Literature DB >> 16257129

ROC curves and evaluation of radiation-induced pulmonary toxicity in breast cancer.

Pehr A Lind1, Berit Wennberg, Giovanna Gagliardi, Stefan Rosfors, Ulla Blom-Goldman, Anders Lideståhl, Gunilla Svane.   

Abstract

PURPOSE: To study clinical, radiologic, and physiologic pulmonary toxicity in 128 women after adjuvant radiotherapy (RT) for breast cancer in relation to dosimetric factors. METHODS AND MATERIAL: The patients underwent pulmonary function testing before and 5 months post-RT. Similarly, computer tomography of the chest was repeated 4 months post-RT and changes were scored with a semiquantitative system. Clinical symptoms were registered and scored according to Common Toxicity Criteria. All patients underwent three-dimensional dose planning, and the ipsilateral lung volume receiving > or = 13 Gy (V13), V20, and V30 were calculated. Multiple logistic or regression analyses were used for multivariate modeling. The relation between the dosimetric factors and side effects was also analyzed with receiver operating characteristic (ROC) curves.
RESULTS: V20 was, according to multivariate modeling, the most important variable for the occurrence of the three studied side effects (p < 0.01). Age was also related to symptomatic and radiologic pneumonitis. Reduced pre-RT functional level was more common in patients developing symptomatic toxicity. The ROC areas for symptomatic pneumonitis in relation to V13, V20, and V30 were 0.69, 0.69, and 0.67, and for radiologic pneumonitis 0.85, 0.85, and 0.81.
CONCLUSIONS: Our results support the use of three-dimensional planning aimed at minimizing the percent of incidentally irradiated lung volume to reduce pulmonary toxicity. Age was also correlated with post-RT side effects. According to ROC analysis, V20 could well predict the risk for radiologic pneumonitis for the studied semiquantitative model.

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Year:  2005        PMID: 16257129     DOI: 10.1016/j.ijrobp.2005.08.011

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


  31 in total

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Review 2.  Imaging radiation-induced normal tissue injury.

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5.  Combining multiple models to generate consensus: application to radiation-induced pneumonitis prediction.

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

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7.  Reduction of radiation pneumonitis by V20-constraints in breast cancer.

Authors:  Ulla Blom Goldman; Berit Wennberg; Gunilla Svane; Håkan Bylund; Pehr Lind
Journal:  Radiat Oncol       Date:  2010-10-29       Impact factor: 3.481

8.  Using machine learning to predict radiation pneumonitis in patients with stage I non-small cell lung cancer treated with stereotactic body radiation therapy.

Authors:  Gilmer Valdes; Timothy D Solberg; Marina Heskel; Lyle Ungar; Charles B Simone
Journal:  Phys Med Biol       Date:  2016-07-27       Impact factor: 3.609

9.  A neural network model to predict lung radiation-induced pneumonitis.

Authors:  Shifeng Chen; Sumin Zhou; Junan Zhang; Fang-Fang Yin; Lawrence B Marks; Shiva K Das
Journal:  Med Phys       Date:  2007-09       Impact factor: 4.071

10.  Evaluation of multiple breathing states using a multiple instance geometry approximation (MIGA) in inverse-planned optimization for locoregional breast treatment.

Authors:  Alexander Lin; Jean M Moran; Robin B Marsh; James M Balter; Benedick A Fraass; Daniel L McShan; Marc L Kessler; Lori J Pierce
Journal:  Int J Radiat Oncol Biol Phys       Date:  2008-10-01       Impact factor: 7.038

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