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Successes and Challenges Associated with Monte Carlo Treatment Planning in the Clinic

I Chetty

J Cygler

I. J. Chetty1*, J Cygler2*, (1) Henry Ford Health System, Detroit, MI, (2) The Ottawa Hospital Regional Cancer Ctr., Ottawa, ON

MO-B-105-1 Monday 9:00AM - 9:55AM Room: 105

The availability of Monte Carlo (MC)-based codes optimized for photon and electron beams in patient-specific geometries has enabled the use of MC-based dose calculations for radiotherapy treatment planning in the routine clinical setting. These codes have made it possible to perform MC-based photon and electron beam dose calculations within minutes on typical treatment planning computational platforms. As efficient and accurate MC codes become more widely utilized in the clinic, it is important that strategies and paradigms for clinical commissioning and implementation of these systems be formulated and discussed. This lecture will focus on such strategies, challenges and successes associated with the use of MC-based dose calculations for external photon and electron beam therapy in the clinic.

Learning Objectives:
1. To provide an educational review of the physics of the MC method including discussion of the approaches used for coupled photon and electron transport.
2. To discuss currently available MC-based photon and electron algorithms fast enough for clinical implementation.
3. To describe the development of beam models for photon and electron beam treatment planning.
4. To discuss the factors associated with MC dose calculation within the patient-specific geometry, such as statistical uncertainties, CT-number to material density assignments, and reporting of dose-to-medium versus dose-to-water.
5. To review paradigms and approaches for commissioning and experimental verification of MC-based photon and electron beam dose calculation algorithms.
6. To summarize the approaches being used in evaluating the clinical impact of MC-based photon dose calculations and the associated issues.

Funding Support, Disclosures, and Conflict of Interest: NIH/NCI R01 CA106770 Varian Medical Systems, Palo Alto, CA

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