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A Method to Evaluate Dosimetric Effects On Organs-At-Risk for Treatment Delivery Systematic Uncertainties

S Liu

S Liu1*, T Mazur1 , Y Fu1 , H Li1 , S Mutic1 , D Yang1 , (1) Washington University in St. Louis, St. Louis, MO


TH-CD-205-11 (Thursday, August 3, 2017) 10:00 AM - 12:00 PM Room: 205

Purpose: To provide a practical method to quantify the dosimetric effects on organs-at-risk (OARs) due to systematic uncertainties in linear accelerator treatment delivery in order to aid treatment planning and raise warnings about additional risks to critical OARs.

Methods: A dose approximation method, based on geometrical transformations, was developed to automatically estimate perturbations to dose volumes arising from five important potential uncertainties at the treatment deliveries - systematic isocenter misalignment between image guidance and beam delivery systems, and systematic errors in collimator, gantry, couch table and multi-leaf collimator (MLC) leaf bank positions. The agreement between the estimated dose using the dose approximation method and the recalculated dose by TPS was verified using a dose difference test (2%, 0 mm Gamma analysis). For each uncertainty, the worst-case maximal doses to the critical OARs (brainstem, chiasm, optic nerves and spinal cord) were quantitatively evaluated, and compared with the maximal doses from clinical plans.

Results: Six brain and six spine IMRT plans were used for evaluation. The average passing rates of 2% dose difference were 98.9%±1.3% for all the uncertainties. The average computational time per patient is 5.8 seconds. The worst-case scenarios for each plan, i.e. the largest changes in maximal doses to OARs, were identified and confirmed to be in agreement with those calculated using the TPS.

Conclusion: For a given external beam treatment plan, the proposed dose approximation method allows efficient evaluation of the dosimetric effects of potential patient positioning uncertainties and systematic machine delivery errors on maximal dose to critical OARs. While the same uncertainties can be manually analyzed using TPS, the proposed method is automatic and computationally inexpensive, and therefore significantly more practical. It could be useful to provide insights about otherwise unquantified risks and plan robustness during the stage of treatment planning.

Funding Support, Disclosures, and Conflict of Interest: Funding: AHRQ R01-HS022888 No conflict of interest Disclosures: Authors have technology licensing fee from Viewray

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