Purpose: It is estimated that in developed countries, close to half of all courses of radiotherapy are employed with palliative intent. Cost, transportation, and treatment duration have been identified as barriers for patients who could benefit from palliative radiotherapy. As CT simulation requires a separate appointment, it contributes to these barriers. In this study, we examined if a patient’s previous diagnostics scans could be used in place of CT simulation while treating with clinically effective volumetric-modulated arc therapy (VMAT).
Methods: Ten spinal metastatic cases were included in this study. Using each patient’s diagnostic scan treatment targets and organs at risk were delineated. Next, two VMAT arcs were employed for each treatment plan on each diagnostic CT with arm and field of view avoidance. All plans were optimized at D95% = 100% and Dmax < 107% with dose constraints to the spinal cord, esophagus, and heart. Diagnostic scan-based plans (DSBP) were then transferred onto CT simulation data sets and recalculated to determine the accuracy of dose calculation using DSBP.
Results: DSBP provided excellent target coverage, with a median D95% of 98% (range, 92%-100%) of the prescription dose with acceptable hot spots, and a median Dmax of 107% (range, 105%-112%). The plans were reviewed by three physicians and determined to be appropriate for treatment. The transferring of plans between diagnostic and simulation scans resulted in changes to D95% of -1.92% ± 2.33% and Dmax of 1.14% ± 2.02% respectively, with absolute differences in D95% of 2.02% ± 2.24% and Dmax of 1.67% ± 1.61% respectively.
Conclusion: This preliminary study suggests that diagnostic scan-based planning with VMAT for spinal metastases is a practical approach with an acceptable dosimetrical accuracy. The benefits of earlier radiobiological effects, reduction in required patient commitment, and simplified workflow predominate the relatively small changes to plan accuracy.
Funding Support, Disclosures, and Conflict of Interest: This study was funded by a research grant awarded by Varian Medical Systems, Inc. (Palo Alto, CA).