AAPM2015 - Statistical Learning to Predict MLC Errors
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Transcript of AAPM2015 - Statistical Learning to Predict MLC Errors
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A Statistical Learning Approach to the Accurate Predictionof MLC Errors During VMAT DeliveryJoel Carlson
Jong Min Park
So-Yeon Park
Jong In Park
Yunseok Choi
Sung-Joon Ye
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MLCs move in complex ways
Prostate Plan: Low complexity H&N plan: High complexity
** ~50x speed **
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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?
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How can we quantify these errors?
How can we predict these errors?
How do these errors impact dose delivery accuracy?
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Quantifying the difference between planning and delivery74 H&N or Prostate VMAT plans from 3 institutions
Dicom RT
Planned Positions
DYNALOG
Delivered Positions
Errors(Prediction Target)
Difference
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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
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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!
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Using a validation set we chose the best features
All Features
Build Model
Vary Features
Error* Minimized?
Predictions
NoFinal Model
Yes
Predictions
Report Statistics
Training Plans
(N = 3)Validation Plans
(N = 6)
Testing Plans
(N = 65)
*Root Mean Squared Error
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Results:
Cubist (Decision Tree)
Best performing algorithm
Best performing feature set:
Velocity, Position, Direction, Movement Category, Bank
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Results: The errors are well predicted by the machine learning algorithms
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Results: Visualizing the movement of a single MLC leaf
~3.5mm
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Results: Visualizing the movement of a single MLC leaf
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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
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Calculate the gamma pass rates
DeliveredPlanned
Predicted Delivered
Eclipse Trilogy + MapCheck2
?
?
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Passing rates are improved by using predicted positions
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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
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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
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A Statistical Learning Approach to the Accurate Predictionof MLC Errors During VMAT DeliveryJoel Carlson
Jong Min Park
So-Yeon Park
Jong In Park
Yunseok Choi
Sung-Joon Ye
Thank you for listening!
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Slides Answering Potential Questions
The following slides serve as supplemental material for answering audience questions
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Planned
Predicted
SMG (R)
Parotid (L) Parotid (R)
PTV_67.5
PTV_54
SMG (L)
PTV_48
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• 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
• Momentum
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
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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.”
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