Medical physics · 2026
Simulation-free spine palliative radiotherapy enabled by AI-adapted diagnostic CT.
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Han Yiding, Hanania Alexander Nicola, Siddiqui Zaid Ali, Ugarte Vincent, Zhou Boran, Mohamed Abdallah S R, Pathak Piyush, Hamstra Daniel Allen, Sun Baozhou
Аннотация
BACKGROUND: Radiotherapy planning traditionally requires a dedicated simulation CT (sCT), which can introduce delays in initiating treatment. This is particularly impactful in spinal palliative care, where timely treatment is often important for symptom control and prevention of neurological deterioration. Although diagnostic CT (dCT) is frequently available earlier in the workflow, it can lead to geometric and dosimetric inaccuracies when used directly for treatment planning due to discrepancies in patient positioning, vertebral alignment, and table curvature.
PURPOSE: To develop and evaluate an AI-based method that transforms dCT into a simulation-equivalent planning CT (AI-pCT), enabling a clinically feasible simulation-free workflow for spinal palliative radiotherapy.
METHODS: Two neural networks were trained to correct spine position and body contour using paired dCT-sCT images from 50 patients (42 train/validation, 8 internal tests) in a safety net hospital and externally evaluated on 7 additional academic medical center (AMC) patients. After rigid bone-based alignment to sCT, dosimetric accuracy was assessed by comparing DVH endpoints (Dmean, Dmax, D95, D99, V100, V107) and DVH Root-Mean-Square (RMS) error for plans recalculated on dCT versus AI-pCT versus sCT. Four radiation oncologists scored image suitability. Significance was evaluated using the Wilcoxon signed-rank test.
RESULTS: In the safety net cohort, AI-pCT substantially reduced geometric and dosimetric error relative to dCT (e.g., Dmean error 2.0%→0.57%; RMS DVH error 6.4%→2.2%, all p < 0.05), improved physician plan-quality ratings from "Acceptable" to "Good-Perfect," and increased plan-level clinical goal achievement from 37.5% to 100%. In the AMC cohort, where baseline dCT was already closely aligned to sCT, AI-pCT produced smaller but still statistically significant gains.
CONCLUSION: AI-pCT achieves sCT-level geometric and dosimetric fidelity without requiring a separate simulation scan, enabling a simulation-free planning workflow for spinal palliative RT. This approach has the potential to reduce treatment delays and improve access, particularly in resource-constrained environments.
PURPOSE: To develop and evaluate an AI-based method that transforms dCT into a simulation-equivalent planning CT (AI-pCT), enabling a clinically feasible simulation-free workflow for spinal palliative radiotherapy.
METHODS: Two neural networks were trained to correct spine position and body contour using paired dCT-sCT images from 50 patients (42 train/validation, 8 internal tests) in a safety net hospital and externally evaluated on 7 additional academic medical center (AMC) patients. After rigid bone-based alignment to sCT, dosimetric accuracy was assessed by comparing DVH endpoints (Dmean, Dmax, D95, D99, V100, V107) and DVH Root-Mean-Square (RMS) error for plans recalculated on dCT versus AI-pCT versus sCT. Four radiation oncologists scored image suitability. Significance was evaluated using the Wilcoxon signed-rank test.
RESULTS: In the safety net cohort, AI-pCT substantially reduced geometric and dosimetric error relative to dCT (e.g., Dmean error 2.0%→0.57%; RMS DVH error 6.4%→2.2%, all p < 0.05), improved physician plan-quality ratings from "Acceptable" to "Good-Perfect," and increased plan-level clinical goal achievement from 37.5% to 100%. In the AMC cohort, where baseline dCT was already closely aligned to sCT, AI-pCT produced smaller but still statistically significant gains.
CONCLUSION: AI-pCT achieves sCT-level geometric and dosimetric fidelity without requiring a separate simulation scan, enabling a simulation-free planning workflow for spinal palliative RT. This approach has the potential to reduce treatment delays and improve access, particularly in resource-constrained environments.