Method for simultaneous selection of treatment isocenters and margins for polymetastatic extracranial stereotactic ablative radiotherapy.
Stereotactic ablative radiotherapy (SABR) represents a new era of treatment for polymetastatic extracranial disease but introduces unique challenges in isocenter selection and margin optimization to balance treatment efficiency and normal tissue sparing.
Develop an end-to-end method to simultaneously cluster M tumor targets and optimize isocenter number (N) and position to minimize the additional margins required to maintain target coverage as tumor-to-isocenter distance increases.
K-means clustering identified candidate isocenters ranging from one to one per target. Margins required for 95% coverage probability were determined based on tumor-to-isocenter distance, accounting for translational (5 mm) and rotational (1°, 2°, 3°) uncertainties modeled as three-dimensional normal distributions. K-means total margin volume was compared versus isotropic 5- and 10-mm margins and benchmarked against a derivative-free hybrid optimization method. The method was evaluated on 20 clinical lung cases with 2-21 targets per patient, target-to-target distances of 3.8-32.4 cm, and volumes of 0.07-41.05 cm3.
The total margin volume determined by k-means showed no significant differences (p = 0.94) relative to the hybrid optimization method, with median differences of 0.03% (0.01 cm3), 0.14% (0.06 cm3), and 0.19% (0.15 cm3) for 1°, 2°, and 3° rotational uncertainties, respectively. Significant (p < 0.0167) differences in total margin were observed versus fixed margins of 5 or 10 mm. Increasing the number of isocenters for a given patient reduced the median total margin volume by 31.0% for N = 2 versus N = 1 and 15.7% for N = 3 versus N = 2, but diminishing returns were observed as additional isocenters were added (N = 4 to N = 6: -6.6%, -5.4%, and -3.7%, respectively). Median run time was 0.19 s for k-means versus 306.9 s for the reference method.
K-means clustering offers an efficient method for selecting the number and locations of isocenters to reduce the total margin volume in multi-target extracranial SABR.
Develop an end-to-end method to simultaneously cluster M tumor targets and optimize isocenter number (N) and position to minimize the additional margins required to maintain target coverage as tumor-to-isocenter distance increases.
K-means clustering identified candidate isocenters ranging from one to one per target. Margins required for 95% coverage probability were determined based on tumor-to-isocenter distance, accounting for translational (5 mm) and rotational (1°, 2°, 3°) uncertainties modeled as three-dimensional normal distributions. K-means total margin volume was compared versus isotropic 5- and 10-mm margins and benchmarked against a derivative-free hybrid optimization method. The method was evaluated on 20 clinical lung cases with 2-21 targets per patient, target-to-target distances of 3.8-32.4 cm, and volumes of 0.07-41.05 cm3.
The total margin volume determined by k-means showed no significant differences (p = 0.94) relative to the hybrid optimization method, with median differences of 0.03% (0.01 cm3), 0.14% (0.06 cm3), and 0.19% (0.15 cm3) for 1°, 2°, and 3° rotational uncertainties, respectively. Significant (p < 0.0167) differences in total margin were observed versus fixed margins of 5 or 10 mm. Increasing the number of isocenters for a given patient reduced the median total margin volume by 31.0% for N = 2 versus N = 1 and 15.7% for N = 3 versus N = 2, but diminishing returns were observed as additional isocenters were added (N = 4 to N = 6: -6.6%, -5.4%, and -3.7%, respectively). Median run time was 0.19 s for k-means versus 306.9 s for the reference method.
K-means clustering offers an efficient method for selecting the number and locations of isocenters to reduce the total margin volume in multi-target extracranial SABR.
Authors
Slagowski Slagowski, Davies Davies, Bayliss Bayliss, Bennett Bennett, Basree Basree, Bassetti Bassetti, Glide-Hurst Glide-Hurst
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