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
MLCs move in complex ways
Prostate Plan: Low complexity H&N plan: High complexity
** ~50x speed **
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?
How can we quantify these errors?
How can we predict these errors?
How do these errors impact dose delivery accuracy?
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
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
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!
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
Results:
Cubist (Decision Tree)
Best performing algorithm
Best performing feature set:
Velocity, Position, Direction, Movement Category, Bank
Results: The errors are well predicted by the machine learning algorithms
Results: Visualizing the movement of a single MLC leaf
~3.5mm
Results: Visualizing the movement of a single MLC leaf
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
Calculate the gamma pass rates
DeliveredPlanned
Predicted Delivered
Eclipse Trilogy + MapCheck2
?
?
Passing rates are improved by using predicted positions
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
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
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!
Slides Answering Potential Questions
The following slides serve as supplemental material for answering audience questions
Planned
Predicted
SMG (R)
Parotid (L) Parotid (R)
PTV_67.5
PTV_54
SMG (L)
PTV_48
• 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
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.”