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Pre-radiotherapy FDG PET predicts radiation pneumonitis in lung cancer

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      Abstract

      Background

      A retrospective analysis is performed to determine if pre-treatment [ 18 F]-2-fluoro-2-deoxyglucose positron emission tomography/computed tomography (FDG PET/CT) image derived parameters can predict radiation pneumonitis (RP) clinical symptoms in lung cancer patients.

      Methods and Materials

      We retrospectively studied 100 non-small cell lung cancer (NSCLC) patients who underwent FDG PET/CT imaging before initiation of radiotherapy (RT). Pneumonitis symptoms were evaluated using the Common Terminology Criteria for Adverse Events version 4.0 (CTCAEv4) from the consensus of 5 clinicians. Using the cumulative distribution of pre-treatment standard uptake values (SUV) within the lungs, the 80th to 95th percentile SUV values (SUV 80 to SUV 95) were determined. The effect of pre-RT FDG uptake, dose, patient and treatment characteristics on pulmonary toxicity was studied using multiple logistic regression.

      Results

      The study subjects were treated with 3D conformal RT (n = 23), intensity modulated RT (n = 64), and proton therapy (n = 13). Multiple logistic regression analysis demonstrated that elevated pre-RT lung FDG uptake on staging FDG PET was related to development of RP symptoms after RT. A patient of average age and V 30 with SUV 95 = 1.5 was an estimated 6.9 times more likely to develop grade ≥ 2 radiation pneumonitis when compared to a patient with SUV 95 = 0.5 of the same age and identical V 30. Receiver operating characteristic curve analysis showed the area under the curve was 0.78 (95% CI = 0.69 – 0.87). The CT imaging and dosimetry parameters were found to be poor predictors of RP symptoms.

      Conclusions

      The pretreatment pulmonary FDG uptake, as quantified by the SUV 95, predicted symptoms of RP in this study. Elevation in this pre-treatment biomarker identifies a patient group at high risk for post-treatment symptomatic RP.

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      Most cited references 51

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      A new look at the statistical model identification

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        Statistical methods for assessing agreement between two methods of clinical measurement.

        In clinical measurement comparison of a new measurement technique with an established one is often needed to see whether they agree sufficiently for the new to replace the old. Such investigations are often analysed inappropriately, notably by using correlation coefficients. The use of correlation is misleading. An alternative approach, based on graphical techniques and simple calculations, is described, together with the relation between this analysis and the assessment of repeatability.
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          Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach.

          Methods of evaluating and comparing the performance of diagnostic tests are of increasing importance as new tests are developed and marketed. When a test is based on an observed variable that lies on a continuous or graded scale, an assessment of the overall value of the test can be made through the use of a receiver operating characteristic (ROC) curve. The curve is constructed by varying the cutpoint used to determine which values of the observed variable will be considered abnormal and then plotting the resulting sensitivities against the corresponding false positive rates. When two or more empirical curves are constructed based on tests performed on the same individuals, statistical analysis on differences between curves must take into account the correlated nature of the data. This paper presents a nonparametric approach to the analysis of areas under correlated ROC curves, by using the theory on generalized U-statistics to generate an estimated covariance matrix.
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            Author and article information

            Affiliations
            [1 ]The University of Texas Health Science Center, Houston, TX, USA
            [2 ]Divisions of Diagnostic Imaging, Houston, TX, USA
            [3 ]Quantitative Sciences, Houston, TX, USA
            [4 ]Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
            [5 ]Department of Computational and Applied Mathematics, Rice University, Houston, TX, USA
            [6 ]Baylor College of Medicine, Houston, TX, USA
            [7 ]Department of Radiation Oncology, Unit 97, The University of Texas M. D. Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA
            Contributors
            Journal
            Radiat Oncol
            Radiat Oncol
            Radiation Oncology (London, England)
            BioMed Central
            1748-717X
            2014
            13 March 2014
            : 9
            : 74
            24625207
            3995607
            1748-717X-9-74
            10.1186/1748-717X-9-74
            Copyright © 2014 Castillo et al.; licensee BioMed Central Ltd.

            This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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