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Session: Enhancing a Physicist's Role in Radiation Therapy Treatment Plan Assessment [Return to Session]

Enhancing a Physicist's Role in Radiation Therapy Treatment Plan Assessment

D Jacqmin1*, M Kim2*, S Parker3*, B Juneja4*, G Kim5*, L Rankine6*, (1) University of Wisconsin-Madison, Madison, WI, (2) University of Washington, Seattle, WA, (3) Wake Forest Baptist Health High Point Medical Center, Winston-Salem, NC, (4) MD Anderson Cancer Center at Cooper, Media, PA, (5) UC San Diego, La Jolla, CA, (6) The University of North Carolina at Chapel Hill, Chapel Hill, NC


WE-FG-BRA-0 (Wednesday, 7/13/2022) 1:45 PM - 3:45 PM [Eastern Time (GMT-4)]

Ballroom A

Treatment planning has evolved with advances in technology over the past few decades. In many clinics, keeping up with this rapid evolution has been challenging and little time has been available to re-examine the planning process as a whole. Areas for further examination include the role of the physicist in plan quality assessment and the integration of quality checks into the treatment planning process.

As the complexity of radiotherapy treatment planning increases, the role of the physicist in the assessment of treatment plan quality becomes more critical. This session aims to define what high quality means in radiotherapy treatment planning and the role of the physicist in ensuring high quality treatment plans for all patients. The session will provide suggestions for the review of treatment plan quality for clinical physicists, as well as real life examples of “acceptable” and “high quality” treatment plans. Recent advancements in data-driven plan quality control and scripting will be discussed to provide physicists with up-to-date information on how to effectively use these tools for the assessment of treatment plan quality.

In addition to examining the physicist’s role in treatment planning, the integration of quality assurance steps in the planning process warrants further consideration. Most treatment planning quality management tasks occur well after the treatment plan has been completed, approved by the physician, and exported to the Oncology Information System (OIS). Errors detected during physics or therapist plan checks lead to re-work and additional effort that can affect multiple members of the radiation oncology team, including dosimetrists, physicists, physicians, radiation therapists, and patients. In the treatment planning process, re-work decreases efficiency and may increase delayed treatments. If some of the checks are placed upstream in the planning process, then some re-work and wasted effort can be avoided. This session will explain the benefits of moving checks upstream in the planning process and provide examples of applying these concepts in clinics using commercially available treatment planning systems.

Learning objectives:

1. To define quality in radiotherapy treatment planning and understand the difference between “acceptable” and “high quality” treatment plans

2. To understand the role of a physicist in determining the quality of a radiotherapy treatment plan and the impact it has on patient treatments

3. Discuss how to evaluate the technical aspects that affect plan quality, such as beam configuration, optimization strategy and plan modulation

4. Describe how to approach clinical aspects of plan quality review, including evaluation of the isodose distribution, target coverage and normal tissue doses with respect to clinical treatment objectives and tissue tolerances.

5. To review examples of acceptable quality plans from real clinical cases in order to help participants identify similar quality issues within their own institutions

6. To summarize and highlight the key aspects of clinical implementation of the current automation/data-driven plan quality control tools

7. Gain insight into the benefits of moving checks upstream in the treatment planning process

8. Learn how institutions are incorporating upstream checks into treatment planning workflow



Treatment Planning, Quality Assurance


TH- External Beam- Photons: Treatment planning using machine learning/Knowledge Based Planning/automation

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