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Michael V Lawrence,
Mert Saynak,
David V Fried,
Ted A Bateman,
Rebecca L Green,
Jessica L Hubbs,
Ronald J Jaszczak,
Terence Z Wong, Sumin Zhou,
Shiva K Das,
Lawrence B Marks
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ABSTRACT: Purpose: Abnormalities in single photon emission computed tomography (SPECT) perfusion within the lung and heart are often detected following radiation for tumors in∕around the thorax (e.g., lung cancer or left-sided breast cancer). The presence of SPECT perfusion defects is determined by comparing pre- and post-RT SPECT images. However, RT may increase the density of the soft tissue surrounding the lung∕heart (e.g., chest wall∕breast) that could possibly lead to an "apparent" SPECT perfusion defect due to increased attenuation of emitted photons. Further, increases in tissue effective depth will also increase SPECT photon attenuation and may lead to "apparent" SPECT perfusion defects. The authors herein quantitatively assess the degree of density changes and effective depth in soft tissues following radiation in a series of patients on a prospective clinical study.Methods: Patients receiving thoracic RT were enrolled on a prospective clinical study including pre- and post-RT thoracic computed tomography (CT) scans. Using image registration, changes in tissue density and effective depth within the soft tissues were quantified (as absolute change in average CT Hounsfield units, HU, or tissue thickness, cm). Changes in HU and tissue effective depth were considered as a continuous variable. The potential impact of these tissue changes on SPECT images was estimated using simulation data from a female SPECT thorax phantom with varying tissue densities.Results: Pre- and serial post-RT CT images were quantitatively studied in 23 patients (4 breast cancer, 19 lung cancer). Data were generated from soft tissue regions receiving doses of 20-50 Gy. The average increase in density of the chest was 5 HU (range 46 to -69). The average change in breast density was a decrease of -1 HU (range 13 to -13). There was no apparent dose response in neither the dichotomous nor the continuous analysis. Seventy seven soft tissue contours were created for 19 lung cancer patients. The average change in tissue effective depth was +0.2 cm (range -1.9 to 2.2 cm). The changes in HU represent a <2% average change in tissue density. Based on simulation, the small degree of density and tissue effective depth change is unlikely to yield meaningful changes in either SPECT lung or heart perfusion.Conclusions: RT doses of 20-50 Gy can cause up to a 46 HU increase in soft tissue density 6 months post-RT. Post-RT soft tissue effective depth may increase by 2.0 cm. These modest increases in soft tissue density and effective depth are unlikely to be responsible for the perfusion changes seen on post-RT SPECT lung or heart scans. Further, there was no clear dose response of thesoft tissue density changes. Ultimately, the authors findings suggest that prior perfusion reports do reflect changes in the physiology of the lungs and heart.
Medical Physics 12/2012; 39(12):7644-9. · 2.83 Impact Factor
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ABSTRACT: To assess the time and regional dependence of radiation therapy (RT)-induced reductions in regional lung perfusion 0.1-12 years post-RT, as measured by single photon emission computed tomography (SPECT) lung perfusion.
Between 1991 and 2005, 123 evaluable patients receiving RT for tumors in/around the thorax underwent SPECT lung perfusion scans before and serially post-RT (0.1-12 years). Registration of pre- and post-RT SPECT images with the treatment planning computed tomography, and hence the three-dimensional RT dose distribution, allowed changes in regional SPECT-defined perfusion to be related to regional RT dose. Post-RT follow-up scans were evaluated at multiple time points to determine the time course of RT-induced regional perfusion changes. Population dose response curves (DRC) for all patients at different time points, different regions, and subvolumes (e.g., whole lungs, cranial/caudal, ipsilateral/contralateral) were generated by combining data from multiple patients at similar follow-up times. Each DRC was fit to a linear model, and differences statistically analyzed.
In the overall groups, dose-dependent reductions in perfusion were seen at each time post-RT. The slope of the DRC increased over time up to 18 months post-RT, and plateaued thereafter. Regional differences in DRCs were only observed between the ipsilateral and contralateral lungs, and appeared due to tumor-associated changes in regional perfusion.
Thoracic RT causes dose-dependent reductions in regional lung perfusion that progress up to approximately 18 months post-RT and persists thereafter. Tumor shrinkage appears to confound the observed dose-response relations. There appears to be similar dose response for healthy parts of the lungs at different locations.
International journal of radiation oncology, biology, physics 08/2009; 76(2):425-32. · 4.59 Impact Factor
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ABSTRACT: To study the temporal nature of regional lung density changes and to assess whether the dose-dependent nature of these changes is associated with patient- and treatment-associated factors.
Between 1991 and 2004, 118 patients with interpretable pre- and post-radiation therapy (RT) chest computed tomography (CT) scans were evaluated. Changes in regional lung density were related to regional dose to define a dose-response curve (DRC) for RT-induced lung injury using three-dimensional planning tools and image fusion. Multiple post-RT follow-up CT scans were evaluated by fitting linear-quadratic models of density changes on dose with time as the covariate. Various patient- and treatment-related factors were examined as well.
There was a dose-dependent increase in regional lung density at nearly all post-RT follow-up intervals. The population volume-weighted changes evolved over the initial 6-month period after RT and reached a plateau thereafter (p < 0.001). On univariate analysis, patient age greater than 65 years (p = 0.003) and/or the use of pre-RT surgery (p < 0.001) were associated with significantly greater changes in CT density at both 6 and 12 months after RT, but the magnitude of this effect was modest.
There appears to be a temporal nature for the dose-dependent increases in lung density. Nondosimetric clinical factors tend to have no, or a modest, impact on these changes.
International journal of radiation oncology, biology, physics 04/2009; 76(1):116-22. · 4.59 Impact Factor
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ABSTRACT: To assess the association between radiotherapy (RT)-induced changes in computed tomography (CT)-defined lung tissue density and pulmonary function tests (PFTs).
Patients undergoing incidental partial lung RT were prospectively assessed for global (PFTs) and regional (CT and single photon emission CT [SPECT]) lung function before and, serially, after RT. The percent reductions in the PFT and the average changes in lung density were compared (Pearson correlations) in the overall group and subgroups stratified according to various clinical factors. Comparisons were also made between the CT- and SPECT-based computations using the Mann-Whitney U test.
Between 1991 and 2004, 343 patients were enrolled in this study. Of these, 111 patients had a total of 203 concurrent post-RT evaluations of changes in lung density and PFTs available for the analyses, and 81 patients had a total of 141 concurrent post-RT SPECT images. The average increases in lung density were related to the percent reductions in the PFTs, albeit with modest correlation coefficients (range, 0.20-0.43). The analyses also indicated that the association between lung density and PFT changes is essentially equivalent to the corresponding association with SPECT-defined lung perfusion.
We found a weak quantitative association between the degree of increase in lung density as defined by CT and the percent reduction in the PFTs.
International journal of radiation oncology, biology, physics 01/2009; 74(3):781-9. · 4.59 Impact Factor
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ABSTRACT: The fusion of predictions from disparate models has been used in several fields to obtain a more realistic and robust estimate of the "ground truth" by allowing the models to reinforce each other when consensus exists, or, conversely, negate each other when there is no consensus. Fusion has been shown to be most effective when the models have some complementary strengths arising from different approaches. In this work, we fuse the results from four common but methodologically different nonlinear multivariate models (Decision Trees, Neural Networks, Support Vector Machines, Self-Organizing Maps) that were trained to predict radiation-induced pneumonitis risk on a database of 219 lung cancer patients treated with radiotherapy (34 with Grade 2+ postradiotherapy pneumonitis). Each model independently incorporated a small number of features from the available set of dose and nondose patient variables to predict pneumonitis; no two models had all features in common. Fusion was achieved by simple averaging of the predictions for each patient from all four models. Since a model's prediction for a patient can be dependent on the patient training set used to build the model, the average of several different predictions from each model was used in the fusion (predictions were made by repeatedly testing each patient with a model built from different cross-validation training sets that excluded the patient being tested). The area under the receiver operating characteristics curve for the fused cross-validated results was 0.79, with lower variance than the individual component models. From the fusion, five features were extracted as the consensus among all four models in predicting radiation pneumonitis. Arranged in order of importance, the features are (1) chemotherapy; (2) equivalent uniform dose (EUD) for exponent a=1.2 to 3; (3) EUD for a=0.5 to 1.2, lung volume receiving >20-30 Gy; (4) female sex; and (5) squamous cell histology. To facilitate ease of interpretation and prospective use, the fused outcome results for the patients were fitted to a logistic probability function.
Medical Physics 11/2008; 35(11):5098-109. · 2.83 Impact Factor
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ABSTRACT: The electron Monte Carlo (eMC) dose calculation algorithm of the Eclipse treatment planning system is based heavily upon Monte Carlo simulation of the linac head and modeling of the linac beam characteristics with minimal measurement of beam data. Commissioning of the eMC algorithm on multiple identical linacs provided a unique opportunity to systematically evaluate the algorithm with actual measurements of clinically relevant beam and dose parameters. In this study, measured and eMC calculated dose distributions were compared both along and perpendicular to electron beam direction for electron energy/applicator/depth combination using measurement data from four Varian 21EX CLINAC linear accelerator (Varian Medical System, Palo Alto, CA). Cutout factors for sizes down to 3 x 3 cm were also compared. Comparisons between the measurement and the eMC calculated values show that the R90, R80, R50, and R10 values mostly agree within 3 mm. Measure and Calculated bremsstrahlung dose Dx correlates well statistically although eMC calculated Dx values are consistently smaller than the measured, with maximum discrepancy of 1% for the 20 MeV electron beams. Surface dose agrees mostly within 2%. Field width and penumbra agree mostly within 3mm. Calculation grid size is found to have a significant effect on the dose calculation. A grid size of 5 mm can produce erroneous dose distributions. Using a grid size of 2.5 mm and a 3% accuracy specified for the eMC to stop calculation iteration, the absolute output agrees with measurements within 3% for field sizes of 5 x 5 cm or larger. For cutout of 3 x 3 cm, however, the output disagreement can reach 8%. Our result indicate that eMC algorithm in Eclipse provides acceptable agreement with measurement data for most clinical situations. Calculation grid size of 2.5 mm or smaller is recommended.
Journal of Applied Clinical Medical Physics 02/2008; 9(3):2720. · 1.29 Impact Factor
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ABSTRACT: This work investigates the use of the self-organizing map (SOM) technique for predicting lung radiation pneumonitis (RP) risk. SOM is an effective method for projecting and visualizing high-dimensional data in a low-dimensional space (map). By projecting patients with similar data (dose and non-dose factors) onto the same region of the map, commonalities in their outcomes can be visualized and categorized. Once built, the SOM may be used to predict pneumonitis risk by identifying the region of the map that is most similar to a patient's characteristics. Two SOM models were developed from a database of 219 lung cancer patients treated with radiation therapy (34 clinically diagnosed with Grade 2+ pneumonitis). The models were: SOM(all) built from all dose and non-dose factors and, for comparison, SOM(dose) built from dose factors alone. Both models were tested using ten-fold cross validation and Receiver Operating Characteristics (ROC) analysis. Models SOM(all) and SOM(dose) yielded ten-fold cross-validated ROC areas of 0.73 (sensitivity/specificity = 71%/68%) and 0.67 (sensitivity/specificity = 63%/66%), respectively. The significant difference between the cross-validated ROC areas of these two models (p < 0.05) implies that non-dose features add important information toward predicting RP risk. Among the input features selected by model SOM(all), the two with highest impact for increasing RP risk were: (a) higher mean lung dose and (b) chemotherapy prior to radiation therapy. The SOM model developed here may not be extrapolated to treatment techniques outside that used in our database, such as several-field lung intensity modulated radiation therapy or gated radiation therapy.
Physics in Medicine and Biology 01/2008; 53(1):203-16. · 2.83 Impact Factor
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Seventh International Conference on Machine Learning and Applications, ICMLA 2008, San Diego, California, USA, 11-13 December 2008; 01/2008
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ABSTRACT: The purpose of this study is to build and test a support vector machine (SVM) model to predict for the occurrence of lung radiation-induced Grade 2+ pneumonitis. SVM is a sophisticated statistical technique capable of separating the two categories of patients (with/without pneumonitis) using a boundary defined by a complex hypersurface. Despite the complexity, the SVM boundary is only minimally influenced by outliers that are difficult to separate. By contrast, the simple hyperplane boundary computed by the more commonly used and related linear discriminant analysis method is heavily influenced by outliers. Two SVM models were built using data from 219 patients with lung cancer treated using radiotherapy (34 diagnosed with pneumonitis). One model (SVM(all)) selected input features from all dose and non-dose factors. For comparison, the other model (SVM(dose)) selected input features only from lung dose-volume factors. Model predictive ability was evaluated using ten-fold cross-validation and receiver operating characteristics (ROC) analysis. For the model SVM(all), the area under the cross-validated ROC curve was 0.76 (sensitivity/specificity = 74%/75%). Compared to the corresponding SVM(dose) area of 0.71 (sensitivity/specificity = 68%/68%), the predictive ability of SVM(all) was improved, indicating that non-dose features are important contributors to separating patients with and without pneumonitis. Among the input features selected by model SVM(all), the two with highest importance for predicting lung pneumonitis were: (a) generalized equivalent uniform doses close to the mean lung dose, and (b) chemotherapy prior to radiotherapy. The model SVM(all) is publicly available via internet access.
Medical Physics 11/2007; 34(10):3808-14. · 2.83 Impact Factor
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ABSTRACT: High-precision intensity-modulated radiotherapy demands high patient positioning accuracy. On-board digital tomosynthesis (DTS) provides three-dimensional (3D) image guidance for daily positioning with a lower imaging dose, faster acquisition, and more geometric flexibility than 3D cone-beam computed tomography (CBCT). This clinical study evaluated DTS as a daily imaging technique for patient positioning and compared the results with 3D CBCT and two-dimensional (2D) radiography.
Head and neck cancer patients undergoing intensity-modulated radiotherapy were studied. For each session, the patient was positioned using laser marks. On-board imaging data sets, including 2D kilovoltage radiographs, DTS, and CBCT, were obtained to measure the daily patient positioning variations. The mean and standard deviations of the positioning variations in the translational and rotational directions were calculated. The positioning differences among 2D radiography, DTS, and CBCT were analyzed.
Image data sets were collected from 65 treatment fractions for 10 patients. The systematic patient positioning variation was <0.10 cm and 1.0 degrees one dimensionally. The random variations were 0.27-0.34 cm in the translational and 0.93 degrees -1.99 degrees in the rotational direction. The mean vector isocenter variation was 0.48 cm. DTS with 40 degrees and 20 degrees scan angles in the coronal or sagittal directions yielded the same results for patient positioning. DTS performance was comparable to that of CBCT, with positioning differences of <0.1 cm and 0.5 degrees . The positioning difference between 2D radiography and DTS was approximately 0.1 cm and 0.2 cm in the vertical/longitudinal and lateral directions.
Our results have demonstrated that DTS is a comparable 3D imaging technique to CBCT for daily patient positioning of head-and-neck patients as determined by manual registration of bony anatomy.
International Journal of Radiation OncologyBiologyPhysics 10/2007; 69(2):598-606. · 4.11 Impact Factor
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ABSTRACT: A feed-forward neural network was investigated to predict the occurrence of lung radiation-induced Grade 2+ pneumonitis. The database consisted of 235 patients with lung cancer treated using radiotherapy, of whom 34 were diagnosed with Grade 2+ pneumonitis at follow-up. The network was constructed using an algorithm that alternately grew and pruned it, starting from the smallest possible network, until a satisfactory solution was found. The weights and biases of the network were computed using the error back-propagation approach. Momentum and variable leaning techniques were used to speed convergence. Using the growing/pruning approach, the network selected features from 66 dose and 27 non-dose variables. During network training, the 235 patients were randomly split into ten groups of approximately equal size. Eight groups were used to train the network, one group was used for early stopping training to prevent overfitting, and the remaining group was used as a test to measure the generalization capability of the network (cross-validation). Using this methodology, each of the ten groups was considered, in turn, as the test group (ten-fold cross-validation). For the optimized network constructed with input features selected from dose and non-dose variables, the area under the receiver operating characteristics (ROC) curve for cross-validated testing was 0.76 (sensitivity: 0.68, specificity: 0.69). For the optimized network constructed with input features selected only from dose variables, the area under the ROC curve for cross-validation was 0.67 (sensitivity: 0.53, specificity: 0.69). The difference between these two areas was statistically significant (p = 0.020), indicating that the addition of non-dose features can significantly improve the generalization capability of the network. A network for prospective testing was constructed with input features selected from dose and non-dose variables (all data were used for training). The optimized network architecture consisted of six input nodes (features), four hidden nodes, and one output node. The six input features were: lung volume receiving > 16 Gy (V16), generalized equivalent uniform dose (gEUD) for the exponent a = 1 (mean lung dose), gEUD for the exponent a = 3.5, free expiratory volume in 1 s (FEV1), diffusion capacity of carbon monoxide (DLCO%), and whether or not the patient underwent chemotherapy prior to radiotherapy. The significance of each input feature was individually evaluated by omitting it during network training and gauging its impact by the consequent deterioration in cross-validated ROC area. With the exception of FEV1 and whether or not the patient underwent chemotherapy prior to radiotherapy, all input features were found to be individually significant (p < 0.05). The network for prospective testing is publicly available via internet access.
Medical Physics 09/2007; 34(9):3420-7. · 2.83 Impact Factor
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ABSTRACT: To develop and test a model to predict for lung radiation-induced Grade 2+ pneumonitis.
The model was built from a database of 234 lung cancer patients treated with radiotherapy (RT), of whom 43 were diagnosed with pneumonitis. The model augmented the predictive capability of the parametric dose-based Lyman normal tissue complication probability (LNTCP) metric by combining it with weighted nonparametric decision trees that use dose and nondose inputs. The decision trees were sequentially added to the model using a "boosting" process that enhances the accuracy of prediction. The model's predictive capability was estimated by 10-fold cross-validation. To facilitate dissemination, the cross-validation result was used to extract a simplified approximation to the complicated model architecture created by boosting. Application of the simplified model is demonstrated in two example cases.
The area under the model receiver operating characteristics curve for cross-validation was 0.72, a significant improvement over the LNTCP area of 0.63 (p = 0.005). The simplified model used the following variables to output a measure of injury: LNTCP, gender, histologic type, chemotherapy schedule, and treatment schedule. For a given patient RT plan, injury prediction was highest for the combination of pre-RT chemotherapy, once-daily treatment, female gender and lowest for the combination of no pre-RT chemotherapy and nonsquamous cell histologic type. Application of the simplified model to the example cases revealed that injury prediction for a given treatment plan can range from very low to very high, depending on the settings of the nondose variables.
Radiation pneumonitis prediction was significantly enhanced by decision trees that added the influence of nondose factors to the LNTCP formulation.
International Journal of Radiation OncologyBiologyPhysics 08/2007; 68(4):1212-21. · 4.11 Impact Factor
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ABSTRACT: To assess the impact of induction chemotherapy, and associated tumor shrinkage, on the subsequent radiation-related changes in pulmonary function and tumor response.
As part of a prospective institutional review board-approved study, 91 evaluable patients treated definitively with thoracic radiation therapy (RT) for unresectable lung cancer were analyzed. The rates of RT-associated pulmonary toxicity and tumor response were compared in the patients with and without pre-RT chemotherapy. In the patients receiving induction chemotherapy, the rates of RT-associated pulmonary toxicity and tumor response were compared in the patients with and without a response (modified Response Evaluation Criteria in Solid Tumor criteria) to the pre-RT chemotherapy. Comparisons of the rates of improvements in pulmonary function tests (PFTs) post-RT, dyspnea requiring steroids, and percent declines in PFTs post-RT were compared in patient subgroups using Fisher's exact test, analysis of variance, and linear or logistic regression.
The use of pre-RT chemotherapy appears to increase the rate of radiation-induced pneumonitis (p = 0.009-0.07), but has no consistent impact on changes in PFTs. The degree of induction chemotherapy-associated tumor shrinkage is not associated with the rate of subsequent RT-associated pulmonary toxicity. The degree of tumor response to chemotherapy is not related to the degree of tumor response to RT.
Additional study is needed to better clarify the impact of chemotherapy on radiation-associated disfunction.
International Journal of Radiation OncologyBiologyPhysics 05/2007; 67(5):1360-9. · 4.11 Impact Factor
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ABSTRACT: To assess the utility of the 6-minute walk test (6MWT) as a predictor of symptomatic radiation-induced pneumonitis (RP).
As part of a prospective trial to study radiation-induced lung injury, 53 patients receiving thoracic radiotherapy (RT) underwent a pre-RT 6MWT, pulmonary function tests (PFTs), and had >or=3-month follow-up for prospective assessment of Grade 2 or worse RP (requiring medications or worse). Dosimetric parameters (e.g., the percentage of lung receiving >or=30 Gy) were extracted from the lung dose-volume histogram. The correlations between the 6MWT and PFT results were assessed using Pearson's correlation. The receiver operating characteristic technique was used in patient subgroups to evaluate the predictive capacities for RP of the dosimetric parameters, 6MWT results, and PFT results, or the combination (using discriminant analysis) of all three metrics. ROCKIT software was used to compare the receiver operating characteristic areas between each predictive model. The association of the decline in 6MWT with the development of RP was evaluated using Fisher's exact test.
The pre-RT PFT and 6MWT results correlated weakly (r = 0.44-0.57, p <or= 0.001), suggesting that they measure somewhat different physiologic functions. Of the 53 patients, 9 (17%) developed RP. The dose-volume histogram-based dosimetric parameters were the best single-metric model for predicting RP (e.g., percentage of lung receiving >or=30 Gy, receiver operating characteristic area 0.73, p = 0.03). Including the PFT or 6MWT results with the percentage of lung receiving >or=30 Gy did not improve the predictions. The predictive abilities of dosimetric-based models improved when the analysis was restricted to those patients whose tumors were not causing regional lung dysfunction. No correlation was found between the decline in the 6MWT result and the RP rate (p = 0.6).
Although the PFTs and 6MWT are related to each other, the correlation coefficients were weak, suggesting that they could be measuring different physiologic functions. In the present data set, the addition of the PFTs or 6MWT did not increase the ability of the dosimetric parameters to predict for acute symptomatic RP. Additional work is needed to better understand the interaction among the PFT results, exercise tolerance (6MWT), and the risk of RT-induced lung dysfunction.
International Journal of Radiation OncologyBiologyPhysics 04/2007; 67(3):759-67. · 4.11 Impact Factor
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Zafer Kocak,
Gerben R Borst,
Jing Zeng, Sumin Zhou,
Donna R Hollis,
Junan Zhang,
Elizabeth S Evans,
Rodney J Folz,
Terrence Wong,
Daniel Kahn,
Jose S A Belderbos,
Joos V Lebesque,
Lawrence B Marks
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ABSTRACT: Clinical and 3D dosimetric parameters are associated with symptomatic radiation pneumonitis rates in retrospective studies. Such parameters include: mean lung dose (MLD), radiation (RT) dose to perfused lung (via SPECT), and pre-RT lung function. Based on prior publications, we defined pre-RT criteria hypothesized to be predictive for later development of pneumonitis. We herein prospectively test the predictive abilities of these dosimetric/functional parameters on 2 cohorts of patients from Duke and The Netherlands Cancer Institute (NKI).
For the Duke cohort, 55 eligible patients treated between 1999 and 2005 on a prospective IRB-approved study to monitor RT-induced lung injury were analyzed. A similar group of patients treated at the NKI between 1996 and 2002 were identified. Patients believed to be at high and low risk for pneumonitis were defined based on: (1) MLD; (2) OpRP (sum of predicted perfusion reduction based on regional dose-response curve); and (3) pre-RT DLCO. All doses reflected tissue density heterogeneity. The rates of grade > or =2 pneumonitis in the "presumed" high and low risk groups were compared using Fisher's exact test.
In the Duke group, pneumonitis rates in patients prospectively deemed to be at "high" vs. "low" risk are 7 of 20 and 9 of 35, respectively; p = 0.33 one-tailed Fisher's. Similarly, comparable rates for the NKI group are 4 of 21 and 6 of 44, respectively, p = 0.41 one-tailed Fisher's.
The prospective model appears unable to accurately segregate patients into high vs. low risk groups. However, considered retrospectively, these data are consistent with prior studies suggesting that dosimetric (e.g., MLD) and functional (e.g., PFTs or SPECT) parameters are predictive for RT-induced pneumonitis. Additional work is needed to better identify, and prospectively assess, predictors of RT-induced lung injury.
International Journal of Radiation OncologyBiologyPhysics 01/2007; 67(1):178-86. · 4.11 Impact Factor
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ABSTRACT: Single photon emission computed tomography (SPECT) provides a map of the spatial distribution of lung perfusion. Thus, SPECT guidance can be used to divert dose away from higher-functioning lung, potentially reducing lung toxicity. We present a methodology for achieving this aim and test it in intensity-modulated radiotherapy (IMRT) treatment-planning.
IMRT treatment plans were generated with and without SPECT guidance and compared for 5 patients. Healthy lung was segmented into four regions on the basis of SPECT intensity in the SPECT plan. Dose was sequentially allowed to the target via regions of increasing SPECT intensity. This process results in reduction of dose to functional lung, reflected in the dose-function histogram (DFH). The plans were compared using DFHs and F(20)/F(30) values (F(x) is the functional lung receiving dose above x Gy).
In all cases, the SPECT-guided plan produced a more favorable DFH compared with the non-SPECT-guided plan. Additionally, the F(20) and F(30) values were reduced for all patients by an average of 13.6% +/- 5.2% and 10.5% +/- 5.8%, respectively. In all patients, DFHs of the two highest-functioning SPECT regions were reduced, whereas DFHs of the two lower-functioning regions were increased, illustrating the dose "give-take" between SPECT regions during redistribution.
SPECT-guided IMRT shows potential for reducing the dose delivered to highly functional lung regions. This dose reduction could reduce the number of high-grade pneumonitis cases that develop after radiation treatment and improve patient quality of life.
International Journal of Radiation OncologyBiologyPhysics 01/2007; 66(5):1543-52. · 4.11 Impact Factor
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Mitchell S Anscher,
Jennifer Garst,
Lawrence B Marks,
Nicole Larrier,
Frank Dunphy,
James E Herndon,
Robert Clough,
Christine Marino,
Zeljko Vujaskovic, Sumin Zhou,
Mark W Dewhirst,
Timothy D Shafman,
Jeffrey Crawford
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ABSTRACT: Thalidomide has broad anticytokine properties, which might protect normal tissues in patients undergoing chemoradiotherapy. The purpose of this study was to determine the maximal tolerated dose of thalidomide when used in combination with vinorelbine plus thoracic radiotherapy.
Eligible patients had inoperable Stage III non-small-cell lung cancer, a Karnofsky Performance Status>or=70, and life expectancy>or=6 months. Patients underwent pretreatment evaluation of lung function. Radiotherapy consisted of 66 Gy in 6.5 weeks. Vinorelbine was administered i.v. (5 mg/m2) 3 times per week just before radiotherapy. Thalidomide was begun at 50 mg, p.o., on day 1 of chemoradiotherapy and continued once daily for 6 months. Side effects were scored using National Cancer Institute Common Toxicity Criteria.
Ten patients were enrolled. Of the first 6 patients, 2 developed major thrombotic events that were believed to be possibly related to thalidomide. The study was suspended and modified to require prophylactic anticoagulation. Of the last 4 patients, 2 developed dose-limiting toxicity attributable to thalidomide; both patients required a dose reduction of thalidomide to <50 mg/day. Because the drug is not available in an oral product providing <50 mg/day, the study was closed.
The combination of thalidomide concurrently with thoracic radiotherapy and vinorelbine resulted in excessive toxicity.
International Journal of Radiation OncologyBiologyPhysics 10/2006; 66(2):477-82. · 4.11 Impact Factor
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ABSTRACT: Incidental esophageal irradiation during lung cancer therapy often causes morbidity. There is interest in trying to relate esophageal dosimetric parameters to the risk of injury. These parameters typically rely on CT-defined esophageal contours, and thus systematic limitations in esophageal contouring will influence these parameters. We herein assess the ability of a correction method, based on physiologic principles, to improve the predictive power of dosimetric parameters for radiation-induced esophageal injury.
Esophageal contours for 236 patients treated for lung cancer were quantitatively analyzed. All patients received three-dimensional planning, and all contours were generated by the same physician on axial CT images. Traditional dose-volume histogram (DVH)-based dosimetric parameters were extracted from the three-dimensional data set. A second set of "anatomically correct" dosimetric parameters was derived by adjusting the contours to reflect the known shape of the esophagus. Each patient was scored for acute and late toxicity using ROTG criteria. Univariate analysis was used to assess the predictive power of corrected and uncorrected dosimetric parameters (e.g., mean dose, V(50), and V(60)) for toxicity. The p values were taken as a measure of their significance.
The univariate results indicate that both corrected and uncorrected dosimetric parameters are generally predictors for toxicity. The corrected parameters are more highly correlated (lower p value) with outcomes than the uncorrected metrics.
The inclusion of corrections, based on anatomic realities, to DVH-based dosimetric parameters may provide dosimetric parameters that are better correlated with clinical outcomes than are traditional DVH-based metrics.
International Journal of Radiation OncologyBiologyPhysics 08/2005; 62(3):645-51. · 4.11 Impact Factor
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ABSTRACT: To evaluate the clinical and three-dimensional dosimetric parameters associated with esophageal injury after radiotherapy (RT) for non-small-cell lung cancer.
The records of 254 patients treated for non-small-cell lung cancer between 1992 and 2001 were reviewed. A variety of metrics describing the esophageal dose were extracted. The Radiation Therapy Oncology Group toxicity criteria for grading of esophageal injury were used. The median follow-up time for all patients was 43 months (range, 0.5-120 months). Logistic regression analysis, contingency table analyses, and Fisher's exact tests were used for statistical analysis.
Acute toxicity occurred in 199 (78%) of 254 patients. For acute toxicity of Grade 2 or worse, twice-daily RT, age, nodal stage of N2 or worse, and most dosimetric parameters were predictive. Late toxicity occurred in 17 (7%) of 238 patients. The median and maximal time to the onset of late toxicity was 5 and 40 months after RT, respectively. Late toxicity occurred in 2%, 3%, 17%, 26%, and 100% of patients with acute Grade 0, 1, 2, 3, and 4 toxicity, respectively. For late toxicity, the severity of acute toxicity was most predictive.
A variety of dosimetric parameters are predictive of acute and late esophageal injury. A strong correlation between the dosimetric parameters prevented a comparison between the predictive abilities of these metrics. The presence of acute injury was the most predictive factor for the development of late injury. Additional studies to define better the predictors of RT-induced esophageal injury are needed.
International Journal of Radiation OncologyBiologyPhysics 03/2005; 61(2):335-47. · 4.11 Impact Factor
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Shiva K Das,
Alan H Baydush, Sumin Zhou,
Moyed Miften,
Xiaoli Yu,
Oana Craciunescu,
Mark Oldham,
Kim Light,
Terence Wong,
Michael Blazing,
Salvador Borges-Neto,
Mark W Dewhirst,
Lawrence B Marks
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ABSTRACT: The purpose of this work is to compare the efficacy of mathematical models in predicting the occurrence of radiotherapy-induced left ventricular perfusion defects assessed using single-photon emission computed tomography (SPECT). The basis of this study is data from 73 left-sided breast/ chestwall patients treated with tangential photon fields. The mathematical models compared were three commonly used parametric models [Lyman normal tissue complication probability (LNTCP), relative serialty (RS), generalized equivalent uniform dose (gEUD)] and a nonparametric model (Linear discriminant analysis--LDA). Data used by the models were the left ventricular dose--volume histograms, or SPECT-based dose-function histograms, and the presence/absence of SPECT perfusion defects 6 months postradiation therapy (21 patients developed defects). For the parametric models, maximum likelihood estimation and F-tests were used to fit the model parameters. The nonparametric LDA model step-wise selected features (volumes/function above dose levels) using a method based on receiver operating characteristics (ROC) analysis to best separate the groups with and without defects. Optimistic (upper bound) and pessimistic (lower bound) estimates of each model's predictive capability were generated using ROC curves. A higher area under the ROC curve indicates a more accurate model (a model that is always accurate has area = 1). The areas under these curves for different models were used to statistically test for differences between them. Pessimistic estimates of areas under the ROC curve using dose-volume histogram/ dose-function histogram inputs, in order of increasing prediction accuracy, were LNTCP (0.79/0.75), RS (0.80/0.77), gEUD (0.81/0.78), and LDA (0.84/0.86). Only the LDA model benefited from SPECT-based regional functional information. In general, the LDA model was statistically superior to the parametric models. The LDA model selected as features the left ventricular volumes above approximately 23 Gy (V23), essentially volume in field, and 33 Gy (V33), as best separating the groups with and without defects. In conclusion, the nonparametric LDA model appears to be a more accurate predictor of radiotherapy-induced left ventricular perfusion defects than commonly used parametric models.
Medical Physics 02/2005; 32(1):19-27. · 2.83 Impact Factor
