Purpose: Automatic treatment planning can be used to determine achievable dose values before optimization. Reverse auto planning instead looks to find the highest target dose while still meeting critical structure objectives. A tool incorporating reverse auto planning is proposed for prediction of planning difficulty. Methods: A SQL database of 53 pancreas stereotactic body radiotherapy patients is populated with dose and structure information. Overlap volume histograms (OVH) are generated for each organ at risk (OAR) and target pair. For each structure, the lowest achievable target dose, which was calculated by selecting the lowest dose from all patients with a smaller distance to overlap, was queried together with maximum target dose. The lowest target dose from the list of maximum target dose per structure represents the highest achieved dose from the database population. Additional scaling can be used which scales the maximum achievable target dose per structure to the limiting dose of the OAR. Results: For patients analyzed using this tool, the predicted maximum target dose serves as a predictor of planning difficulty. If the predicted maximum target dose is high, plans which meet OAR objectives are easier to generate. If the predicted maximum target dose is low, plans are more difficult and planning objectives may not be achievable. Conclusion: A tool for predicting the maximum achievable target dose is developed. This tool estimates plan difficulty and achievability prior to planning and can be used to determine if dose escalation may be appropriate or if alternate treatment strategies should be used due to difficulty in achieving desired goals. Support by Philips Healthcare: Philips Radiation Oncology Services.
ASJC Scopus subject areas
- Radiology Nuclear Medicine and imaging