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Managing uncertainty using robust optimization. Timothy Chan University of Toronto BIRS radiation therapy workshop March 12, 2011. Overview. Uncertainty in radiation therapy Methods to manage uncertainty Robust optimization Areas for further research.

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Managing uncertainty using robust optimization

Managing uncertainty using robust optimization

Timothy Chan

University of Toronto

BIRS radiation therapy workshop

March 12, 2011


Overview
Overview

  • Uncertainty in radiation therapy

  • Methods to manage uncertainty

    • Robust optimization

  • Areas for further research


Top 10 health technology hazards for 2011 by erci institute
“Top 10 Health Technology Hazards for 2011” by ERCI Institute

1. Radiation overdose and other dose errors during radiation therapy*

2. Alarm hazards

3. Cross-contamination from flexible endoscopes

4. The high radiation dose of CT scans

5. Data loss, system incompatibilities, and other health IT complications

6. Luer misconnections

7. Oversedation during use of PCA infusion pumps

8. Needlesticks and other sharps injuries

9. Surgical fires

10. Defibrillator failures in emergency resuscitation attempts

* Not on the 2010 Top 10


New york times articles
New York Times articles Institute

  • Series of NYT articles in January 2010

    • “Radiation offers new cures, and ways to do harm,” Jan. 23, 2010

    • “Case studies: when medical radiation goes awry,” Jan. 26, 2010

    • “As technology surges, radiation safeguards lag,” Jan. 26, 2010

  • Most issues cited were human errors, but they do mention software/programming flaws, missing part of the target

  • Implicit discussions of setup errors, dose calculation errors, imaging error


Aimms robust optimization solver
AIMMS robust optimization solver Institute

  • From a March 2009 press release by AIMMS:

  • “…agreement to develop Robust Optimization support for AIMMS.”

  • “Potential areas of application for Robust Optimization are…”

    • Medicine (e.g., Intensive Modulated Radiation Therapy)


Types of uncertainty
Types of uncertainty Institute

  • Imaging

  • Contouring

  • Dose calculation

  • Set-up

  • Motion

    • Organ position

    • Breathing motion

  • Delivery

  • Modality-specific uncertainties

    • Range uncertainty in proton therapy


Methods to address uncertainty
Methods to address uncertainty Institute

PTV

  • Margins

    • Batman’s utility belt

    • Microscopic growth (CTV)

    • Intrafraction motion (ITV)

    • Set-up errors (PTV)

  • Image-guidance

    • Acquire new images online/offline

    • Adjust patient positioning

    • Create new treatment plan

ITV

CTV

GTV


Robust optimization
Robust optimization Institute

  • Somewhere in between using a fixed margin and acquiring new data constantly

  • Robust optimization approach:

    • Create a model of the uncertain effect (e.g., breathing motion)

    • Incorporate knowledge of uncertainty into the optimization process (as opposed to measuring sensitivity to uncertainty post-optimization)

    • Robust treatments should be de-sensitized to uncertainty

      • E.g., resulting dose distributions may be more homogeneous

  • For discussion purposes, will review selected contributions in IMRT and IMPT

    • Won’t be able to do justice to everybody, especially many contributions from medical physics community


Chu zinchenko henderson sharpe 2005
Chu, Zinchenko, InstituteHenderson, Sharpe (2005)

  • Application area/site: Prostate

  • Uncertainty: Setup (interfraction position errors in general)

  • Optimization problem: Minimize overdose/underdose penalties subject to approximate DV-constraints and an ellipsoidal model of data uncertainty (SOCP formulation)

  • Result: Robust

    treatment delivered

    comparable CTV

    coverage with reduced

    healthy tissue dose

    over multiple

    simulated scenarios


Olafsson and wright 2006
Olafsson and Wright (2006) Institute

  • Application area/site: Nasopharynx

  • Uncertainty: Dose calculation and interfraction position errors

  • Optimization problem: Minimize overdose/underdose penalties subject to tumor dose bounds and an ellipsoidal model of data uncertainty (SOCP formulation)

    • Due to structure, solvable as a sequence of linear programs

  • Result: Better tumor coverage vs. nominal (non-robust) plan; lower healthy tissue dose vs. margin plan


Nohadani et al 2009
Nohadani et al. (2009) Institute

  • Application area/site: Lung

  • Uncertainty: Dose calculation (pencil beam vs. MC)

  • Optimization problem: Minimizing expectation of quadratic penalties – probabilistic approach

  • Result: Robust solution using fast, inaccurate pencil beam dose calculations has comparable dosimetric characteristics as one from Monte Carlo dose calculations


Chan bortfeld tsitsiklis 2006 bortfeld et al 2008
Chan, Bortfeld, Tsitsiklis (2006); InstituteBortfeld et al. (2008)

  • Application area/site:Lung

  • Uncertainty: Irregular breathing motion (intrafraction)

  • Optimization problem: Minimize dose delivered subject to tumor coverage and polyhedral model of data uncertainty (LP)

  • Result:Better tumor

    coverage vs. nominal

    (non-robust) plan;

    lower healthy tissue

    dose vs. margin plan

Nominal

Robust

Margin


Unkelbach chan bortfeld 2007 unkelbach et al 2009
Unkelbach, Chan, Bortfeld (2007); Unkelbach et al. (2009) Institute

  • Application area/site: Paraspinal

  • Uncertainty: Range and setup errors

  • Optimization problem: Minimize expected quadratic penalties; minimize absolute worst case penalties

  • Result: Robust plans cover target reliably over multiple uncertain scenarios

    Nominal (overshoot) Robust (overshoot)


Fredriksson forsgren hardemark 2011
Fredriksson, Forsgren, Hardemark (2011) Institute

  • Application area/site: Lung, paraspinal, prostate

  • Uncertainty: Range and setup errors

  • Optimization problem: Minimax stochastic program with quadratic penalties and range of possible values for probabilities (convex QP)

  • Result: Balanced trade-off in tumor coverage and healthy tissue sparing between nominal (non-robust) and margin approaches


New horizons for robust planning
New horizons for robust planning? Institute

  • Other cancer sites/modalities

  • Improved clinical acceptance

    • Get robust planning into commercial TPS

    • More experimental research to measure delivery of robust treatments (e.g., Vrancic 2009)


New horizons for robust planning1
New horizons for robust planning? Institute

  • Better models of uncertainty

    • Improved or more frequent imaging may allow us to create better, more dynamic models of uncertainty

      • Cervical cancer: significant shrinkage possible in short time frame

  • Adaptation

    • Adaptive radiation therapy largely remains separate from robust methods

    • Combine multi-stage robust methods with adaptive RT (e.g., AARC with infrequent uncertainty set updates)


Overview of adaptive robust optimization in lung
Overview of adaptive robust optimization in lung Institute

  • RO method uses uncertainty set of breathing motion PDFs to create treatments de-sensitized to irregular breathing motion

  • “Static” robust optimization method used one uncertainty set throughout the fractionated treatment

  • With newly acquired PDFs, uncertainty set can be updated and treatment can be re-optimized

  • Updating algorithms

    • Exponential smoothing

    • Running average

    • Dirichlet distribution-based


Treatment planning timeline
Treatment planning timeline Institute

Treatment planning

Treatment delivery

Day 1

Day 2

Acquire images

Optimize treatment

Deliver treatment

Deliver treatment

Traditional

Robust

Create uncertainty set

Acquire PDF data

Acquire PDF data

Update uncertainty set

Re-optimize

Update uncertainty set

Re-optimize

Adaptive




Conclusions
Conclusions Institute

  • Much activity in robust RT methods over last ~five years

  • Future directions

    • Clinical-clinical

      • Get in TPS

      • Clinical trials

    • Clinical-methodological

      • Applications to other sites

      • Proton therapy

      • Arc therapy

    • Methodological

      • Better models of uncertainty

      • Adaptive-robust



Pflugfelder wilkens oelfke 2008
Pflugfelder, Wilkens, Oelfke (2008) Institute

  • Application area/site: Paraspinal

  • Uncertainty: Range and setup errors

  • Optimization problem: Quadratic penalty functions – probabilistic approach

  • Robust optimization problem: Quadratic penalty functions

  • Result:


Sequence of pdfs
Sequence of PDFs Institute


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