In response to Dr. Bayne et al.
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2. Erdi YE, Rosenzweig K, Erdi AK, et al. Radiotherapy treatment plan- ning for patients with non-small cell lung cancer using positron emis- sion tomography (PET). Radiother Oncol 2002;62:51– 60. 3. Hicks RJ, Mac Manus MP, Matthews JP, et al. Early FDG-PET imaging after radical radiotherapy for non-small-cell lung cancer: inflammatory changes in normal tissues correlate with tumor response and do not confound therapeutic response evaluation. Int J Radiat Oncol Biol Phys 2004;60:412– 418. 4. Caldwell CB, Mah K, Skinner M, et al. Can PET provide the 3D extent of tumor motion for individualized internal target volumes? A phantom study of the limitations of CT and the promise of PET. Int J Radiat Oncol Biol Phys 2003;55:1381–1393. IN RESPONSE TO DR. BAYNE ET AL. To the Editor: We appreciate the comments from Dr. Bayne et al. on our recent article “Defining a radiotherapy target with positron emission tomography.” Dr. Bayne et al. have addressed their concerns on (1) using a mathematical model may result in failure to gain maximum advantage from the ability of a skilled positron emission tomography (PET) specialist working with a radiation oncologist in target delineation and, (2) given the variability of tumor mobility, mathematical models may not ever be useful in the target delineation of PET image. The first concern is unnecessary. The proposed mathematical algorithm should only be used to determine the boundary of target volume followed by user identification of tumor location manifested on a PET image. Consequently, it helps the radiation oncologist and PET specialist to gain their maximum capability in the target delineation. A PET image has been physically generated through three-dimensional signal detection of positron emission, fundamentally a physics and image detection process. Within this process, the target boundary shown on a PET image gets blurred and cannot be visually determined by a human being. Therefore, regardless of the user’s experience, there are always uncertainties associ- ated with manual delineation of target volume. Developing a good math- ematical model and algorithm for PET image target delineation would not replace the radiation oncologist’s or PET specialist’s function in target definition; instead it provides a useful tool for them to increase their ability in the target delineation. We share their second concern on the difficulties in the model-based PET image target delineation. As has been pointed out in our discussion (see the last paragraph in the Discussion section of reference 1) and in the comments, high background uptake, heterogeneity uptake in the target volume, and tumor mobility will cause discrepancy in applying the pro- posed iterative algorithm. In fact, these factors cause uncertainties to any methods of target delineation, whether manual- or model-based. However, effects of these uncertainties on target delineation could be reduced by an improved physics and mathematical model and algorithm. We are working on this subject to improve our model based target delineation. Hopefully, our results can soon relieve their concerns. Finally, we like to reclaim that the model developed in this study has only been tested using phantom experiments under ideal conditions of PET imaging. Uncertainties, such as uptake heterogeneity and tumor motion, have not been considered in the current algorithm. In addition, the param- eters used in the linear regression function were determined using our specific PET equipment and volume contouring method, which should not be directly applied without recalibrating the model to different equipment and computer contouring technique. In addition, the target boundary de- tection algorithm has not been designed for tumor diagnosis, and should not be applied in isolation to delineate the target. However, it can be a useful tool to correct observers’ uncertainties and help radiation oncologist and PET specialist in target delineation. DI YAN, D.SC. INGA S. GRILLS, M.D. LARRY L. KESTIN, M.D. Department of Radiation Oncology William Beaumont Hospital Royal Oak, MI QUINTEN G. BLACK, M.D. 21st Century Oncology, Inc. Asheville, NC doi:10.1016/j.ijrobp.2005.01.051 300 I. J. Radiation Oncology ● Biology ● Physics Volume 62, Number 1, 2005
Transcript of In response to Dr. Bayne et al.
2. Erdi YE, Rosenzweig K, Erdi AK, et al. Radiotherapy treatment plan-ning for patients with non-small cell lung cancer using positron emis-sion tomography (PET). Radiother Oncol 2002;62:51–60.
3. Hicks RJ, Mac Manus MP, Matthews JP, et al. Early FDG-PET imagingafter radical radiotherapy for non-small-cell lung cancer: inflammatorychanges in normal tissues correlate with tumor response and do notconfound therapeutic response evaluation. Int J Radiat Oncol Biol Phys2004;60:412–418.
4. Caldwell CB, Mah K, Skinner M, et al. Can PET provide the 3D extentof tumor motion for individualized internal target volumes? A phantomstudy of the limitations of CT and the promise of PET. Int J RadiatOncol Biol Phys 2003;55:1381–1393.
IN RESPONSE TO DR. BAYNE ET AL.
To the Editor: We appreciate the comments from Dr. Bayne et al. onour recent article “Defining a radiotherapy target with positron emissiontomography.” Dr. Bayne et al. have addressed their concerns on (1) usinga mathematical model may result in failure to gain maximum advantagefrom the ability of a skilled positron emission tomography (PET) specialistworking with a radiation oncologist in target delineation and, (2) given thevariability of tumor mobility, mathematical models may not ever be usefulin the target delineation of PET image.
The first concern is unnecessary. The proposed mathematical algorithmshould only be used to determine the boundary of target volume followedby user identification of tumor location manifested on a PET image.Consequently, it helps the radiation oncologist and PET specialist to gaintheir maximum capability in the target delineation. A PET image has beenphysically generated through three-dimensional signal detection ofpositron emission, fundamentally a physics and image detection process.Within this process, the target boundary shown on a PET image getsblurred and cannot be visually determined by a human being. Therefore,regardless of the user’s experience, there are always uncertainties associ-ated with manual delineation of target volume. Developing a good math-ematical model and algorithm for PET image target delineation would notreplace the radiation oncologist’s or PET specialist’s function in target
definition; instead it provides a useful tool for them to increase their abilityin the target delineation.
We share their second concern on the difficulties in the model-basedPET image target delineation. As has been pointed out in our discussion(see the last paragraph in the Discussion section of reference 1) and in thecomments, high background uptake, heterogeneity uptake in the targetvolume, and tumor mobility will cause discrepancy in applying the pro-posed iterative algorithm. In fact, these factors cause uncertainties to anymethods of target delineation, whether manual- or model-based. However,effects of these uncertainties on target delineation could be reduced by animproved physics and mathematical model and algorithm. We are workingon this subject to improve our model based target delineation. Hopefully,our results can soon relieve their concerns.
Finally, we like to reclaim that the model developed in this study hasonly been tested using phantom experiments under ideal conditions of PETimaging. Uncertainties, such as uptake heterogeneity and tumor motion,have not been considered in the current algorithm. In addition, the param-eters used in the linear regression function were determined using ourspecific PET equipment and volume contouring method, which should notbe directly applied without recalibrating the model to different equipmentand computer contouring technique. In addition, the target boundary de-tection algorithm has not been designed for tumor diagnosis, and shouldnot be applied in isolation to delineate the target. However, it can be auseful tool to correct observers’ uncertainties and help radiation oncologistand PET specialist in target delineation.
DI YAN, D.SC.INGA S. GRILLS, M.D.LARRY L. KESTIN, M.D.Department of Radiation OncologyWilliam Beaumont HospitalRoyal Oak, MI
QUINTEN G. BLACK, M.D.21st Century Oncology, Inc.Asheville, NC
doi:10.1016/j.ijrobp.2005.01.051
300 I. J. Radiation Oncology ● Biology ● Physics Volume 62, Number 1, 2005
