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Determination of the Optimal Fall-Off Value for the NTO in Centrally Located Lung Masses and Prostate Patients

M Gagne*, S James, C Huh, A Al-Basheer, Augusta University, Augusta, GA

Presentations

(Sunday, 7/12/2020)   [Eastern Time (GMT-4)]

Room: AAPM ePoster Library

Purpose: the Manual Normal Tissue Objective (NTO) generally gives better results than when using the Automatic NTO at our clinic. This project is designed to find the optimal Fall-off value for the NTO for centrally located lung masses using a SBRT technique and for prostate plans using VMAT.


Methods: Using a computational human phantom series provided by the NCI and Photon Optimizer (PO version 15.6.05), plans containing different Fall-offs in the NTO were calculated in Eclipse (AAA version 15.6.05). For the prostate plans, a lower and upper objective was set at 100% and 105% of the prescription dose in the PO, respectively. For the lung plans, only a lower objective of 100% was used. Each NTO had a Start Dose of 105%, an End Dose of 40%, and a Distance from Target Border objective set at 0.20 cm. Fall-off values varied from 0.1 up to 10. All NTO and PTV objectives used a priority of 150. Percentage decrease in dose from 0.2 to 2.0 cm, maximum dose, and PTV coverage were evaluated to determine the optimal Fall-off.


Results: the prostate plans, PTV coverage decreased with increasing Fall-off, but was above 97.2% for all plans. The maximum dose was never greater than 107.2%. The greatest decrease in dose occurred with a Fall-off of 2.0, 0.6, and 0.4, respectively.
For the lung plans, PTV coverage stayed above 98.7% for every Fall-off. A maximum dose greater than 125% occurred when the Fall-off was 0.2 and below or 0.9 and above. Using Fall-off values of 0.4 and 0.5 resulted in the greatest decrease in dose while keeping the hotspot below 125%.



Conclusion: For our clinic's guidelines, in order to maintain optimal coverage, with an acceptable hot spot and large dose falloff, a NTO Fall-off value between 0.4 and 0.6 should be used.

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Keywords

Optimization, Dosimetry

Taxonomy

Not Applicable / None Entered.

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