肺癌の定位放射線治療(SBRT)における変形可能な線量マッピングおよび蓄積技術
Indrin J Chetty1, Hualiang Zhong2
1Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles CA 90048.
Abstract:
Deformable dose mapping (DDM) and accumulation (DDA) are essential tools in lung cancer stereotactic body radiation therapy (SBRT). Here we provide a critical review of deformable image registration (DIR)-based dose mapping and accumulation techniques in stereotactic body radiation therapy (SBRT) for lung cancers, with emphasis on methodological principles, clinical applications, limitations, and guidance for practice. A broad appraisal of the literature was conducted, emphasizing deformable image registration (DIR) algorithms and related dose mapping strategies, including direct dose mapping (DDM), voxel warping, and energy/mass-congruent mapping (EMCM). These methods were examined across key clinical scenarios for lung SBRT planning, including motion management, adaptive radiotherapy (ART) and re-irradiation. Significant errors can occur when anatomic changes are large, such as tumor regression, mass and density variations, etc. as observed in re-irradiation scenarios. These errors will propagate to the mapped and composite dose distributions, particularly in steep dose gradients, resulting in inaccuracies. Biomechanical models combined with EMCM better preserve physical principles under such conditions. Quality assurance remains challenging due to absence of standardized benchmarks. Tools for validation of DIR and DDA accuracy in the clinic are severely lacking. Development of quality assurance frameworks are critical toward safe implementation. Clinicians should apply DIR-based dose accumulation conservatively, particularly when anatomy changes considerably in re-irradiation settings, given the potential for significant uncertainties in the composite doses. Each clinical case should be viewed carefully by assessing the risk/benefit, and clinical application should follow cooperative group guidelines. Standardization of methods for dose accumulation will enhance dose-volume-effect modeling.
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