Informatics and Information in in Radiation Oncology : OncoSpace - - PowerPoint PPT Presentation

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Informatics and Information in in Radiation Oncology : OncoSpace - - PowerPoint PPT Presentation

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Informatics and Information in in Radiation Oncology : OncoSpace John W. Wong, Ph.D. Todd McNutt, Ph.D. Department of Radiation Oncology Supported in Part by Elekta Impac Supported in Part by Elekta-Impac OCI_50 th 2008, JWW Status of

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SLIDE 1

Informatics and Information in in Radiation Oncology : OncoSpace

John W. Wong, Ph.D. Todd McNutt, Ph.D. Department of Radiation Oncology

Supported in Part by Elekta Impac

OCI_50th 2008, JWW

Supported in Part by Elekta-Impac

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SLIDE 2

Status of health care information Status of health care information

“Today, most business ---- down to the smallest corner y, grocery store have better information about their sales and inventories than even affluent medical practices have about their patients ” practices have about their patients. ………….

  • -- Michael Bloomberg

Michael Bloomberg

to the Academy National Health Policy Conference, 05/07

OCI_50th 2008, JWW

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SLIDE 3

NIH Roadmap 3: Re-engineering the Clinical Research Enterprise

  • R.A. Harrington, M.D., Duke Research Institute (2005) :

g , , ( ) – “One of the greatest inefficiencies of the current model of clinical research in our country is the lack of t i i i f t t ( hi h i l d h d a sustaining infrastructure (which includes shared resources, common data standards, and effective use

  • f information technology among researchers), as

well as the lack of a convenient forum to share best practices and learn from one another’s mistakes and successes” successes .

OCI_50th 2008, JWW

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SLIDE 4

Cancer Bio informatics Grid (caBIG)

  • NCI: cancer Bioinformatics Grid (caBIG) provides infra-

Cancer Bio-informatics Grid (caBIG)

structure support – clinical trials management systems integrative cancer research – integrative cancer research – tissue banks and pathology – Image workspace Image workspace

  • Not directed to address specific research or clinical

questions q

OCI_50th 2008, JWW

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SLIDE 5

Data “Loss” at the Institutional Level Data Loss at the Institutional Level

  • Data we are capturing

– Labs, Images, Treatment Plans

  • Data we are sending away

– Patients in protocols Patients in protocols

  • Data we are storing

– Disparate databases Data (experience) we are not capturing

  • Data (experience) we are not capturing

– Discarded treatment plans (and decision making process)

  • Information and knowledge are Not captured systematically

OCI_50th 2008, JWW

g p y y

  • Not utilized efficiently to impact research and patient care
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SLIDE 6

Challenges of data longevity and re-use Challenges of data longevity and re use

  • RTOG

– Formed 1968, funded since 1971 – Activated 300 trials

  • 40 on-going
  • 60,000 patients enrolled

Q f – Established QA, credentialing process for RTP and dosimetry – Centralized date repository; lacks secondary research Centralized date repository; lacks secondary research – No measure of impact on community practice

OCI_50th 2008, JWW

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SLIDE 7

Multiple Informatics Initiatives at JHU Multiple Informatics Initiatives at JHU

  • Johns Hopkins University Health Systems

C itt f H lth I f ti – Committee for Health Informatics – Johns Hopkins Medical Image Archive (JHMIA) – I4M: Integration of Imaging, Information and Intervention in g g g, Medicine – Clinical Trial Groups Industrial collaborations – Industrial collaborations

  • Microsoft (Almaga -- Healthcare Informatics)
  • IBM (Computational Medicine)
  • Harris Corporation (Multi-disciplinary clinic)
  • …………

OCI_50th 2008, JWW

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SLIDE 8

The JHMIA Program– (Radiology) e J

  • g a

( ad o ogy)

A single archive where all medical images and other non textual data (and associated reports etc ) from non-textual data (and associated reports, etc.) from across a Healthcare Enterprise are stored

  • Clinical (300 TB now --- 700 TB in 2 years)
  • Research
  • Waveform
  • Genomic (planned)

P t i ( l d)

  • Proteomic (planned)
  • Medical Image Archive Medical Data Archive?

OCI_50th 2008, JWW

  • Medical Image Archive Medical Data Archive?
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SLIDE 9

JHMIA A E t i I A hi An Enterprise Image Archive

Current Participants Committed

  • JHH Radiology
  • JHH Vascular Surgery
  • JHH Peds Cardiology
  • BMC Vascular
  • BMC Adult Echo Cardio
  • Endoscopy (GI)
  • JHH Rad Oncology
  • JHH Adult Echo Cardio
  • Surgery

Potential

  • OB/GYN

g y

  • Cardiology
  • BMC Radiology
  • Ophthalmology
  • Pathology
  • Howard County General Hospital

p gy

OCI_50th 2008, JWW

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SLIDE 10

JHMIA and I4M JHMIA and I M

Analytic Database(s): Analytic Database(s): Query and Security Analytic and Change Tools: Extraction of Information Decision Support:

Web-service

Data-mining Statistical Modeling

OCI_50th 2008, JWW

  • Goal: To improve both medical research and patient care
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SLIDE 11

The enterprise model: JHMIA and I4M The enterprise model: JHMIA and I M

Radiation Oncology Infra-structure: JHMIA Radiology TeraMedica Robotic Surgery Ophthalmology Infra-structure: JHMIA, Radiology, TeraMedica,

Data-mining Shape and Change Tools Decision Support Analytic Database

  • Challenges to implement across multi-disciplines:

– Data Standards W kfl P d d M t Diff – Workflow, Procedure, and Management Differences

  • Different intervention time-scale

OCI_50th 2008, JWW

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SLIDE 12

OncoSpace: p Closed Loop Adaptive Radiation Therapy

Data Information Intervention Response Patient Data – Information – Intervention – Response Real Time Image Guided Intervention T t t R ti i ti E l T t t A t Treatment Re-optimization; Early Treatment Assessment,… Population : New protocol, New dose level, New standards

OncoSpace Infrastructure

OCI_50th 2008, JWW

OncoSpace Infrastructure

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SLIDE 13

OncoSpace: OncoSpace: Radiation Oncology as the I4M test-bed

Radiation Oncology Robotic Surgery Ophthalmology Infra-structure: JHMIA, Radiology, TeraMedica, Decision Support Shape and Change Tools Analytic Database

OncoSpace

Decision Support Data-mining

OCI_50th 2008, JWW

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SLIDE 14

Extending the OncoSpace Model: g p Sharing Research and Clinical Care

I4M Infr Institute n ra-structu Institute 1 JHU

Genomics

I4M Infra-structure

JHU

ure

Ophthalmology

Radiation Oncology

OCI_50th 2008, JWW

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SLIDE 15

OncoSpace

  • OncoSpace is a new research infra-structure based on

p Radiation Oncology as a “use-case” model

  • Bioengineering Research Partnership

lti di i li R di ti O l R di l – multi-disciplinary: Radiation Oncology, Radiology, Physics and Astronomy, Computer Science and Biostatistics – multi-institutional: Hopkins, clinical partner sites – IMPAC

OCI_50th 2008, JWW

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SLIDE 16

Distributed Research Model Distributed Research Model

Current Trial Practice Hypothetical Future Practice

Patient Tx Follow up Treatment T t t Patient Tx Follow up Treatment Protocol Treatment Protocol

Journal Publications

Journal Publication

Publication of Data to DB’s

Increased potential for data reuse

STOP START

OCI_50th 2008, JWW

START OVER

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SLIDE 17

Data Delivery in Cooperative Research: Data Delivery in Cooperative Research: Hitting the Wall

FTP and GREP are not adequate

  • You can GREP 1 MB in a second
  • You can GREP 1 GB in a minute

You can GREP 1 TB in 2 days

  • You can FTP 1 MB in 1 sec
  • You can FTP 1 GB / min (~1

$/GB)

q

  • You can GREP 1 TB in 2 days
  • You can GREP 1 PB in 3 years

50 MB l l DICOM t f t k 1 i $/GB)

  • 2 days;1K$ / 3 years and 1M$
  • 50 MB local DICOM transfer takes 1 min
  • 100 patients x 10 (3D) scans = 5 - 10 TB
  • A factor of 10 improvement in access speed

cannot offset the growth in data and cannot offset the growth in data and complexity

R thi k d t b ’ f ti

OCI_50th 2008, JWW

  • Rethink databases’ function

– following the CS community

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SLIDE 18

OncoSpace: Adapting the SkyServer Approach p p g y pp

  • SDSS is a collaborative

effort to map 25% of the

  • Shared resources

– Methodology

sky

  • SkyServer publishes data

from the SDSS

– Software – Expertise – Experience

from the SDSS

  • >> 100’s of new

discoveries in astrophysics

p

  • New opportunities

– Analysis Visualization

  • Increased scale and scope

for research

– Visualization – User experience

  • Skyserver.sdss.org

OCI_50th 2008, JWW

Alex Szalay PhD - JHU Jim Gray PhD - Microsoft

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SLIDE 19

OncoSpace: Adapting the SkyServer Approach O coSpace dapt g t e S ySe e pp oac

  • Active Databases
  • There is too much data to move around,

take the analysis to the data!

  • Do all data manipulations at database

B ild t d d f ti i th – Build custom procedures and functions in the database

  • Established Web-service for broad access

Established Web service for broad access

– Query across multiple databases

  • Automatic parallelism guaranteed

OCI_50th 2008, JWW

p g

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SLIDE 20

Database Consideration Database Consideration Operational vs Analytical

  • Workflow management
  • Patient records and archival
  • Decision Support
  • On-line Analytical Processing
  • Multidimensional analysis
  • Time variant

iltering

  • Non-volatile

How do I organize my data? T ypically Hierarchical How might I analyze my data? Star Schema D i t t l i

  • n & Fi
Dimension Dimension Dimension Dimension Dimension FACTS

DICOM RT OO principles Design to support analysis… Fast query

Extracti Data E OIS, OCIS, EPR TPS, PACS OncoSpace

OCI_50th 2008, JWW

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SLIDE 21

OncoSpace: Work in progress (McNutt)

Membership Vocabulary OncoSpace D t Data Preparation Analytical Database

  • Technologies

– SQL Server 2005 Ruby on Rails

OCI_50th 2008, JWW

– Ruby on Rails

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SLIDE 22

lab_values

id test_name test_value test_unit test_date patient id

lesions

id lesion_site lesion_date lesion_size lesion_size_unit lesion_type current_t_stage

pathology_features

id
  • rgan_site
anatomic_site name value unit

patient_representations

id dicom_uid representation_name image_set_modality

primary_tumors

id tumor_site t_stage n_stage m_stage
  • verall_stage
tumor_histology

transformations

id is_rigid x_translate y_translate z_translate x_rotate y rotate

Transformation

p _ created_on updated_on user_id version current_n_stage current_m_stage current_overall_stage current_grade created_on updated_on primary_tumor_id patient_id

medical_histories

id
  • rgan_system
disease initial_diagnosis patient_id feature_grade collection_date patient_id created_on updated_on user_id version representation_date x_start y_start z_start x_pixel_dimension y_pixel_dimension z_pixel_dimension created_on tumor_grade presentation_date created_on updated_on patient_id user_id version y_rotate z_rotate deformable_transformation... deformable_transformation from_patient_rep_id to_patient_rep_id created_on updated_on user id

Dose_

dose_grids

id dose_per_fraction x_start y_start z_start

family_histories

id disease_type disease name user_id version created_on updated_on user_id version updated_on patient_id user_id version

patients

id last_name first_name

radiation_summaries

id target

radiotherapy_sessions

id number_of_fractions user_id version

Patient Radiation grids

x_pixel_dimension y_pixel_dimension z_pixel_dimension dose_grid_blob patient_representation_id created_on updated_on user_id disease_name relation age_at_diagnosis collection_date patient_id created_on updated_on user_id version medical_record_number birth_date gender race first_contact postal_code clinical_site protocol target radiation_technique radiation_protocol dose_per_fraction nominal_total_dose simulation_date rt_completion_date treatment_interruption_rea... treatment termination reason prescribed_dose_per_fraction start_date completion_date technique modality beam_energy dicomrt_plan_uid radiation_summary_id

Summaries

version version created_on updated_on user_id version treatment_termination_reason primary_tumor_id lesion_id created_on updated_on patient_id user_id version patient_representation_id dose_grid_id created_on updated_on user_id version

social_histories

id social_history_type social_history_name social_history_value social_history_unit collection_date

clinical_events

id event_type event_name event_date patient_id created_on updated on
  • utcomes
id days_since_enrollment
  • utcome_type
  • utcome_name
  • utcome_grading_scale
  • utcome_grade
patient id

prescribed_drugs

id drug_name drug_type dose dose_unit frequency delivery route

roi_dose_per_sessions

id roi_name volume mean_dose max_dose min_dose stddev_dose

roi_dose_summaries

id roi_name number_of_fractions volume mean_dose max_dose min dose

roi_geometries

id roi_name volume x_center_of_mass y_center_of_mass z_center_of_mass surface_mesh patient_id created_on updated_on user_id version

surgical_procedures

id procedure_name attending_physician procedure_date patient_id created_on updated on

OCI_50th 2008, JWW

updated_on user_id version patient_id created_on updated_on user_id version delivery_route start_date stop_date patient_id created_on updated_on user_id version dose_volume_histogram roi_geometry_id radiotherapy_session_id created_on updated_on user_id version min_dose stddev_dose dose_volume_histogram created_on updated_on patient_id user_id version binary_mask patient_representation_id created_on updated_on user_id version updated_on user_id version
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SLIDE 23

Hopkins OncoSpace

Clinicians Researchers Bio- Statisticians View/ Analyze Data View/ Analyze Data View/ Analyze Data MS Web Services Tools Security Project 3 Active Data base Services y Project 2 IMPAC/ Project 1

OCI_50th 2008, JWW

PACs MIS IMPAC/ RTP Labs

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SLIDE 24

Other Users Other Users

Hopkins Institution 2 Institution 2 Institution n

caBIG caBIG Globus Globus Globus Web Services Web Services Globus Web Services Globus Web Services

Active Data Base Active Data Base Active Data Base

OCI_50th 2008, JWW

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SLIDE 25

OncoSpace: 4 Projects

1 Integration of clinical workflow with data collection to

OncoSpace: 4 Projects

  • 1. Integration of clinical workflow with data collection to

populate OncoSpace.

  • 2. Optimize database architecture for secured distributed

web-access

  • 3. Tools for query, analysis, and navigation of OncoSpace

to derive information from various classes of questions to derive information from various classes of questions

  • 4. Bio-statistic research and development to support data

mining and ensure valid decision making from the OncoSpace Systems and Nested Experiments.

OCI_50th 2008, JWW

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SLIDE 26

Influence of Shape

OCI_50th 2008, JWW

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SLIDE 27

Overlap Volume Histogram (OVH)

1mm

V l Cumulative Overlap Volume Histogram (COVH)

OVH maps the shape of OAR to a volume-distance plane through target expanding and shrinking. 1mm

1mm

Volume

1mm

Distance

1mm 2mm

  • 1mm
  • 2mm

Expansion Shrinkage

OCI_50th 2008, JWW

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SLIDE 28

Sphere: radius 7cm Rectangle: 3.7*3.7*12.1cm Spatial resolution: 0 1*0 1*0 1cm Target OAR2 Spatial resolution: 0.1 0.1 0.1cm Image size: 291*291*291pix OAR1

0.8 0.9 1

0.3 0.35

0.5 0.6 0.7 OAR1 OAR2

0.2 0.25 OAR1 OAR2

0.2 0.3 0.4

0.1 0.15

OCI_50th 2008, JWW

OncoSpace 2008, JWW Distance in cm unit Distance in cm unit

  • 6
  • 4
  • 2

2 4 6 8 0.1

  • 6
  • 4
  • 2

2 4 6 8 0.05

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SLIDE 29

Statistic Mapping T

0.8 1 0.8 1

DVH of parotid

L

OVH

M

OVH

H

OVH

0 2 0.4 0.5 0.6 0 2 0.4 0.5 0.6 [ , , ]

L M H

DVH T OVH OVH OVH =

  • 2

2 4 0.2

Distance (cm)

2 4 6 8

Distance (cm)

2 4 6 8

Distance (cm)

20 40 60 80 0.2

Dose (Gy)

[ , , ]

L M H

Parotid: V(30Gy)<50% of volume

⇓

Dose corresponding 50% of volume:

32 5 [0 4685 4 411 5 189 ] Gy T cm cm cm =

⇓

Distance corresponding 50% of volume:

OCI_50th 2008, JWW

OncoSpace 2008, JWW

32.5 [0.4685 , 4.411 ,5.189 ] Gy T cm cm cm =

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SLIDE 30

Treatment plan evaluation (parotids)

Re-plan 22: R parotid (31Gy)

8 10 4 6 8

  • f PTVH

2 d3:OVH 2 3 4 4 6 8

  • 2
  • 2
  • 1

1 2

  • 2

2 d1:OVH of PTVL d2:OVH of PTVM

OCI_50th 2008, JWW

OncoSpace 2008, JWW : 0—25Gy : 25—30Gy : 30—35Gy : 35—40Gy : >60Gy Dose corresponding 50% of volume:

  • ×

∗

+

:50—55Gy :55—60Gy :40—45Gy :45—50Gy

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SLIDE 31

Re-plan 22: right parotid

0 9 1 0 9 1 0.7 0.8 0.9 0.7 0.8 0.9

Left parotid

0.4 0.5 0.6 0.4 0.5 0.6 0.1 0.2 0.3 0.1 0.2 0.3

58.1 63 70

, , PTV PTV PTV

Dose in Gy Dose in Gy

Brainstem Cord

10 20 30 40 50 60 70 80 10 20 30 40 50 60 70 80

Right parotid: 50% volume 31Gy24.4Gy

:Re-plan :Original plan

OCI_50th 2008, JWW

OncoSpace 2008, JWW

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SLIDE 32

OncoSpace: p Physician’s Tool for Personalized Medicine

  • Enable natural recall of past experience’s with patients

p p p – display of data that match physician’s way of thinking

  • Allow other physicians to share the experience
  • Allow other centers to contribute and use OncoSpace to

broaden the stored experience caBIG compliance to insure data reuse and sharing

  • caBIG compliance to insure data reuse and sharing

OCI_50th 2008, JWW

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SLIDE 33

OncoSpace: OncoSpace: as a Collaborative Research Model

All queries should be IRB approved

  • All queries should be IRB approved
  • RTOG as a service to legitimize query

– Data is live for re-use Data is live for re use – Demonstrate Patient Care Improvement

OCI_50th 2008, JWW

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SLIDE 34

Challenges Challenges

  • OncoSpace Design

p g

  • Statistical Research
  • Quality Assurance
  • HIPPA and Security

OCI_50th 2008, JWW

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SLIDE 35

OncoSpace: Ph i i ’ T l f P li d M di i as Physician’s Tool for Personalized Medicine

  • Enable natural recall of past experience’s with patients

p p p – display of data that match physician’s way of thinking

  • Allow other physicians to share the experience
  • Allow other centers to contribute and use OncoSpace to

broaden the stored experience caBIG compliance to insure data reuse and sharing

  • caBIG compliance to insure data reuse and sharing

OCI_50th 2008, JWW

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SLIDE 36

OncoSpace: as a Collaborati e Research Model as a Collaborative Research Model

  • All queries should be IRB approved

q pp

  • RTOG as a service to legitimize query

– Data is live for re-use – Demonstrate Patient Care Improvement

OCI_50th 2008, JWW

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SLIDE 37

Challenges Challenges

  • OncoSpace Design

p g

  • Statistical Research
  • Quality Assurance
  • HIPPA and Security

OCI_50th 2008, JWW

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SLIDE 38

Future Role of Physics in Radiation Therapy utu e

  • e o

ys cs ad at o e apy

  • Technological focus

– Dose escalation; proton? – How high can we deliver? H hi h d d? – How high do we need?

  • Bridging discoveries to RT

– Room to de-escalate – Room to de-escalate

  • Information and Informatics

– Improve effectiveness and p efficiency of research – Disseminate knowledge for care

OCI_50th 2008, JWW

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SLIDE 39

Future Role of Physics in Radiation Therapy utu e

  • e o

ys cs ad at o e apy

  • Technological focus

– Dose escalation; proton? – How high can we deliver? H hi h d d? – How high do we need?

  • Bridging discoveries to RT

– Room to de-escalate – Room to de-escalate

  • Information and Informatics

– Improve effectiveness and p efficiency of research – Disseminate knowledge for care

OCI_50th 2008, JWW