7/22/2014
Database sharing model for research and
decision support in radiation therapy
Todd McNutt, Scott Robertson, Sierra Cheng, Joseph Moore, Harry
Quon, Joseph Herman, Michael Bowers, John Wong, Theodore
DeWeese
Disclosure:
Funding from Elekta, Philips and Toshiba
Personalized care using
database of prior patients
How to best to
treat individual
patient?
Radiotherapy
Surgery
Chemotherapy
Pathology
CONSULT
Performance
Status
Clinical
assessments
TREATMENT
FOLLOW-UP
Diagnosis
Prediction of
complications for
early
intervention?
Lab Values
Toxicities
Disease
Comorbidities
Quality Status
Patient
Survival of Life
History
Predicted
response and
toxicity?
Project components
• Integration of data collection with clinical
workflow
– “Big Data” requires meaningful data
• Database design, security and distributed webaccess
• Tools for query, analysis, navigation and decision
support
– Sample Questions and Uses
• Toxicity Trending
• DVH vs Toxicity
• Automatic Treatment Planning and Quality
• Prophylactic PEG use
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Treatment Timeline
Consult
Weekly On
Treatment
Demographics
Diagnosis
Staging
Baseline Tox
Baseline QoL
History
Toxicity
QoL
Patient status
Symptom Mgmt
Simulation
Planning
Targets
OARs
OVH
Rx
Dose
DVH
End of
Treatment
Follow Up
Late toxicity
QoL
Patient status
Disease response
Acute toxicity
QoL
Patient status
Symptom mgmt
Disease response
Image
Guidance
Motion
Disease
Response
Chart
Review
Data Collection in Clinic
Clinical Assessment
Quality of life
Disease Status
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Chart Review (~40 per hour)
Highlight when
data missing
Identify abnormal
prescriptions
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Extract, Transform, Load
- SQL Query
- Lab, Toxicity,
Assessments
MOSAIQ
Oncospace
- Scripts, Python, DICOM
- DVH, OVH, Shapes
Pinnacle TPS
&
DICOM RT PACS
Oncospace tables and schema
Private Health Info
(access restricted)
Patient
Family
History
Social
History
Medical
History
Surgical
Procedures
Radiation
Summary
Tumors
Image
Feature
Medications
(chemo)
Pathology
Feature
Organ
DVH Data
Test Results
(Labs)
Assessments
(Toxicities)
Patient Representations
(CT based geometries)
Organ Dose
Summaries
Organ DVH
Feature
1 : N multiple instances
1 : 1 single instance
m : n relates m to n
Radiotherapy
Sessions
(m:n)
Clinical
Events
Image
Transform
Regions of
Interest
(m:n)
ROI Dose
Summary
ROI DVH
Data
ROI DVH
Features
Shape
Descriptor
Data
Shape
Relationship
Features
Use of SQL
DB reduces
search to an
SQL query
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Designed for data sharing
U54 Grant (U Wash, Penn, U Mich)
Johns Hopkins
Institution 1-N (UW)
shared
Pancreas
150+ pts
Head & Neck
Panc SBRT
Head & Neck
700+ pts
Panc SBRT
Thoracic
Prostate
Informaticist and the Clinician
• Where clinical knowledge and
informatics science meet?
• What is real knowledge?
• What is NEW knowledge?
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The Vs of Big Data
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Viability and Value
• Predictive factors must be accessible for new
patients
• Prediction must be clinically valuable and extend
the knowledge of the clinician
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Decision support for…
• SAFETY can be improved by alerting users when patient
treatment information deviates from normal.
• QUALITY can be improved by predicting how well you can
do for a patient and seeking to achieve it.
• PERSONALIZATION occurs when physicians and patients
can review results of prior similar patients and make
decisions based on the data specific to the patients needs.
Toxicity trends during and after
treatment – detect outliers
During Treatment
Follow up
Swallowing
Worsens after Tx
for many patients
then improves long
term
Mucositis
Heals after Tx for
most patients
Dysphagia
Inflammation
Xerostomia
Tends to be
permanent
Dry Mouth
#pts
Toxicity grade (0-5)
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Shape-dose relationship for
auto-planning
Shape relationship
DB of prior patients
Dose prediction
parotids
1
Right parotid
Left parotid
0.9
0.8
normalized volume
0.7
PTV
0.6
0.5
0.4
0.3
0.2
0.1
0
0
5 10 15 20 25 30 35 40 45 50 55 60 65 70
Dose (Gy)
Decisions:
Plan quality assessment
Automated planning
Expected toxicities
Dosimetric trade-offs
• More efficient plan optimization (10 fold)
• Normal tissue doses reduced (5-10%)
• Clinically released for Pancreatic Cancer
Flavors of Data
Depends of timeline
FIXED
already
happened
VARIABLE
influence
outcome
measures
Use to stratify patients
Adjustable to
influence outcome?
POPULATION
predict
outcome
measures
Predict outcome with
FIXED and VARIABLE
based on POPULATION?
Treatment Timeline
Consult
End of
Treatment
Weekly On
Treatment
Demographics
Diagnosis
Staging
Baseline Tox
Baseline QoL
History
Acute toxicity
QoL
Patient status
Symptom mgmt
Disease response
Toxicity
QoL
Patient status
Symptom Mgmt
Simulation
Planning
Targets
OARs
OVH
Rx
Dose
DVH
Auto
Plan
Risk
Based
At what time point do we have
enough data to make decision
based on future prediction?
Image
Guidance
Motion
Disease
Response
Symptom
Mgmt
Follow Up
Late toxicity
QoL
Patient status
Disease response
Therapy
Mgmt
Input Variables => Prediction?
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DVH, Toxicities and Grade
distributions
Dysphagia
Larynx_edema
30% Volume
Voice Change
Larynx
50% Volume
Toxicity Grade
0,1,2,3,4,5
Mean and stddev
of DX% at grade
Number of
patients by
grade at D50%
Dysphagia and Xerostomia
Larynx vs Grade ≥ 2 Dysphagia
Probability of  Grade 2 Dysphagia
1
0.9
0.9
0.8
0.8
0.8
0.7
0.7
0.7
0.6
0.6
0.5
0.5
0.4
0.4
Normalized Volume
Normalized Volume
All DVH Curves for the Larynx (142 Patients)
1
0.9
0.6
0.5
0.4
0.3
Dysphagia Grade < 2 (N=87)
0.2
1
0.3
0.3
0.2
0.2
Dysphagia Grade  2 (N=55)
0.1
0.1
0
0
0
1000
2000
3000
4000
5000
6000
7000
Dose (cGy)
0.1
0
1000
2000
3000
4000
5000
6000
7000
0
Dose (cGy)
Plugging Into Oncospace
Scott Robertson PhD
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Voice Change
Bad DVH!
≠
• DVH assumes that every sub-region of an OAR has the
same radiosensitivity and functional importance to the
related toxicity
• DVH assumes that each OAR is uniquely responsible for
the overall human function related to the toxicity
Parotid Data
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Classification with correlated features: unreliability of
feature ranking and solutions
Simulation of 1, 10 and 20 variables with a correlation of
0.9 with variable 3
http://www.cedars-sinai.edu/
Genuer et al.
Correlation is not Causation
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Acknowledgments
•
–
–
JHU-RO
– Binbin Wu PhD
– Kim Evans MS
– Robert Jacques PhD
– Joseph Moore PhD
– Scott Robertson PhD
– Wuyang Yang MS, MD
– John Wong PhD
– Theodore DeWeese MD
GI Team
– Joseph Herman MD
– Amy Hacker-Prietz PA
H&N Team
– Harry Quon MD
– Giuseppe Sanguineti MD
– Heather Starmer MD
– Jeremy Richmond MD
– Anna Keiss MD
•
JHU - CS
–
–
–
–
•
Russ Taylor PhD
Misha Kazhdan PhD
Patricio Simari PhD
Jonathan Katzman
JHU - Physics
–
–
Alex Szalay PhD
Tomas’ Bodavari PhD
•
Philips PROS
•
Erasmus
–
–
Karl Bzdusek
Steven Petit PhD
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