Michele Avanzo1, Sara Barbiero2, Marco Trovo3, Jean-Pierre Bissonnette4, Rajesh Jena5, Joseph Stancanello6, Giovanni Pirrone7, Fabio Matrone8, Emilio Minatel8, Cristina Cappelletto7, Carlo Furlan8, David A Jaffray4, Giovanna Sartor7. 1. Medical Physics, Centro di Riferimento Oncologico IRCCS Aviano, 33081 Aviano, Italy. Electronic address: mavanzo@cro.it. 2. Radiotherapy Department, Casa di Cura S. Rossore, Pisa, Italy. 3. Radiation Oncology Department, Centro di Riferimento Oncologico IRCCS Aviano, 33081 Aviano, Italy; Radiation Oncology Department, Azienda Sanitaria Universitaria Integrata, Udine, Italy. 4. Department of Radiation Oncology, University of Toronto, Toronto, Canada; Department of Medical Physics, Princess Margaret Cancer Centre, Toronto, Canada. 5. Department of Oncology, University of Cambridge, Cambridge CB2 0QQ, UK. 6. Centro di Riferimento Oncologico IRCCS Aviano, 33081 Aviano, Italy. 7. Medical Physics, Centro di Riferimento Oncologico IRCCS Aviano, 33081 Aviano, Italy. 8. Radiation Oncology Department, Centro di Riferimento Oncologico IRCCS Aviano, 33081 Aviano, Italy.
Abstract
PURPOSE: To correlate radiation dose to the risk of severe radiologically-evident radiation-induced lung injury (RRLI) using voxel-by-voxel analysis of the follow-up computed tomography (CT) of patients treated for lung cancer with hypofractionated helical Tomotherapy. METHODS AND MATERIALS: The follow-up CT scans from 32 lung cancer patients treated with various regimens (5, 8, and 25 fractions) were registered to pre-treatment CT using deformable image registration (DIR). The change in density was calculated for each voxel within the combined lungs minus the planning target volume (PTV). Parameters of a Probit formula were derived by fitting the occurrences of changes of density in voxels greater than 0.361gcm-3 to the radiation dose. The model's predictive capability was assessed using the area under receiver operating characteristic curve (AUC), the Kolmogorov-Smirnov test for goodness-of-fit, and the permutation test (Ptest). RESULTS: The best-fit parameters for prediction of RRLI 6months post RT were D50 of 73.0 (95% CI 59.2.4-85.3.7)Gy, and m of 0.41 (0.39-0.46) for hypofractionated (5 and 8 fractions) and D50 of 96.8 (76.9-123.9)Gy, and m of 0.36 (0.34-0.39) for 25 fractions RT. According to the goodness-of-fit test the null hypothesis of modeled and observed occurrence of RRLI coming from the same distribution could not be rejected. The AUC was 0.581 (0.575-0.583) for fractionated and 0.579 (0.577-0.581) for hypofractionated patients. The predictive models had AUC>upper 95% band of the Ptest. CONCLUSIONS: The correlation of voxel-by-voxel density increase with dose can be used as a support tool for differential diagnosis of tumor from benign changes in the follow-up of lung IMRT patients.
PURPOSE: To correlate radiation dose to the risk of severe radiologically-evident radiation-induced lung injury (RRLI) using voxel-by-voxel analysis of the follow-up computed tomography (CT) of patients treated for lung cancer with hypofractionated helical Tomotherapy. METHODS AND MATERIALS: The follow-up CT scans from 32 lung cancerpatients treated with various regimens (5, 8, and 25 fractions) were registered to pre-treatment CT using deformable image registration (DIR). The change in density was calculated for each voxel within the combined lungs minus the planning target volume (PTV). Parameters of a Probit formula were derived by fitting the occurrences of changes of density in voxels greater than 0.361gcm-3 to the radiation dose. The model's predictive capability was assessed using the area under receiver operating characteristic curve (AUC), the Kolmogorov-Smirnov test for goodness-of-fit, and the permutation test (Ptest). RESULTS: The best-fit parameters for prediction of RRLI 6months post RT were D50 of 73.0 (95% CI 59.2.4-85.3.7)Gy, and m of 0.41 (0.39-0.46) for hypofractionated (5 and 8 fractions) and D50 of 96.8 (76.9-123.9)Gy, and m of 0.36 (0.34-0.39) for 25 fractions RT. According to the goodness-of-fit test the null hypothesis of modeled and observed occurrence of RRLI coming from the same distribution could not be rejected. The AUC was 0.581 (0.575-0.583) for fractionated and 0.579 (0.577-0.581) for hypofractionated patients. The predictive models had AUC>upper 95% band of the Ptest. CONCLUSIONS: The correlation of voxel-by-voxel density increase with dose can be used as a support tool for differential diagnosis of tumor from benign changes in the follow-up of lung IMRT patients.
Authors: Pouya Jelvehgaran; Jeffrey D Steinberg; Artem Khmelinskii; Gerben Borst; Ji-Ying Song; Niels de Wit; Daniel M de Bruin; Marcel van Herk Journal: Radiat Oncol Date: 2019-10-30 Impact factor: 3.481