PURPOSE: When using non-patient-specific treatment planning margins, respiratory motion may lead to geometric miss of the target while unnecessarily irradiating normal tissue. Imaging different respiratory states of a patient allows patient-specific target design. We used four-dimensional computed tomography (4DCT) to characterize tumor motion and create treatment volumes in 10 patients with lung cancer. These were compared with standard treatment volumes. METHODS AND MATERIALS: Four-dimensional CT and free breathing helical CT data of 10 patients were acquired. Gross target volumes (GTV) were delineated on the helical scan as well as on each phase of the 4D data. Composite GTVs were defined on 4DCT. Planning target volumes (PTV) including clinical target volume, internal margin (IM), and setup margin were generated. 4DPTVs with different IMs and standard PTVs were compared by computing centroid positions, volumes, volumetric overlap, and bounding boxes. RESULTS: Four-dimensional PTVs and conventional PTVs differed in volume and centroid positions. Overlap between 4DPTVs generated from two extreme tumor positions only compared with 10 respiratory phases was 93.7%. Comparing PTVs with margins of 15 mm (IM 5 mm) on composite 4D target volumes to PTVs with 20 mm (IM 10 mm) on helical CT data resulted in a decrease in target volume sizes by 23% on average. CONCLUSION: With patient-specific characterization of tumor motion, it should be possible to decrease internal margins. Patient-specific treatment volumes can be generated using extreme tumor positions on 4DCT. To date, more than 150 patients have been treated using 4D target design.
PURPOSE: When using non-patient-specific treatment planning margins, respiratory motion may lead to geometric miss of the target while unnecessarily irradiating normal tissue. Imaging different respiratory states of a patient allows patient-specific target design. We used four-dimensional computed tomography (4DCT) to characterize tumor motion and create treatment volumes in 10 patients with lung cancer. These were compared with standard treatment volumes. METHODS AND MATERIALS: Four-dimensional CT and free breathing helical CT data of 10 patients were acquired. Gross target volumes (GTV) were delineated on the helical scan as well as on each phase of the 4D data. Composite GTVs were defined on 4DCT. Planning target volumes (PTV) including clinical target volume, internal margin (IM), and setup margin were generated. 4DPTVs with different IMs and standard PTVs were compared by computing centroid positions, volumes, volumetric overlap, and bounding boxes. RESULTS: Four-dimensional PTVs and conventional PTVs differed in volume and centroid positions. Overlap between 4DPTVs generated from two extreme tumor positions only compared with 10 respiratory phases was 93.7%. Comparing PTVs with margins of 15 mm (IM 5 mm) on composite 4D target volumes to PTVs with 20 mm (IM 10 mm) on helical CT data resulted in a decrease in target volume sizes by 23% on average. CONCLUSION: With patient-specific characterization of tumor motion, it should be possible to decrease internal margins. Patient-specific treatment volumes can be generated using extreme tumor positions on 4DCT. To date, more than 150 patients have been treated using 4D target design.