Building Treatment Planning
Knowledge Base In RT
Jackie Wu, PhD FAAPM
Professor
Disclosure

Research Grant: NIH/NCI

Master Research Grant: Varian Medical Systems

License Agreement: Varian Medical Systems

Speaker Agreement: Varian Medical Systems
Duke University Medical Center
Department of Radiation Oncology
DUKE University Radiation Oncology
DUKE University Radiation Oncology
Building Planning Knowledge Base for RT

Goal:

Model Best Treatment Option(s) for A Patient Based on His/Her
Unique Anatomy, Medical Condition and Consensus Data

Target and OAR delineation

Dose prescription/fractionation

Treatment plan/parameter design

Treatment plan evaluation/selection

Outcome and plan dosimetry
DUKE University Radiation Oncology
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Building Planning Knowledge Base for RT
Building Planning Knowledge Base:
All Component of Tx. Plan Design


Planning Knowledge Sources

Past data





Guidelines, clinical trials, peer reviewed papers, normal tissue
complication models
Experience

Planning Knowledge Sources
Plans, quality assessment, patient anatomy, machine
characteristics, calibration, treatment outcomes
Formalized knowledge


Quantitative vs. qualitative

Volume, overlaps, etc

Guidelines, outcome data, etc
Continuous vs. discrete


Beam angles, Tradeoffs, etc
Numerical vs. Descriptive
Cord
Distance
to OAR (DOH): PCS

OAR
PTV

PTV volume

Fraction
of OAR volume overlapping
PTV (overlap volume)
Case with
2
Spinal

volumes
Case 2
Case 1
Cord
Fraction
of OAR volume outside the treatment fields (out-of-field
volume)
PTV

Tightness of the geometric enclosure of PTV surrounding OAR

Curvature of specific OAR
Percent Dose (%)
In the head of physicians, physicists, dosimetrists, therapists
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Distance
to target histogram (DTH):
Case 1PCS
1
Spinal Cord and PTV DVHs
Case
Spinal

Percent Volume (%)
Building Planning Knowledge Base for RT
DUKE University Radiation Oncology
DUKE University Radiation Oncology
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Building Planning Knowledge Base:
All Component of Tx. Plan Design
Building Planning Knowledge Base:
All Component of Tx. Plan Design
Rectum
_
_
HN Patient and dose
prescription
Model
Predict parotid D50
by “standard model”
Plan
N
Y
Meet Tradeoff
Criteria?
Predict parotid DVHs
by “standard model”
DUKE University Radiation Oncology
Building Planning Knowledge Base:
All Component of Tx. Plan Design
R
Which side has
lower D50?
Predict R parotid DVH
model”
DUKE University Radiation
Oncology
by “combined
L
Predict L parotid DVH
by “combined model”
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Building Planning Knowledge Base:
All Component of Tx. Plan Design
DUKE University Radiation Oncology
Building Planning Knowledge Base:
All Component of Tx. Plan Design
DUKE University Radiation Oncology
Building Planning Knowledge Base:
All Component of Tx. Plan Design
DUKE University Radiation Oncology
Courtesy Of Y. Ge @UNCC
4
Building Planning Knowledge Base:
Integrated Decision Support Tool
Prediction
Building Planning Knowledge Base:
Multi-institution, Multi-modality Model
Building Planning Knowledge Base:
Multi-institution, Multi-modality Model
Guidelines
Parotid DVH
Institution 2 Not contributing to model
Gui
Contralateral
Lung DVH
_
_
Plan
vs. Tomotherapy Model
vs. IMRT Model
Model
Plan
Institution 3 Contributing to model
DUKE University Radiation Oncology
Courtesy Of Y. Ge @UNCC
DUKE University Radiation Oncology
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Building Planning Knowledge Base:
Platform Design
Knowledge
Base
Training
Data
base
of
High
Data
base
of
DataDatabase
base
ofHigh
High
of
quality
treatment
quality
treatment
quality
treatment
Expert
plan
plan
plan
Knowledge
&
Treatment Cases
Patient & Plan
Design
Features
Building Planning Knowledge Base:
Platform Design
Dose
Parameter
Features
Output
Features
Yi
Input Features
Xi
Machine
Learning
Knowledge Base
Y1= F1(X)
Y2=F2(X)
Knowledge Base Application
Feature
New
Characterization
DUKE
University Radiation Oncology
Patient
Xnew
Knowledge Base Application
Y=f(X)
Dose Parameter
Features
Ynew
Feature
New
Characterization
DUKE
University Radiation Oncology
Patient
Xnew
Y=f(X)
Dose Parameter
Features
Ynew
Acknowledgement

Yaorong Ge, PhD

FangFang Yin, PhD

Lulin Yuan, PhD

Jianfei Li, PhD

Ying Xiao, PhD

Jun Lian, PhD

Taoran Li, PhD

Simin Lu, MS

Yang Sheng, MS
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References

AAPM2014: SU-F-BRD-9:
Bouquet Templates

AAPM2014: TU-C-17A-11: Lu et al, Progressive Knowledge Modeling for Pelvic
IMRT/VMAT Treatment Planning
Yuan et al, Lung IMRT Planning Using Standardized Beam

AAPM2014: TH-A-9A-1:
Liu et al, Active Optical Flow Model: Predicting Voxel-Level
Dose Prediction in Spine SBRT

AAPM 2014: SU-E-T-527:
Treatment Planning
Lian et al, Prior Knowledge Guided TomoTherapy

Lian et al, Modeling the dosimetry of organ-at-risk in head and neck IMRT planning:
An intertechnique and interinstitutional study, Medical Physics 2013, 40(12)

Yuan et al, Incorporating single-side sparing in models for predicting parotid dose
sparing in head and neck IMRT, Medical Physics 2014, 41(2)

Liu et al, An Active Optical Flow Model for Dose Prediction in Spinal SBRT Plans,
MICCAI Workshop on Computational Methods and Clinical Applications for Spine
Imaging 2014

Zhu et al, A planning quality evaluation tool for prostate adaptive IMRT based on
THANK YOU!
machine learning, 2011, 38(2)

Yuan et al, Quantitative analysis of the factors which affect the inter-patient
DUKE University Radiation Oncology
organ-at-risk
dose sparing variation in IMRT plans, Medical Physics 2012, 39(11)
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