Purpose To determine whether in-house patient-specific IMRT QA results predict the Imaging and Radiation Oncology Core (IROC)-Houston phantom results. Methods and Materials IROC Houston’s IMRT head and neck phantoms have been irradiated by numerous institutions as part of clinical trial credentialing. We retrospectively compared these phantom results with those of in-house IMRT QA (following the institution’s clinical process) for 855 irradiations performed between 2003 and 2013. The sensitivity and specificity of IMRT QA to detect unacceptable or acceptable plans was determined relative to the IROC Houston phantom results. Additional analyses evaluated specific IMRT QA dosimeters and analysis methods. Results IMRT QA universally showed poor sensitivity relative to the head and neck phantom i.e., poor ability to predict a failing IROC Houston phantom result. Depending on how the IMRT QA results were interpreted, overall sensitivity ranged from 2% to 18%. For different IMRT QA methods, sensitivity ranged from 3% to 54%. Although the observed sensitivity was particularly poor at clinical thresholds (e.g., 3% dose difference or 90% of pixels passing gamma), receiver operator characteristic analysis indicated that no threshold showed good sensitivity and specificity for the devices evaluated. Conclusions IMRT QA is not a reasonable replacement for a credentialing phantom. Moreover, the particularly poor agreement between IMRT QA and the IROC Houston phantoms highlights surprising inconsistency in the QA process.

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.

Summary A retrospective study of 70 lung cancer patients with 4DCT images was performed to estimate the toxicity reduction achieved when using CT-ventilation functional avoidance radiotherapy in place of traditional radiotherapy. Normal tissue complication probability (NTCP) models were developed from clinically used plans based on dose to functional lung. Thirty patients were re-planned using functional avoidance techniques and RTOG 0617 constraints, and the reduction in lung toxicity was assessed using the NTCP models. Purpose CT-ventilation imaging is a new modality that uses 4DCT information to calculate lung ventilation. While retrospective studies have reported on the reduction in dose to functional lung, no work has been published in which the dosimetric improvements have been translated to a reduction in the probability of pulmonary toxicity. Our work estimates the reduction in toxicity for CT-ventilation based functional avoidance planning. Methods and Materials Seventy previously treated lung cancer patients with 4DCT imaging were used for the study. CT-ventilation maps were calculated using the 4DCT, deformable image registration, and a density-change-based algorithm. Pneumonitis was graded based on imaging and clinical presentation. Maximum-likelihood methods were used to generate normal tissue complication probability (NTCP) models predicting grade 2 or higher (2+) and grade 3+ pneumonitis as a function of dose (V5Gy, V10Gy, V20Gy, V30Gy, and mean dose) to functional lung. For thirty patients a functional plan was generated with the goal of reducing dose to the functional lung while meeting RTOG 0617 constraints. The NTCP models were applied to the functional plans and the clinically-used plans to calculate toxicity reduction. Results Using functional avoidance planning, absolute reductions in grade 2+ NTCP of 6.3%, 7.8% and 4.8% were achieved based on the mean fV20Gy, fV30Gy, and fMLD metrics, respectively. Absolute grade 3+ NTCP reductions of 3.6%, 4.8%, and 2.4% were achieved with fV20Gy, fV30Gy, and fMLD. Maximum absolute reductions of 52.3% and 16.4% were seen for grade 2+ and grade 3+ pneumonitis for individual patients. Conclusion Our study quantifies the possible toxicity reduction from CT-ventilation-based functional avoidance planning. Reductions in grades 2+ and 3+ pneumonitis were 7.1% and 4.7% based on mean dose-function metrics with reductions as high as 52.3% for individual patients. Our work provides seminal data for determining the potential toxicity benefit from incorporating CT-ventilation into thoracic treatment planning.

Purpose 4DCT-ventilation imaging is increasingly being used to calculate lung ventilation and implement functional-guided radiotherapy in clinical trials. There has been little exhaustive work evaluating which dose-function metrics should be used for treatment planning and plan evaluation. The purpose of our study was to evaluate which dose-function metrics best predict for radiation pneumonitis (RP). Methods and Materials Seventy lung cancer patients with 4DCT imaging and pneumonitis grading were used. Pre-treatment 4DCTs of each patient were used to calculate ventilation images. We evaluated 3 types of dose function metrics that combined that patient’s 4DCT-ventilation image and treatment planning dose distribution: 1) structure-based approaches 2) image-based approaches using the dose-function histogram (DFH) and 3) non-linear weighting schemes. Log-likelihood methods were used to generate normal tissue complication probability (NTCP) models predicting grade 3+ pneumonitis for all dose-function schemes. The area under the curve (AUC) was used to assess the predictive power of the models. All techniques were compared to NTCP models based on traditional, total lung dose metrics. Results The most predictive models were structure-based approaches that focused on the volume of functional lung receiving ≥20Gy (AUC=0.70). Probability of grade 3+ RP of 20% and 10% correspond to V20Gy to the functional sub-volumes of 26.8% and 9.3%, respectively. Imaging-based analysis with the DFH and non-linear weighted ventilation values yielded AUCs of 0.66 and 0.67, respectively, when evaluating the percentage of functionality receiving ≥20Gy. All dose-function metrics outperformed the traditional dose metrics (mean lung dose, AUC=0.55). Conclusion A full range of dose-function metrics and functional thresholds were examined. The calculated AUC values for the most predictive functional models occupied a narrow range (0.66–0.70) and all demonstrated notable improvements over AUC from traditional lung dose metrics (0.55). Identifying the combinations most predictive of grade 3+ RP provides valuable data to inform the functional-guided radiotherapy process.