Purpose Four-dimensional computed tomography (4DCT)-based ventilation is an emerging imaging modality that can be used in the thoracic treatment planning process. The clinical benefit of using ventilation images in radiation treatment plans remains to be tested. The purpose of the current work was to test the potential benefit of using ventilation in treatment planning by evaluating whether dose to highly ventilated regions of the lung resulted in increased incidence of clinical toxicity. Methods and Materials Pretreatment 4DCT data were used to compute pretreatment ventilation images for 96 lung cancer patients. Ventilation images were calculated using 4DCT data, deformable image registration, and a density-change based algorithm. Dose—volume and ventilation-based dose function metrics were computed for each patient. The ability of the dose—volume and ventilation-based dose—function metrics to predict for severe (grade 3+) radiation pneumonitis was assessed using logistic regression analysis, area under the curve (AUC) metrics, and bootstrap methods. Results A specific patient example is presented that demonstrates how incorporating ventilation-based functional information can help separate patients with and without toxicity. The logistic regression significance values were all lower for the dose—function metrics (range P=.093-.250) than for their dose—volume equivalents (range, P=.331-.580). The AUC values were all greater for the dose—function metrics (range, 0.569-0.620) than for their dose—volume equivalents (range, 0.500-0.544). Bootstrap results revealed an improvement in model fit using dose—function metrics compared to dose—volume metrics that approached significance (range, P=.118-.155). Conclusions To our knowledge, this is the first study that attempts to correlate lung dose and 4DCT ventilation-based function to thoracic toxicity after radiation therapy. Although the results were not significant at the .05 level, our data suggests that incorporating ventilation-based functional imaging can improve prediction for radiation pneumonitis. We present an important first step toward validating the use of 4DCT-based ventilation imaging in thoracic treatment planning.

Purpose To determine the incidence of and risk factors for radiation pneumonitis (RP) after stereotactic ablative radiation therapy (SABR) to the lung in patients who had previously undergone conventional thoracic radiation therapy. Methods and Materials Seventy-two patients who had previously received conventionally fractionated radiation therapy to the thorax were treated with SABR (50 Gy in 4 fractions) for recurrent disease or secondary parenchymal lung cancer (T <4 cm, N0, M0, or Mx). Severe (grade ≥3) RP and potential predictive factors were analyzed by univariate and multivariate logistic regression analyses. A scoring system was established to predict the risk of RP. Results At a median follow-up time of 16 months after SABR (range, 4-56 months), 15 patients had severe RP (14 [18.9%] grade 3 and 1 [1.4%] grade 5) and 1 patient (1.4%) had a local recurrence. In univariate analyses, Eastern Cooperative Oncology Group performance status (ECOG PS) before SABR, forced expiratory volume in 1 second (FEV1), and previous planning target volume (PTV) location were associated with the incidence of severe RP. The V10 and mean lung dose (MLD) of the previous plan and the V10-V40 and MLD of the composite plan were also related to RP. Multivariate analysis revealed that ECOG PS scores of 2-3 before SABR (P=.009), FEV1 ≤65% before SABR (P=.012), V20 ≥30% of the composite plan (P=.021), and an initial PTV in the bilateral mediastinum (P=.025) were all associated with RP. Conclusions We found that severe RP was relatively common, occurring in 20.8% of patients, and could be predicted by an ECOG PS score of 2-3, an FEV1 ≤ 65%, a previous PTV spanning the bilateral mediastinum, and V20 ≥30% on composite (previous RT + SABR) plans. Prospective studies are needed to validate these predictors and the scoring system on which they are based.

We did not find a consistent pattern of ventilation change as a function of radiation dose. Pretreatment ventilation was significantly lower for lobes that contained tumor, due to occlusion of the central airway. The weekly lobe ventilation data indicated that when tumor volume shrinks, ventilation increases, and when the thoracic anatomy is not visibly changed, ventilation is likely to remain unchanged.

Purpose To determine whether single nucleotide polymorphisms (SNPs) in genes associated with DNA repair, cell cycle, transforming growth factor beta, tumor necrosis factor and receptor, folic acid metabolism, and angiogenesis can significantly improve the fit of the Lyman-Kutcher-Burman (LKB) normal-tissue complication probability (NTCP) model of radiation pneumonitis (RP) risk among patients with non-small cell lung cancer (NSCLC). Methods and Materials Sixteen SNPs from 10 different genes (XRCC1, XRCC3, APEX1, MDM2, TGFβ, TNFα, TNFR, MTHFR, MTRR, and VEGF) were genotyped in 141 NSCLC patients treated with definitive radiotherapy, with or without chemotherapy. The LKB model was used to estimate the risk of severe (Grade ≥3) RP as a function of mean lung dose (MLD), with SNPs and patient smoking status incorporated into the model as dose-modifying factors. Multivariate (MV) analyses were performed by adding significant factors to the MLD model in a forward stepwise procedure, with significance assessed using the likelihood-ratio test. Bootstrap analyses were used to assess the reproducibility of results under variations in the data. Results Five SNPs were selected for inclusion in the multivariate NTCP model based on MLD alone. SNPs associated with an increased risk of severe RP were in genes for TGFβ, VEGF, TNFα, XRCC1 and APEX1. With smoking status included in the MV model, the SNPs significantly associated with increased risk of RP were in genes for TGFβ, VEGF, and XRCC3. Bootstrap analyses selected a median of 4 SNPs per model fit, with the 6 genes listed above selected most often. Conclusions This study provides evidence that SNPs can significantly improve the predictive ability of the Lyman MLD model. With a small number of SNPs, it was possible to distinguish cohorts with >50% risk versus <10% risk of RP when exposed to high MLDs.

Stereotactic body radiation therapy (SBRT) has emerged as an effective, noninvasive treatment option for primary liver cancer and metastatic disease occurring in the liver. Although SBRT can be highly effective for establishing local control in hepatic malignancies, a tradeoff exists between tumor control and normal tissue complications. The objective of the present study was to review the normal tissue dose-volume effects for SBRT-induced liver and gastrointestinal toxicities and derive normal tissue complication probability models.

Purpose A new form of functional imaging has been proposed in the form of 4DCT-ventilation. Because 4DCTs are acquired as part of routine care for lung cancer patients, calculating ventilation maps from 4DCTs provides spatial lung function information without added dosimetric or monetary cost to the patient. Before 4DCT-ventilation is implemented it needs to be clinically validated. Pulmonary function tests (PFTs) provide a clinically established way of evaluating lung function. The purpose of our work was to perform a clinical validation by comparing 4DCT-ventilation metrics with PFT data. Methods and Materials Ninety-eight lung cancer patients with pre-treatment 4DCT and PFT data were included in the study. PFT metrics used to diagnose obstructive lung disease were recorded: forced expiratory volume in 1 second (FEV1) and FEV1/forced vital capacity (FEV1/FVC). 4DCT data sets and spatial registration were used to compute 4DCT-ventilation images using a density-change based and a Jacobian-based model. The ventilation maps were reduced to single metrics intended to reflect the degree of ventilation obstruction. Specifically, we computed the coefficient of variation (CoV) (standard deviation/mean), ventilation V20 (volume of lung ≤20% ventilation), and correlated the ventilation metrics with PFT data. Regression analysis was used to determine whether 4DCT-ventilation data could predict for normal versus abnormal lung function using PFT thresholds. Results Correlation coefficients comparing 4DCT-ventilation to PFT data ranged from 0.63–0.72 with the best agreement between FEV1 and CoV. 4DCT-ventilation metrics were able to significantly delineate between clinically normal versus abnormal PFT results. Conclusions Validation of 4DCT-ventilation with clinically relevant metrics is essential. We demonstrate good global agreement between PFTs and 4DCT-ventilation, indicating that 4DCT ventilation provides a reliable assessment of lung function. 4DCT-ventilation enables exciting opportunities to assess lung function and create functional avoidance radiotherapy plans. The current work presents supporting evidence for the integration of 4DCT-ventilation into clinical trials.

Computed tomography (CT) derived ventilation algorithms estimate the apparent voxel volume changes within an inhale/exhale CT image pair. Transformation-based methods compute these estimates solely from the spatial transformation acquired by applying a deformable image registration (DIR) algorithm to the image pair. However, approaches based on finite difference approximations of the transformation’s Jacobian have been shown to be numerically unstable. As a result, transformation-based CT ventilation is poorly reproducible with respect to both DIR algorithm and CT acquisition method. Purpose: We introduce a novel Integrated Jacobian Formulation (IJF) method for estimating voxel volume changes under a DIR recovered spatial transformation. The method is based on computing volume estimates of DIR mapped subregions using the hit-or-miss sampling algorithm for integral approximation. The novel approach allows for regional volume change estimates that 1) respect the resolution of the digital grid and 2) are based on approximations with quantitatively characterized and controllable levels of uncertainty. As such, the IJF method is designed to be robust to variations in DIR solutions and thus overall more reproducible. Methods: Numerically, Jacobian estimates are recovered by solving a simple constrained linear least squares problem that guarantees the recovered global volume change is equal to the global volume change obtained from the inhale and exhale lung segmentation masks. Reproducibility of the IJF method with respect to DIR solution was assessed using the expert-determined landmark point pairs and inhale/exhale phases from 10 4DCTs available on www.dir-lab.com. Reproducibility with respect to CT acquisition was assessed on the 4DCT and 4D cone beam CT (4DCBCT) images acquired for five lung cancer patients prior to radiotherapy. Results: The ten Dir-Lab 4DCT cases were registered twice with the same DIR algorithm, but with different smoothing parameter. Finite difference Jacobian (FDJ) and IFJ images were computed for both solutions. The average spatial errors (300 landmarks per case) for the two DIR solution methods were 0.98 (1.10) and 1.02 (1.11). The average Pearson correlation between the FDJ images computed from the two DIR solutions was 0.83 (0.03), while for the IJF images it was 1.00 (0.00). For inter-modality assessment, the IJF and FDJ images were computed from the 4DCT and 4DCBCT of five patients. The average Pearson correlation of the spatially aligned FDJ images was 0.27 (0.11), while it was 0.77 (0.13) for the IFJ method. Conclusion: The mathematical theory underpinning the IJF method allows for the generation of ventilation images that are 1) computed with respect to DIR spatial accuracy on the digital voxel grid and 2) based on DIR measured subregional volume change estimates acquired with quantifiable and controllable levels of uncertainty. Analyses of the experiments are consistent with the mathematical theory and indicate that IJF ventilation imaging has a higher reproducibility with res...

Purpose: Functional imaging has been proposed that uses 4DCT images to calculate 4DCT-based lung ventilation (4DCT-ventilation). We have started a 2-institution, phase 2 prospective trial evaluating the feasibility, safety, and preliminary efficacy of 4DCT-ventilation functional avoidance. The trial hypothesis is that the rate of grade ≥2 radiation pneumonitis could be reduced to 12% with functional avoidance, compared with a 25% rate of pneumonitis with a historical control. The trial employed a Simon 2-stage design with a planned futility analysis after 17 evaluable patients. The purpose of this work is to present the trial design and implementation, dosimetric data, and clinical results for the planned futility analysis. Methods and Materials: Eligible patients were patients with lung cancer who were prescribed doses of 45 to 75 Gy. For each patient, the 4DCT data were used to generate a 4DCT-ventilation image using the Hounsfield unit technique along with a compressible flow-based image registration algorithm. Two intensity modulated radiation therapy treatment plans were generated: (1) a standard lung plan and (2) a functional avoidance treatment plan that aimed to reduce dose to functional lung while meeting target and normal tissue constraints. Patients were treated with the functional avoidance plan and evaluated for thoracic toxicity (presented as rate and 95% confidence intervals [CI]) with a 1-year follow-up. Results: The V20 to functional lung was 21.6% ± 9.5% (mean ± standard deviation) with functional avoidance, representing a decrease of 3.2% (P < .01) relative to standard, nonfunctional treatment plans. The rates of grade ≥2 and grade ≥3 radiation pneumonitis were 17.6% (95% CI, 3.8%−43.4%) and 5.9% (95% CI, 0.1%−28.7%), respectively. Conclusions: Dosimetrically, functional avoidance achieved reduction in doses to functional lung while meeting target and organ at risk constraints. On the basis of Simon’s 2-stage design and the 17.6% grade ≥2 pneumonitis rate, the trial met its futility criteria and has continued accrual.