Literature DB >> 9747813

Radiation pneumonitis as a function of mean lung dose: an analysis of pooled data of 540 patients.

S L Kwa1, J V Lebesque, J C Theuws, L B Marks, M T Munley, G Bentel, D Oetzel, U Spahn, M V Graham, R E Drzymala, J A Purdy, A S Lichter, M K Martel, R K Ten Haken.   

Abstract

PURPOSE: To determine the relation between the incidence of radiation pneumonitis and the three-dimensional dose distribution in the lung. METHODS AND MATERIALS: In five institutions, the incidence of radiation pneumonitis was evaluated in 540 patients. The patients were divided into two groups: a Lung group, consisting of 399 patients with lung cancer and 1 esophagus cancer patient and a Lymph./Breast group with 78 patients treated for malignant lymphoma, 59 for breast cancer, and 3 for other tumor types. The dose per fraction varied between 1.0 and 2.7 Gy and the prescribed total dose between 20 and 92 Gy. Three-dimensional dose calculations were performed with tissue density inhomogeneity correction. The physical dose distribution was converted into the biologically equivalent dose distribution given in fractions of 2 Gy, the normalized total dose (NTD) distribution, by using the linear quadratic model with an alpha/beta ratio of 2.5 and 3.0 Gy. Dose-volume histograms (DVHs) were calculated considering both lungs as one organ and from these DVHs the mean (biological) lung dose, NTDmean, was obtained. Radiation pneumonitis was scored as a complication when the pneumonitis grade was grade 2 (steroids needed for medical treatment) or higher. For statistical analysis the conventional normal tissue complication probability (NTCP) model of Lyman (with n=1) was applied along with an institutional-dependent offset parameter to account for systematic differences in scoring patients at different institutions.
RESULTS: The mean lung dose, NTDmean, ranged from 0 to 34 Gy and 73 of the 540 patients experienced pneumonitis, grade 2 or higher. In all centers, an increasing pneumonitis rate was observed with increasing NTDmean. The data were fitted to the Lyman model with NTD50=31.8 Gy and m=0.43, assuming that for all patients the same parameter values could be used. However, in the low dose range at an NTDmean between 4 and 16 Gy, the observed pneumonitis incidence in the Lung group (10%) was significantly (p=0.02) higher than in the Lymph./Breast group (1.4%). Moreover, between the Lung groups of different institutions, also significant (p=0.04) differences were present: for centers 2, 3, and 4, the pneumonitis incidence was about 13%, whereas for center 5 only 3%. Explicitly accounting for these differences by adding center-dependent offset values for the Lung group, improved the data fit significantly (p < 10(-5)) with NTD50=30.5+/-1.4 Gy and m=0.30+/-0.02 (+/-1 SE) for all patients, and an offset of 0-11% for the Lung group, depending on the center.
CONCLUSIONS: The mean lung dose, NTDmean, is relatively easy to calculate, and is a useful predictor of the risk of radiation pneumonitis. The observed dose-effect relation between the NTDmean and the incidence of radiation pneumonitis, based on a large clinical data set, might be of value in dose-escalating studies for lung cancer. The validity of the obtained dose-effect relation will have to be tested in future studies, regarding the influence of confounding factors and dose distributions different from the ones in this study.

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Year:  1998        PMID: 9747813     DOI: 10.1016/s0360-3016(98)00196-5

Source DB:  PubMed          Journal:  Int J Radiat Oncol Biol Phys        ISSN: 0360-3016            Impact factor:   7.038


  142 in total

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6.  Improving normal tissue complication probability models: the need to adopt a "data-pooling" culture.

Authors:  Joseph O Deasy; Søren M Bentzen; Andrew Jackson; Randall K Ten Haken; Ellen D Yorke; Louis S Constine; Ashish Sharma; Lawrence B Marks
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Review 7.  Accurate accumulation of dose for improved understanding of radiation effects in normal tissue.

Authors:  David A Jaffray; Patricia E Lindsay; Kristy K Brock; Joseph O Deasy; W A Tomé
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8.  Combining multiple models to generate consensus: application to radiation-induced pneumonitis prediction.

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9.  Investigation of the support vector machine algorithm to predict lung radiation-induced pneumonitis.

Authors:  Shifeng Chen; Sumin Zhou; Fang-Fang Yin; Lawrence B Marks; Shiva K Das
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Authors:  Amit Sawant; Raghu Venkat; Vikram Srivastava; David Carlson; Sergey Povzner; Herb Cattell; Paul Keall
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