Comparing different NTCP models that predict the incidence of radiation pneumonitis. Normal tissue complication probability.

PURPOSE To compare different normal tissue complication probability (NTCP) models to predict the incidence of radiation pneumonitis on the basis of the dose distribution in the lung. METHODS AND MATERIALS The data from 382 breast cancer, malignant lymphoma, and inoperable non-small-cell lung cancer patients from two centers were studied. Radiation pneumonitis was scored using the Southwestern Oncology Group criteria. Dose-volume histograms of the lungs were calculated from the dose distributions that were corrected for dose per fraction effects. The dose-volume histogram of each patient was reduced to a single parameter using different local dose-effect relationships. Examples of single parameters were the mean lung dose (MLD) and the volume of lung receiving more than a threshold dose (V(Dth)). The parameters for the different NTCP models were fit to patient data using a maximum likelihood analysis. RESULTS The best fit resulted in a linear local dose-effect relationship, with the MLD as the resulting single parameter. The relationship between the MLD and NTCP could be described with a median toxic dose (TD(50)) of 30.8 Gy and a steepness parameter m of 0.37. The best fit for the relationship between the V(Dth) and the NTCP was obtained with a D(th) of 13 Gy. The MLD model was found to be significantly better than the V(Dth) model (p <0.03). However, for 85% of the studied patients, the difference in NTCP calculated with both models was <10%, because of the high correlation between the two parameters. For dose distributions outside the range of the studied dose-volume histograms, the difference in NTCP, using the two models could be >35%. For arbitrary dose distributions, an estimate of the uncertainty in the NTCP could be determined using the probability distribution of the parameter values of the Lyman-Kutcher-Burman model. CONCLUSION The maximum likelihood method revealed that the underlying local dose-effect relation for radiation pneumonitis was linear (the MLD model), rather than a step function (the V(Dth) model). Thus, for the studied patient population, the MLD was the most accurate predictor for the incidence of radiation pneumonitis.

[1]  J. Lyman Complication Probability as Assessed from Dose-Volume Histograms , 1985 .

[2]  R K Ten Haken,et al.  Dose-volume histogram and 3-D treatment planning evaluation of patients with pneumonitis. , 1994, International journal of radiation oncology, biology, physics.

[3]  L. Boersma,et al.  Dose-effect relations for local functional and structural changes of the lung after irradiation for malignant lymphoma. , 1994, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[4]  Y Seppenwoolde,et al.  Partial irradiation of the lung. , 2001, Seminars in radiation oncology.

[5]  S. T. Buckland,et al.  Computer-Intensive Methods for Testing Hypotheses. , 1990 .

[6]  J. Lebesque,et al.  The simultaneous boost technique: the concept of relative normalized total dose. , 1991, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[7]  G J Kutcher,et al.  Probability of radiation-induced complications for normal tissues with parallel architecture subject to non-uniform irradiation. , 1993, Medical physics.

[8]  J. Lebesque,et al.  Dose escalation in NSCLC using three dimensional conformal radiotherapy (3 DCRT) , 2000 .

[9]  Y. Seppenwoolde Radiation induced lung damage , 2002 .

[10]  A Ottolenghi,et al.  Radiation pneumonitis after breast cancer irradiation: analysis of the complication probability using the relative seriality model. , 2000, International journal of radiation oncology, biology, physics.

[11]  H. A. Kahn,et al.  Statistical Methods in Epidemiology , 1989 .

[12]  M. Goitein,et al.  Tolerance of normal tissue to therapeutic irradiation. , 1991, International journal of radiation oncology, biology, physics.

[13]  R K Ten Haken,et al.  Dose escalation in non-small-cell lung cancer using three-dimensional conformal radiation therapy: update of a phase I trial. , 2001, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[14]  M. Goitein,et al.  Fitting of normal tissue tolerance data to an analytic function. , 1991, International journal of radiation oncology, biology, physics.

[15]  J. Van Dyk,et al.  Evaluation of isoeffect formulae for predicting radiation-induced lung damage. , 1993, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[16]  Scott M. Smith,et al.  Computer Intensive Methods for Testing Hypotheses: An Introduction , 1989 .

[17]  J A Purdy,et al.  Clinical dose-volume histogram analysis for pneumonitis after 3D treatment for non-small cell lung cancer (NSCLC) , 1999, International journal of radiation oncology, biology, physics.

[18]  J. Fowler Radiation-induced lung damage: dose-time fractionation considerations. , 1990, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[19]  M Engelsman,et al.  Impact of simple tissue inhomogeneity correction algorithms on conformal radiotherapy of lung tumours. , 2001, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[20]  G J Kutcher,et al.  Probability of radiation-induced complications in normal tissues with parallel architecture under conditions of uniform whole or partial organ irradiation. , 1993, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[21]  H Meertens,et al.  Calculation of the uncertainty in complication probability for various dose-response models, applied to the parotid gland. , 2001, International journal of radiation oncology, biology, physics.

[22]  R Mohan,et al.  Clinically relevant optimization of 3-D conformal treatments. , 1992, Medical physics.

[23]  A. Niemierko,et al.  Modeling of normal tissue response to radiation: the critical volume model. , 1993, International journal of radiation oncology, biology, physics.

[24]  P Baas,et al.  Radiation dose-effect relations and local recovery in perfusion for patients with non-small-cell lung cancer. , 2000, International journal of radiation oncology, biology, physics.

[25]  P Baas,et al.  Evaluation of two dose-volume histogram reduction models for the prediction of radiation pneumonitis. , 1998, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[26]  R K Ten Haken,et al.  Radiation pneumonitis as a function of mean lung dose: an analysis of pooled data of 540 patients. , 1998, International journal of radiation oncology, biology, physics.

[27]  R W de Boer,et al.  Estimation of overall pulmonary function after irradiation using dose-effect relations for local functional injury. , 1995, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[28]  J. Gentle,et al.  Randomization and Monte Carlo Methods in Biology. , 1990 .

[29]  C. Burman,et al.  Calculation of complication probability factors for non-uniform normal tissue irradiation: the effective volume method. , 1989, International journal of radiation oncology, biology, physics.

[30]  D. Clayton,et al.  Statistical Models in Epidemiology , 1993 .

[31]  G J Kutcher,et al.  Analysis of clinical complication data for radiation hepatitis using a parallel architecture model. , 1995, International journal of radiation oncology, biology, physics.

[32]  M. Kris,et al.  Promising survival with three-dimensional conformal radiation therapy for non-small cell lung cancer. , 1997, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[33]  M T Munley,et al.  Physical and biological predictors of changes in whole-lung function following thoracic irradiation. , 1997, International journal of radiation oncology, biology, physics.