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7/22/2014
1
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
Patient
History
Performance
Status
Clinical
assessments
Quality
of Life Survival
Lab Values
Comorbidities
Diagnosis
Disease
Status
Chemotherapy Surgery
Pathology
Toxicities
Radiotherapy
FO
LL
OW
-UP
TREATMENT
CO
NS
UL
T
How to best to
treat individual
patient?
Prediction of
complications for
early
intervention?
Predicted
response and
toxicity?
• Integration of data collection with clinical workflow
– “Big Data” requires meaningful data
• Database design, security and distributed web-access
• 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
Project components
7/22/2014
2
Treatment Timeline
Simulation Targets
OARs
OVH
Consult Demographics
Diagnosis
Staging
Baseline Tox Baseline QoL
History
Planning Rx
Dose
DVH
Weekly On
Treatment Toxicity
QoL
Patient status
Symptom Mgmt
End of
Treatment Acute toxicity
QoL
Patient status
Symptom mgmt
Disease response
Follow Up Late toxicity
QoL
Patient status
Disease response
Image
Guidance Motion
Disease
Response
Chart
Review
Data Collection in Clinic
7/22/2014 5
Clinical Assessment
Quality of life Disease Status
Chart Review (~40 per hour)
7/22/2014 6
Highlight when
data missing
Identify abnormal
prescriptions
7/22/2014
3
Extract, Transform, Load
Oncospace MOSAIQ
Pinnacle TPS
&
DICOM RT PACS
- Scripts, Python, DICOM
- DVH, OVH, Shapes
- SQL Query
- Lab, Toxicity,
Assessments
Oncospace tables and schema
(m:n)
(m:n)
Patient
Family
History
Social
History
Medical
History
Private Health Info
(access restricted)
Tumors
Test Results
(Labs)
Assessments
(Toxicities)
Clinical
Events
Surgical
Procedures
Medications
(chemo)
Organ Dose
Summaries
Radiation
Summary
Patient Representations
(CT based geometries)
Pathology
Feature
Image
Feature
Organ
DVH Data
Organ DVH
Feature
Image
Transform
Radiotherapy
Sessions
Regions of
Interest
ROI Dose
Summary
Shape
Descriptor
Shape
Relationship
Data Features ROI DVH
Features
ROI DVH
Data
1 : N multiple instances 1 : 1 single instance m : n relates m to n
Use of SQL
DB reduces
search to an
SQL query
7/22/2014
4
Designed for data sharing U54 Grant (U Wash, Penn, U Mich)
Head & Neck
700+ pts
Johns Hopkins Institution 1-N (UW)
Panc SBRT
Thoracic
Head & Neck
Panc SBRT
Prostate
shared
Pancreas
150+ pts
Informaticist and the Clinician
• Where clinical knowledge and
informatics science meet?
• What is real knowledge?
• What is NEW knowledge?
7/22/2014 11
The Vs of Big Data
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7/22/2014
5
Viability and Value
• Predictive factors must be accessible for new
patients
• Prediction must be clinically valuable and extend
the knowledge of the clinician
7/22/2014 13
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
Dysphagia Swallowing
Mucositis Inflammation
Xerostomia Dry Mouth
Worsens after Tx
for many patients
then improves long
term
Heals after Tx for
most patients
Tends to be
permanent
Follow up During Treatment
#pts
Toxicity grade (0-5)
7/22/2014
6
Shape-dose relationship for
auto-planning
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
Shape relationship Dose prediction DB of prior patients
parotids
PTV 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Dose (Gy)
no
rmaliz
ed v
olu
me
Right parotid
Left parotid
Flavors of Data Depends of timeline
FIXED
already
happened
VARIABLE
influence
outcome
measures
POPULATION
predict
outcome
measures
Use to stratify patients Adjustable to
influence outcome?
Predict outcome with
FIXED and VARIABLE
based on POPULATION?
Treatment Timeline
Simulation Targets
OARs
OVH
Consult Demographics
Diagnosis
Staging
Baseline Tox Baseline QoL
History
Planning Rx
Dose
DVH
Weekly On
Treatment Toxicity
QoL
Patient status
Symptom Mgmt
End of
Treatment Acute toxicity
QoL
Patient status
Symptom mgmt
Disease response
Follow Up Late toxicity
QoL
Patient status
Disease response
Image
Guidance Motion
Disease
Response
Auto
Plan
Risk
Based
Symptom
Mgmt
Therapy
Mgmt
At what time point do we have
enough data to make decision
based on future prediction?
Input Variables => Prediction?
7/22/2014
7
DVH, Toxicities and Grade
distributions
Voice Change
Larynx
50% Volume
Dysphagia
Larynx_edema
30% Volume
Number of
patients by
grade at D50%
Toxicity Grade
0,1,2,3,4,5
Mean and stddev
of DX% at grade
Dysphagia and Xerostomia
Probability of Grade 2 Dysphagia
Dose (cGy)
No
rma
lize
d V
olu
me
0 1000 2000 3000 4000 5000 6000 70000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 1000 2000 3000 4000 5000 6000 70000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
All DVH Curves for the Larynx (142 Patients)
Dose (cGy)
No
rma
lize
d V
olu
me
Dysphagia Grade < 2 (N=87)
Dysphagia Grade 2 (N=55)
Larynx vs Grade ≥ 2 Dysphagia
Plugging Into Oncospace Scott Robertson PhD
21
7/22/2014
8
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
7/22/2014
9
Classification with correlated features: unreliability of
feature ranking and solutions
http://www.cedars-sinai.edu/
Simulation of 1, 10 and 20 variables with a correlation of
0.9 with variable 3
Genuer et al.
Correlation is not Causation
7/22/2014 26
Acknowledgments
• JHU - CS
– Russ Taylor PhD
– Misha Kazhdan PhD
– Patricio Simari PhD
– Jonathan Katzman
• JHU - Physics
– Alex Szalay PhD
– Tomas’ Bodavari PhD
• Philips PROS
– Karl Bzdusek
• Erasmus
– Steven Petit PhD
• 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