@article{IBB_ID_10919,
author={Cella L, Palma G, Deasy JO, Oh JH, Liuzzi R, D'Avino V, Conson M, Pugliese N, Picardi M, Salvatore M, Pacelli R},
title={Complication probability models for radiation-induced heart valvular dysfunction: Do heart-lung interactions play a role?},
date={2014 Oct 31},
journal={Plosone (ISSN: 1932-6203, 1932-6203electronic, 1932-6203linking)},
year={2014},
fullvolume={207},
volume={207},
pages={11175301--11175311},
url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84910000530&partnerID=40&md5=ba6234b6b3a174070926e96f728bc35b},
abstract={Purpose: The purpose of this study is to compare different normal tissue complication probability (NTCP) models for predicting heart valve dysfunction (RVD) following thoracic irradiation. Methods: All patients from our institutional Hodgkin lymphoma survivors database with analyzable datasets were included (n = 90). All patients were treated with three-dimensional conformal radiotherapy with a median total dose of 32 Gy. The cardiac toxicity profile was available for each patient. Heart and lung dose-volume histograms (DVHs) were extracted and both organs were considered for Lyman-Kutcher-Burman (LKB) and Relative Seriality (RS) NTCP model fitting using maximum likelihood estimation. Bootstrap refitting was used to test the robustness of the model fit. Model performance was estimated using the area under the receiver operating characteristic curve (AUC). Results: Using only heart-DVHs, parameter estimates were, for the LKB model: D50 = 32.8 Gy, n = 0.16 and m = 0.67; and for the RS model: D50 = 32.4 Gy, s = 0.99 and γ = 0.42. AUC values were 0.67 for LKB and 0.66 for RS, respectively. Similar performance was obtained for models using only lung-DVHs (LKB: D50 = 33.2 Gy, n = 0.01, m = 0.19, AUC = 0.68; RS: D50 = 24.4 Gy, s = 0.99, γ = 2.12, AUC = 0.66). Bootstrap result showed that the parameter fits for lung-LKB were extremely robust. A combined heart-lung LKB model was also tested and showed a minor improvement (AUC = 0.70). However, the best performance was obtained using the previously determined multivariate regression model including maximum heart dose with increasing risk for larger heart and smaller lung volumes (AUC = 0.82). Conclusions: The risk of radiation induced valvular disease cannot be modeled using NTCP models only based on heart dose-volume distribution. A predictive model with an improved performance can be obtained but requires the inclusion of heart and lung volume terms, indicating that heart-lung interactions are apparently important for this endpoint. © 2014 Cella et al.},
keywords={Area Under The Curve, Article, Cardiotoxicity, Computer Assisted Radiotherapy, Controlled Study, Dose Volume Histogram, Fractionation, Heart Valve Dysfunction, Heart Volume, Hodgkin Disease, Human, Lung Volume, Lyman Kutcher Burman Model, Major Clinical Study, Mathematical Model, Normal Tissue Complication Probability Model, Predictive Value, Radiation Dose, Radiation Hazard, Radiation Injury, Radiological Parameters, Receiver Operating Characteristic, Relative Seriality Model, Sensitivity And Specificity, Valvular Heart Disease, Biological Model, Confidence Interval, Pathophysiology, Radiation Response, Statistical Model, Dose-Response Relationship, Likelihood Functions, Cardiovascular, Radiation Injuries, Roc Curve, },
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Kong, F. M., Pan, C., Eisbruch, A., Ten Haken, R. K., Physical models and simpler dosimetric descriptors of radiation late toxicity (2007) Semin Radiat Oncol, 17, pp. 108-120
Louwe, R. J., Wendling, M., Van Herk, M. B., Mijnheer, B. J., Three-dimensional heart dose reconstruction to estimate normal tissue complication probability after breast irradiation using portal dosimetry (2007) Med Phys, 34, pp. 1354-1363
Trott, K. R., Doerr, W., Facoetti, A., Hopewell, J., Langendijk, J., Biological mechanisms of normal tissue damage: Importance for the design of NTCP models (2012) Radiother Oncol, 105, pp. 79-85
Maraldo, M. V., Brodin, N. P., Aznar, M. C., Vogelius, I. R., Munck Af Rosenschold, P., Estimated risk of cardiovascular disease and secondary cancers with modern highly conformal radiotherapy for early-stage mediastinal Hodgkin lymphoma (2013) Ann Oncol, 24, pp. 2113-2118
Heidenreich, P. A., Hancock, S. L., Lee, B. K., Mariscal, C. S., Schnittger, I., Asymptomatic cardiac disease following mediastinal irradiation (2003) J Am Coll Cardiol, 42, pp. 743-749
Kutcher, G. J., Burman, C., Calculation of complication probability factors for non-uniform normal tissue irradiation: The effective volume method (1989) Int J Radiat Oncol Biol Phys, 16, pp. 1623-1630
Kong, F. M., Ritter, T., Quint, D. J., Senan, S., Gaspar, L. E., Consideration of dose limits for organs at risk of thoracic radiotherapy: Atlas for lung, proximal bronchial tree, esophagus, spinal cord, ribs, and brachial plexus (2011) Int J Radiat Oncol Biol Phys, 81, pp. 1442-1457
Deasy, J. O., Blanco, A. I., Clark, V. H., CERR: A computational environment for radiotherapy research (2003) Med Phys, 30, pp. 979-985
Marks, L. B., Yorke, E. D., Jackson, A., Ten Haken, R. K., Constine, L. S., Use of normal tissue complication probability models in the clinic (2010) Int J Radiat Oncol Biol Phys, 76, pp. S10-S19
Joiner, M. C., Bentzen, S. M., Bauman, D. E., Time-dose relationships: The linear-quadratic approach and the model in clinical practice (2002) Basic Clinical Radiobiology, pp. 120-146. , Steel GG (ed)
Bentzen, S. M., Skoczylas, J. Z., Bernier, J., Quantitative clinical radiobiology of early and late lung reactions (2000) Int J Radiat Biol, 76, pp. 453-462
Venzon, D. J., Moolgavkar, S. H., A Method for Computing Profile-Likelihood-Based Confidence Intervals (1988) Journal of the Royal Statistical Society Series C (Applied Statistics), 37, pp. 87-94
Semenenko, V. A., Li, X. A., Lyman-Kutcher-Burman NTCP model parameters for radiation pneumonitis and xerostomia based on combined analysis of published clinical data (2008) Phys Med Biol, 53, pp. 737-755
Deasy, J. O., Chao, K. S., Markman, J., Uncertainties in model-based outcome predictions for treatment planning (2001) Int J Radiat Oncol Biol Phys, 51, pp. 1389-1399
Steyerberg, E. W., Vickers, A. J., Cook, N. R., Gerds, T., Gonen, M., Assessing the performance of prediction models: A framework for traditional and novel measures (2010) Epidemiology, 21, pp. 128-138
Adams, M. J., Hardenbergh, P. H., Constine, L. S., Lipshultz, S. E., Radiation-associated cardiovascular disease (2003) Crit Rev Oncol Hematol, 45, pp. 55-75
Bentzen, S. M., Prediction of radiotherapy response using SF2: Why it may work after all (1994) Radiother Oncol, 31, pp. 85-86
Lee, T. F., Chao, P. J., Ting, H. M., Chang, L., Huang, Y. J., Using Multivariate Regression Model with Least Absolute Shrinkage and Selection Operator (LASSO) to Predict the Incidence of Xerostomia after Intensity-Modulated Radiotherapy for Head and Neck Cancer (2014) PLoS One, 9, p. e89700
Huang, E. X., Hope, A. J., Lindsay, P. E., Trovo, M., El Naqa, I., Heart irradiation as a risk factor for radiation pneumonitis (2011) Acta Oncol, 50, pp. 51-60},
document_type={Journal Article, Research Support, Non-U. S. Gov'T, },
affiliation={Institute of Biostructure and Bioimaging, National Council of Research (CNR)Naples, Italy
Department of Advanced Biomedical Sciences, Federico II University School of MedicineNaples, Italy
Department of Medical Physics, Memorial Sloan Kettering Cancer CenterNew York, NY, United States
Department of Clinical Medicine and Surgery, Federico II University School of MedicineNaples, Italy},
ibbaffiliation={1},
}