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AAPM2015 - Statistical Learning to Predict MLC Errors
- 1. A Statistical Learning Approach to the Accurate Prediction of MLC Errors During VMAT Delivery Joel Carlson Jong Min Park So-Yeon Park Jong In Park Yunseok Choi Sung-Joon Ye
- 2. MLCs move in complex ways Prostate Plan: Low complexity H&N plan: High complexity ** ~50x speed **
- 3. Complex movements lead to errors in leaf positions How can we quantify these errors? Planned Delivered Goal: Create a more realistic representation in the TPS of where the MLC leaves will be upon delivery How can we predict these errors? How do these errors impact dose delivery accuracy?
- 4. How can we quantify these errors? How can we predict these errors? How do these errors impact dose delivery accuracy?
- 5. Quantifying the difference between planning and delivery 74 H&N or Prostate VMAT plans from 3 institutions Dicom RT Planned Positions DYNALOG Delivered Positions Errors (Prediction Target) Difference
- 6. How can we quantify these errors? How can we predict these errors? How do these errors impact dose delivery accuracy? Goal: Create a more realistic representation in the TPS of where the MLC leaves will be upon delivery
- 7. We first extracted a rich feature set from the DICOM-RT plan files Using only information available before plan delivery ~150 features* quantifying the MLC leaf motion *list available, just ask!
- 8. Using a validation set we chose the best features All Features Build Model Vary Features Error* Minimized? Predictions No Final Model Yes Predictions Report Statistics Training Plans (N = 3) Validation Plans (N = 6) Testing Plans (N = 65) *Root Mean Squared Error
- 9. Results: Cubist (Decision Tree) Best performing algorithm Best performing feature set: Velocity, Position, Direction, Movement Category, Bank
- 10. Results: The errors are well predicted by the machine learning algorithms
- 11. Results: Visualizing the movement of a single MLC leaf ~3.5mm
- 12. Results: Visualizing the movement of a single MLC leaf
- 13. How can we quantify these errors? How can we predict these errors? How do these errors impact dose delivery accuracy? Goal: Create a more realistic representation in the TPS of where the MLC leaves will be upon delivery
- 14. Calculate the gamma pass rates DeliveredPlanned Predicted Delivered Eclipse Trilogy + MapCheck2 ? ?
- 15. Passing rates are improved by using predicted positions
- 16. There exist errors between planned and delivered MLC positions These errors are predictable at the planning stage Utilizing predicted positions: • Increases gamma passing rates • Leads to a more realistic representation of where the leaves will be upon delivery In conclusion
- 17. Explore differences in patient DVHs • In progress Integrate predictions into TPS • Will give planners a better view of what will be delivered Publish fully reproducible code and data Future work
- 18. A Statistical Learning Approach to the Accurate Prediction of MLC Errors During VMAT Delivery Joel Carlson Jong Min Park So-Yeon Park Jong In Park Yunseok Choi Sung-Joon Ye Thank you for listening!
- 19. Slides Answering Potential Questions The following slides serve as supplemental material for answering audience questions
- 20. Planned Predicted SMG (R) Parotid (L) Parotid (R) PTV_67.5 PTV_54 SMG (L) PTV_48
- 21. • Numerical Values: • Error Magnitude • MLC Index • Width and Mass of leaf • Positions • ±5 CPs • ±5 CPs of both adjacent MLCs • Velocities • ±5 CPs • ±5 CPs of both adjacent MLCs • Accelerations • ±5 CPs • ±5 CPs of both adjacent MLCs All Features • Categorical • Whether the MLC was previously at rest, coming to a stop, moving before and after, single CP movement • Whether adjacent MLCs were both moving in the same direction, both opposite, same/opposite, or at rest • Moving towards (push) or away (pull) from the isocenter • The CP at which the error occurred
- 22. Cubist • “…is a rule-based model where a tree is grown, and each of the terminal leaves contain regression models. These models are based on the predictors in previous splits.”
Editor's Notes
- Today, I’m going to tell you why your TPS is not accurately telling you the dose your linac is physically capable of delivering, and how we can start taking steps towards fixing that problem
- In VMAT plans we modulate several parameters. One of these being MLC movements. Plans for different sites have mlc movements of varying complexity. As we can see Particularly in complex plans MLC errors though to be dominant error causing mechanism
- Therefore, one statement that we can make is that. This raises several questions
- Position, Velocity, Momentum, Acceleration, Direction of movement, At rest/Moving, Previously at rest, Stopping, Mass, Width, Direction of adjacent leaves, Velocity of adjacent leaves, Acceleration of adjacent leaves, etc…
- Candidate for deletion
- Inst 2: Larger proportion of prostate plans…even moving MLC leaves are moving slower? Approx. > half of CPs the leaf is moving in HN plans Approx 25% moving in the course of prostate plan
- ~6 mins
- From institution 1 only 3%/3mm : 1.75% 2%/2mm: 3.3% 1%/2mm: 3.5% All significant at the, p < 0.05