality model could describe the dataset. The volume effect was found to be relevant in the description of radiation pneumonitis. Age was found to be associated with increased risk of radiation pneumonitis. Two distinct dose-response curves were obtained by splitting the group according to age. The impact of the parameter uncertainties on the dose-response curve was quite large. The parameter set determined could be used predictively on 3 of the 5 patient groups. CONCLUSION: The complication data could be modeled with the relative seriality model. However, further independent datasets, classified according to the same endpoint, must be analyzed before introducing NTCP modeling in clinical practice.
Authors: Jenine K Sanzari; Gabriel S Krigsfeld; Anne L Shuman; Antonia K Diener; Liyong Lin; Wilfried Mai; Ann R Kennedy Journal: Life Sci Space Res (Amst) Date: 2015-04
Authors: Jingfang Mao; Zafer Kocak; Sumin Zhou; Jennifer Garst; Elizabeth S Evans; Junan Zhang; Nicole A Larrier; Donna R Hollis; Rodney J Folz; Lawrence B Marks Journal: Int J Radiat Oncol Biol Phys Date: 2007-02-02 Impact factor: 7.038
Authors: P Mavroidis; C Shi; G A Plataniotis; M G Delichas; B Costa Ferreira; S Rodriguez; B K Lind; N Papanikolaou Journal: Br J Radiol Date: 2010-09-21 Impact factor: 3.039