Literature DB >> 12865174

Dose-volume analysis of lung complications in the radiation treatment of malignant thymoma: a retrospective review.

Vitali Moiseenko1, Tim Craig, Andrea Bezjak, Jake Van Dyk.   

Abstract

BACKGROUND AND
PURPOSE: Radiation pneumonitis and fibrosis are limiting factors in radiation treatment (RT) of thoracic tumors. The objectives of this study were to quantify the influence of irradiated lung volume and dose on lung response, and to evaluate the influence of other prognostic factors.
MATERIAL AND METHODS: Treatment histories of 55 thymoma patients were evaluated retrospectively for radiation pneumonitis and fibrosis. Complications were scored as pneumonitis if observed within 6 months of completion of RT, and as fibrosis if observed after 6 months. Complications were classified as 'all pneumonitis' and 'all fibrosis' if a patient either showed symptoms (such as chronic cough and dyspnea) or radiographic changes in lung. The second group scored as 'symptomatic pneumonitis' and 'symptomatic fibrosis' consisted of patients that exhibited clinical symptoms. Dose-volume data were estimated using representative anatomies combined with available individual dose data. The Lyman NTCP model was used to assess the dependence of lung complication incidence on dose and volume.
RESULTS: The derived values of the parameters governing dose-volume dependence for symptomatic complications agreed with currently accepted and recently published values within the margins of error. Dose-response curves for complications that included radiographic changes were less steep than for symptomatic complications. The volume dependence for symptomatic fibrosis was more pronounced compared to all fibrosis. A strong correlation was observed between developing pneumonitis and developing fibrosis.
CONCLUSIONS: The long survival allowed the assessment of lung complication data in thymoma patients for both acute and late response. Mean dose in lung strongly correlated with lung complications that manifest clinically. The determination of the dose-volume dependence is affected by the choice of endpoints (i.e. whether complications are scored based on clinical symptoms or radiographic changes not accompanied by clinical symptoms).

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Mesh:

Year:  2003        PMID: 12865174     DOI: 10.1016/s0167-8140(03)00003-3

Source DB:  PubMed          Journal:  Radiother Oncol        ISSN: 0167-8140            Impact factor:   6.280


  15 in total

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

Authors:  Shiva K Das; Shifeng Chen; Joseph O Deasy; Sumin Zhou; Fang-Fang Yin; Lawrence B Marks
Journal:  Med Phys       Date:  2008-11       Impact factor: 4.071

2.  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
Journal:  Med Phys       Date:  2007-10       Impact factor: 4.071

3.  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

4.  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

5.  Proton therapy radiation pneumonitis local dose-response in esophagus cancer patients.

Authors:  Alfredo E Echeverria; Matthew McCurdy; Richard Castillo; Vincent Bernard; Natalia Velez Ramos; William Buckley; Edward Castillo; Ping Liu; Josue Martinez; Thomas Guerrero
Journal:  Radiother Oncol       Date:  2012-11-02       Impact factor: 6.280

6.  Using generalized equivalent uniform dose atlases to combine and analyze prospective dosimetric and radiation pneumonitis data from 2 non-small cell lung cancer dose escalation protocols.

Authors:  Fan Liu; Ellen D Yorke; José S A Belderbos; Gerben R Borst; Kenneth E Rosenzweig; Joos V Lebesque; Andrew Jackson
Journal:  Int J Radiat Oncol Biol Phys       Date:  2012-05-05       Impact factor: 7.038

7.  Using patient data similarities to predict radiation pneumonitis via a self-organizing map.

Authors:  Shifeng Chen; Sumin Zhou; Fang-Fang Yin; Lawrence B Marks; Shiva K Das
Journal:  Phys Med Biol       Date:  2007-12-19       Impact factor: 3.609

8.  Predicting lung radiotherapy-induced pneumonitis using a model combining parametric Lyman probit with nonparametric decision trees.

Authors:  Shiva K Das; Sumin Zhou; Junan Zhang; Fang-Fang Yin; Mark W Dewhirst; Lawrence B Marks
Journal:  Int J Radiat Oncol Biol Phys       Date:  2007-07-15       Impact factor: 7.038

9.  Thymic tumors and results of radiotherapy.

Authors:  Sureyya Sarıhan; Ahmet Sami Bayram; Cengiz Gebitekin; Omer Yerci; Deniz Sıgırlı
Journal:  Rep Pract Oncol Radiother       Date:  2018-02-12

10.  Pulmonary Toxicity after Total Body Irradiation-An Underrated Complication? Estimation of Risk via Normal Tissue Complication Probability Calculations and Correlation with Clinical Data.

Authors:  Michael Oertel; Christopher Kittel; Jonas Martel; Jan-Henrik Mikesch; Marco Glashoerster; Matthias Stelljes; Hans Theodor Eich
Journal:  Cancers (Basel)       Date:  2021-06-12       Impact factor: 6.639

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